CN116205108A - Bridge crane stress course acquisition method and system - Google Patents
Bridge crane stress course acquisition method and system Download PDFInfo
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Abstract
The invention discloses a method and a system for acquiring stress course of a bridge crane, wherein the method comprises the steps of acquiring characteristic data of preset time in an actual working state of the crane; expanding the lifting load data of preset time through Latin hypercube sampling technology to obtain lifting load data of a crane fixed inspection period, inputting the lifting load data of the fixed inspection period into a pre-trained longhorn beetle whisker-least square support vector machine prediction model, and obtaining predicted working cycle times; converting the lifting load data of the fixed inspection period and the predicted working cycle times into trolley wheel pressures with equal sample sizes; and (3) establishing a finite element model of the crane girder, simulating the random load of the trolley to run from one side span end to the other side span end of the girder at a rated speed by combining transient analysis, and acquiring the stress course of the fixed inspection period of any position of the crane girder after circularly traversing all trolley wheel pressure loads. The invention provides high-precision stress characteristic data for fatigue life assessment of the crane structure.
Description
Technical Field
The invention relates to the technical field of crane stress acquisition, in particular to a bridge crane stress course acquisition method and system.
Background
The bridge crane is used as special equipment and is widely applied to various production workshops and related industrial scenes, and because the working load of the bridge crane has randomness, intermittence and contingency, the fatigue performance of the main beam can be gradually degraded under the condition of long-term service, and sudden accidents of structural fracture occur. To prevent such accidents, accurate acquisition of stress history at key locations of the structure is key to performing fatigue life assessment and early prevention.
When the existing method calculates the stress course of the crane structure, the theoretical calculation or simulation analysis is carried out on the stress change of a specific dangerous section and a dangerous point under a certain or limited working cycle, so that the working characteristic that the crane trolley bears random load for a long time is ignored, and the influence of the increase of the stress cycle number under a single working cycle caused by irregular stress change in the process of carrying the trolley on the main beam structure is ignored. Therefore, it is not reasonable to analyze the stress variation of the crane structure only to bear a specific load for a short time, which will lead to a great error in the later fatigue life evaluation.
Disclosure of Invention
The invention aims to provide a method and a system for acquiring stress history of a bridge crane, which are used for solving the problem of poor accuracy of stress data used in fatigue life assessment of the crane in the prior art.
In order to achieve the above purpose, the invention is realized by adopting the following technical scheme:
the invention discloses a bridge crane stress course acquisition method, which comprises the following steps:
acquiring characteristic data of preset time in an actual working state of the crane, wherein the characteristic data comprises lifting load data and working cycle number data, and the preset time is smaller than a fixed detection period;
expanding the lifting load data of preset time through Latin hypercube sampling technology to obtain lifting load data of a crane fixed inspection period, inputting the lifting load data of the fixed inspection period into a pre-trained longhorn beetle whisker-least square support vector machine prediction model, and obtaining predicted working cycle times;
converting the lifting load data of the fixed inspection period and the predicted working cycle times into trolley wheel pressures with equal sample sizes;
establishing a finite element model of a crane girder, writing a macro command, simulating random load of a trolley to run from one side span end to the other side span end of the girder at a rated speed by combining transient analysis, and acquiring stress histories of a fixed inspection period of any position of the crane girder after circulating through all trolley wheel pressure loads.
Further, obtaining the characteristic data of the preset time in the actual working state of the crane includes:
collecting lifting load data of a crane for 30 days through a load sensor arranged at a supporting position of a lifting reel of a crane trolley;
the number of work cycles of the crane for 30 days is obtained, wherein the reading of the load sensor is changed to 0 from 0 to 0 again for one work cycle.
Further, the method for training the longhorn beetle whisker-least square support vector machine prediction model comprises the following steps:
optimizing super-parameter selection in a least square support vector machine model through a longhorn beetle whisker search optimization algorithm, and building an original longhorn beetle whisker-least square support vector machine prediction model;
dividing the characteristic data of the preset time into training set data and test set data;
inputting training set data into an original longhorn beetle whisker-least square support vector machine prediction model to obtain an intermediate longhorn beetle whisker-least square support vector machine prediction model;
and optimizing and correcting the intermediate longhorn beetle whisker-least square support vector machine prediction model through the test set to obtain a final longhorn beetle whisker-least square support vector machine prediction model.
