CN116739317A - Mining winch automatic management