Further, the method for acquiring the trolley wheel pressure with the equal sample size comprises the following steps:
randomizing the lifting load data of the fixed inspection period and the predicted working cycle times by adopting matlab;
and acquiring the trolley wheel pressure of the sample quantity by the randomized lifting load data and the trolley dead weight.
Further, the step of obtaining the stress history of the fixed inspection period of any position of the main girder of the crane comprises the following steps:
establishing a finite element model of a main girder of the crane, and writing macro commands for reading wheel pressure, running speed, cyclic loading and damping setting;
based on a crane girder finite element model, selecting a FULL complete method for solving, defining a Rayleigh damping parameter and a step loading mode, and realizing transient analysis setting;
inputting the wheel pressure data of the trolley in a fixed detection period into a finite element model of the main girder of the crane by utilizing a macro command for reading the wheel pressure, selecting the initial position of the trolley, applying the wheel pressure load for the first time, and controlling the trolley to run to the cross end of the other side of the main girder at a rated speed by utilizing a running speed macro command to complete one working cycle;
circulating through all wheel pressure loads by utilizing a circulating loading macro command to finish finite element simulation of a random belt running process of the crane trolley in a fixed inspection period;
and according to the fatigue position coordinates required to be analyzed, extracting stress history data corresponding to the node fixed inspection period by combining the node selection command.
Further, the read wheel pressure macro command is written as follows: and defining the size of the wheel pressure matrix, and automatically writing specific wheel pressure values in combination with a circulation command.
Further, writing an operation speed macro command is as follows: and calculating the number of the grids advancing every second according to the running speed of the trolley and the grid size, and controlling the node position of the loading wheel pressure every second by using a circulation command.
Further, writing a cyclic loading macro command is as follows: and controlling the trolley to run from one side span end of the main beam to the other side span section by taking a second as a unit by utilizing a time step and a node selection command, and applying all wheel pressure data of a fixed inspection period in combination with cycle traversal.
Further, the macro command for damping setting is: and calculating and setting two parameter values of Rayleigh damping in transient analysis according to the maximum and minimum frequency values of the modal analysis result.
The invention discloses a bridge crane stress course acquisition method, which comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method of any of the first aspects.
According to the technical scheme, the invention has the beneficial effects that:
according to the invention, based on the characteristic data of a small sample in an actual working state, the method is expanded through Latin hypercube sampling technology, a trained longhorn whisker-least square support vector machine prediction model is combined, crane constant inspection period service information conforming to the actual working condition is obtained through the modes of acquisition, expansion and prediction, a finite element model is established, and a relevant macro command is combined to simulate the stress change condition of a crane trolley at any position in the process of running from one side cross end to the other side cross end of a main beam in a constant inspection period for a large range of time duration at a rated speed in a random carrying manner, so that high-precision stress characteristic data is provided for evaluating the fatigue life of the crane structure, and meanwhile, high-precision and low-cost technical reference is provided for acquiring high Zhou Yingli process of the same type of mechanical equipment.
Drawings
FIG. 1 is an overall flow diagram of a stress acquisition method of the present invention;
FIG. 2 is a flowchart of the longhorn beetle whisker-least squares support vector machine predictive model training in the present invention;
FIG. 3 is a schematic diagram of an expansion prediction method for service information of a bridge crane in a fixed inspection period;
FIG. 4 is a chart of the data of the fixed inspection period lifting load and the working cycle times of the bridge crane;
FIG. 5 is a graph showing a comparison of service information randomization of the bridge crane according to the present invention;
FIG. 6 is a schematic diagram of a simulated trolley of the present invention traveling with a single duty cycle;
FIG. 7 is a flow chart of the wheel pressure load cycle traversal of the present invention;
FIG. 8 is a schematic view of the cross-sectional hazard points of the main girder structure of the crane according to the present invention;
FIG. 9 is a high-precision stress history of the risk points of the main girder structure of the crane according to the present invention.
Detailed Description
The invention is further described in connection with the following detailed description, in order to make the technical means, the creation characteristics, the achievement of the purpose and the effect of the invention easy to understand.
As shown in fig. 1 to 9, the invention discloses a bridge crane stress history acquisition method, which comprises the following steps: acquiring characteristic data of preset time in an actual working state of the crane, wherein the characteristic data comprises lifting load data and working cycle number data, and the preset time is smaller than a fixed detection period; expanding the lifting load data of preset time through Latin hypercube sampling technology to obtain lifting load data of a crane fixed inspection period, inputting the lifting load data of the fixed inspection period into a pre-trained longhorn beetle whisker-least square support vector machine prediction model, and obtaining predicted working cycle times; converting the lifting load data of the fixed inspection period and the predicted working cycle times into trolley wheel pressures with equal sample sizes; establishing a finite element model of a crane girder, writing a macro command, simulating random load of a trolley to run from one side span end to the other side span end of the girder at a rated speed by combining transient analysis, and acquiring stress histories of a fixed inspection period of any position of the crane girder after circulating through all trolley wheel pressure loads.
According to the invention, based on the characteristic data of a small sample in an actual working state, the method is expanded through Latin hypercube sampling technology, a trained longhorn whisker-least square support vector machine prediction model is combined, crane constant inspection period service information conforming to the actual working condition is obtained through the modes of acquisition, expansion and prediction, a finite element model is established, and a relevant macro command is combined to simulate the stress change condition of a crane trolley at any position in the process of running from one side cross end to the other side cross end of a main beam in a constant inspection period for a large range of time duration at a rated speed in a random carrying manner, so that high-precision stress characteristic data is provided for evaluating the fatigue life of the crane structure, and meanwhile, high-precision and low-cost technical reference is provided for acquiring high Zhou Yingli process of the same type of mechanical equipment.
Specifically, a load spectrum acquisition system is utilized to acquire lifting load through a load sensor arranged at a supporting position of a lifting reel of a crane trolley, the reading of the load sensor is changed from 0 to 0 again and is recorded as one working cycle, the lifting load and the working cycle times of the crane in an actual working state for 30 days are acquired, and the acquisition of small samples of service information of the crane is completed.
based on a 30-day lifting load sample, a Latin hypercube sampling technology is combined, lifting load data of a crane in a fixed inspection period of 2 years is obtained through expansion, the lifting load data is input into a trained BAS-LSSVM prediction model, corresponding working cycle times are output, and crane load spectrum data, namely service information, of 2 years is formed.
The model training process of the BAS-LSSVM comprises the following steps:
(a) The prediction performance of the LSSVM model is determined by the selection of two super parameters in the model, and the selection of the super parameters delta and gamma in the LSSVM model is optimized by using a longhorn whisker search (BAS) optimization algorithm so as to improve the accuracy of LSSVM prediction and complete the construction of the BAS-LSSVM prediction model.
(b) Dividing a small sample of service information of a crane for 30 days into a training set and a testing set, utilizing a BAS-LSSVM prediction model, learning an implicit mapping relation between lifting load and working cycle times in the training set to obtain a trained BAS-LSSVM prediction model, and utilizing the sample in the testing set as model performance verification to ensure that the trained BAS-LSSVM prediction model can predict crane high-precision working characteristic parameters with higher fitting degree with actual use working conditions.
And step 3, converting the lifting load data of the fixed inspection period and the predicted working cycle times into trolley wheel pressures with equal sample sizes.
And randomizing the service information working sequence by using matlab, adding the dead weight and lifting load of the trolley to calculate the trolley wheel pressure, and forming the trolley wheel pressure with equal sample size.
And 4, establishing a finite element model of the crane girder, writing a macro command, simulating random loading of the trolley by combining transient analysis to run from one side span end to the other side span end of the girder at a rated speed, and obtaining the stress course of the crane girder in any position fixed inspection period after circulating through all trolley wheel pressure loads.
And 41, establishing a finite element model of the crane girder by using APDL, and preparing for the operation of the transient analysis module by writing a macro command, wherein the method comprises four parts of wheel pressure reading, running speed reading, cyclic loading and damping setting.
a. Reading wheel pressure: the wheel pressure matrix size is defined by using a dim command, and specific wheel pressure values are automatically written in combination with a do cycle command.
b. Running speed, namely calculating the number of the grids advancing per second according to the running speed of the trolley and the size of the grids, and controlling the node position of the loading wheel pressure per second by using a do circulation command.
c. And (3) circularly loading, namely controlling the trolley to run from one side span end to the other side span section of the main beam in a second unit by utilizing time step time and node selection nsel command, and applying all wheel pressure data for 2 years by combining with do cycle traversal.
d. And (3) damping setting, namely calculating and setting two parameter values alpha and betad of Rayleigh damping in a transient module according to the maximum frequency value and the minimum frequency value of the modal analysis result.
And 42, finishing material definition, grid division and boundary constraint based on a finite element model of the crane girder, entering transient analysis setting, selecting FULL complete method solution, defining Rayleigh damping parameters and a step loading mode, and finishing pretreatment.
And 43, inputting 2-year trolley wheel pressure data into an APDL program by using a macro command for reading the wheel pressure, selecting a node at one side of the main beam span end, namely the initial position of the trolley, applying a first wheel pressure load, and then controlling the trolley to operate to the other side span end of the main beam at a rated speed by using an operation speed macro command to complete one working cycle.
Step 44, using the cyclic loading macro command, cyclic traversing all wheel loads, and completing the finite element simulation of the crane trolley random belt running process for 2 years as described in step 6.
And 45, according to fatigue position coordinates which are analyzed as required, selecting nsel commands in combination with the nodes, and extracting stress history data of the corresponding nodes for 2 years in POST26 POST processing.
The invention is described in further detail below with reference to the accompanying drawings.
1) Taking a certain 32-ton bridge crane as a target, and acquiring service information under the condition of 30 days of actual working by using a load spectrum acquisition system, wherein the service information is shown in a table 1;
TABLE 1 working service information of 30 days of crane
2) As shown in fig. 2, the working service information data of table 1 is divided into a training set and a testing set, the testing set is input into an LSSVM prediction model, the selection of super parameters in the LSSVM is optimized by combining with a longicorn search optimization algorithm (BAS), model training is carried out, an implicit rule between lifting load and working cycle times in the training set is mined, and a mapping relation is formed, so that a trained BAS-LSSVM prediction model is obtained; and (3) inputting the lifting load in the test set into a trained BAS-LSSVM prediction model, comparing the difference between the model prediction result and the measured value, and verifying that the trained BAS-LSSVM prediction model has excellent performance in the aspect of crane service information prediction.
3) As shown in fig. 3, based on 30-day lifting load acquisition data, the Latin hypercube sampling technology is combined, lifting load data of 2 years of crane fixed inspection period is obtained through expansion, the lifting load data is input into a trained BAS-LSSVM prediction model, and corresponding working cycle times are output, so that high-precision working service information of the crane of 2 years shown in fig. 4 is formed.
4) Because the service information counted in table 1 is a regular load spectrum arranged according to the load sizes from small to large, but in practice, the load sizes are randomly changed, in order to make the simulation more close to the actual situation, on the premise of ensuring that the corresponding cycle times of each load are unchanged, the load sequence is randomized through programming, and the result is shown in fig. 5.
5) And calculating the trolley wheel pressure in a mode of adding the dead weight and the lifting load of the crane trolley to form the trolley wheel pressure with equal sample size.
6) Establishing a finite element model of a crane girder by using APDL, finishing material definition, grid division and boundary constraint, entering transient analysis setting, selecting FULL complete method solution, and defining Rayleigh damping parameters and a step loading mode, wherein:
the calculation formulas of Rayleigh damping parameters alpha and beta are as follows
The viscous damping coefficient xi takes 0.03, the upper limit f of frequency 1 And a lower limit f 2 The maximum and minimum values are selected according to the natural frequency of the mode.
For selecting the step loading mode, the invention assumes that the crane has completed the running process of the trolley on the main beam at the rated running speed after the cargo is lifted, so the load loading mode is selected as the step loading, and the process of gradually increasing the load in the lifting stage is ignored.
7) Taking a working cycle as an example, finite element simulation of the crane trolley carrying process is illustrated, lifting and unloading stages are omitted, the load is unchanged in the trolley carrying process at the speed of 30m/min (500 mm/s), the load acting position is continuously changed along time, random load acting is equivalently applied to 5 unit nodes of the wheel pressure acting range, step load is selected, the distance between two wheel pressure acting is equal to 2450mm of a trolley wheel distance, namely 49 unit lengths, 10 unit lengths are driven along the main beam direction per second, and one working cycle is completed until the trolley is driven to the track end position.
8) The crane trolley completes the crane working service information simulation with a fixed inspection period of 2 years according to the wheel pressure load cycle traversing flow shown in fig. 7, and the specific flow is as follows:
a: initial parameters are set, time t=0, and the number of load cycles z=1.
b: the initial acting node number J value of the trolley wheel pressure at the beginning of each cycle is set to 50.
c: let time t=t+1, at this time, time step=t, based on j=50, using NSEL node selection command, select 5 node numbers (J-49, J-48, J-47, J-46, J-45) of the first wheel-pressure effect and 5 node numbers (J, J +1, j+2, j+3, j+4) of the second wheel-pressure effect, and apply the Z-th load in the array suizaihe on each node, perform transient analysis solution, and save the calculation result in the RST result file.
d: and deleting ALL loads applied in the previous time step by utilizing an FDELE (full command), enabling J=J+10 to represent that the trolley moves forwards for 1 second at the speed of 500mm/s, advancing for 10 units, judging whether J is greater than 545, if not, indicating that the trolley does not move to the tail end of the track, jumping to the step 3 for continuous loop iteration, if so, indicating that the trolley has moved to the tail end of the track, and restarting to move from the initial position of the track again by updating the size of the random load, and entering the step 5.
e: and (3) judging whether Z=Z+1 is greater than the maximum circulation times 1800, if not, indicating that the trolley does not complete the circulation loading process of all loads, and jumping to the step (2) to continue the circulation iteration, if so, entering the step (6).
f: and (5) completing the cyclic loading process of the trolley and ending the cycle.
9) By using a POST26 time history POST processor and combining with an APDL selection command, selecting nodes on units where key positions (span center and span end) are located, extracting change data such as VonMsis stress of a dangerous point (1) at a connecting position of a span center lower flange plate and a web plate of a main beam structure and shear stress of a dangerous point (1) at a middle of a span end web plate, and if stress change of other positions is needed, extracting stress histories of the dangerous point can be completed only by selecting corresponding position nodes as shown in fig. 9.
Example 2
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be appreciated by those skilled in the art that the present invention can be carried out in other embodiments without departing from the spirit or essential characteristics thereof. Accordingly, the above disclosed embodiments are illustrative in all respects, and not exclusive. All changes that come within the scope of the invention or equivalents thereto are intended to be embraced therein.
Claims (10)
1. The method for acquiring the stress history of the bridge crane is characterized by comprising the following steps of:
acquiring characteristic data of preset time in an actual working state of the crane, wherein the characteristic data comprises lifting load data and working cycle number data, and the preset time is smaller than a fixed detection period;
expanding the lifting load data of preset time through Latin hypercube sampling technology to obtain lifting load data of a crane fixed inspection period, inputting the lifting load data of the fixed inspection period into a pre-trained longhorn beetle whisker-least square support vector machine prediction model, and obtaining predicted working cycle times;
converting the lifting load data of the fixed inspection period and the predicted working cycle times into trolley wheel pressures with equal sample sizes;
establishing a finite element model of a crane girder, writing a macro command, simulating random load of a trolley to run from one side span end to the other side span end of the girder at a rated speed by combining transient analysis, and acquiring stress histories of a fixed inspection period of any position of the crane girder after circulating through all trolley wheel pressure loads.
2. The bridge crane stress history acquisition method according to claim 1, wherein acquiring characteristic data of a preset time in an actual operating state of the crane comprises:
collecting lifting load data of a crane for 30 days through a load sensor arranged at a supporting position of a lifting reel of a crane trolley;
the number of work cycles of the crane for 30 days is obtained, wherein the reading of the load sensor is changed to 0 from 0 to 0 again for one work cycle.
3. The bridge crane stress history acquisition method according to claim 1, wherein the training method of the longhorn beetle whisker-least square support vector machine prediction model comprises:
optimizing super-parameter selection in a least square support vector machine model through a longhorn beetle whisker search optimization algorithm, and building an original longhorn beetle whisker-least square support vector machine prediction model;
dividing the characteristic data of the preset time into training set data and test set data;
inputting training set data into an original longhorn beetle whisker-least square support vector machine prediction model to obtain an intermediate longhorn beetle whisker-least square support vector machine prediction model;
and optimizing and correcting the intermediate longhorn beetle whisker-least square support vector machine prediction model through the test set to obtain a final longhorn beetle whisker-least square support vector machine prediction model.
4. The bridge crane stress history acquisition method according to claim 1, wherein the trolley wheel pressure acquisition method for the equal sample amount comprises:
randomizing the lifting load data of the fixed inspection period and the predicted working cycle times by adopting matlab;
and acquiring the trolley wheel pressure of the sample quantity by the randomized lifting load data and the trolley dead weight.
5. The bridge crane stress history acquisition method according to claim 1, wherein the step of acquiring the stress history of the crane girder at any position in the inspection cycle comprises the steps of:
establishing a finite element model of a main girder of the crane, and writing macro commands for reading wheel pressure, running speed, cyclic loading and damping setting;
based on a crane girder finite element model, selecting a FULL complete method for solving, defining a Rayleigh damping parameter and a step loading mode, and realizing transient analysis setting;
inputting the wheel pressure data of the trolley in a fixed detection period into a finite element model of the main girder of the crane by utilizing a macro command for reading the wheel pressure, selecting the initial position of the trolley, applying the wheel pressure load for the first time, and controlling the trolley to run to the cross end of the other side of the main girder at a rated speed by utilizing a running speed macro command to complete one working cycle;
circulating through all wheel pressure loads by utilizing a circulating loading macro command to finish finite element simulation of a random belt running process of the crane trolley in a fixed inspection period;
and according to the fatigue position coordinates required to be analyzed, extracting stress history data corresponding to the node fixed inspection period by combining the node selection command.
6. The bridge crane stress history acquisition method according to claim 5, wherein the writing and reading of the wheel pressure macro command is: and defining the size of the wheel pressure matrix, and automatically writing specific wheel pressure values in combination with a circulation command.
7. The bridge crane stress history acquisition method according to claim 5, wherein the writing of the operation speed macro command is: and calculating the number of the grids advancing every second according to the running speed of the trolley and the grid size, and controlling the node position of the loading wheel pressure every second by using a circulation command.
8. The bridge crane stress history acquisition method according to claim 5, wherein writing a cyclic loading macro command is: and controlling the trolley to run from one side span end of the main beam to the other side span section by taking a second as a unit by utilizing a time step and a node selection command, and applying all wheel pressure data of a fixed inspection period in combination with cycle traversal.
9. The bridge crane stress history acquisition method according to claim 5, wherein the macro command for damping setting is: and calculating and setting two parameter values of Rayleigh damping in transient analysis according to the maximum and minimum frequency values of the modal analysis result.
10. The method for acquiring the stress history of the bridge crane is characterized by comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1 to 9.
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| CN117454511A (en) * | 2023-10-26 | 2024-01-26 | 北京航空航天大学 | 5X 5 spectrum compiling method of transport aircraft based on longhorn beetle whisker searching method and interior point method |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117454511A (en) * | 2023-10-26 | 2024-01-26 | 北京航空航天大学 | 5X 5 spectrum compiling method of transport aircraft based on longhorn beetle whisker searching method and interior point method |
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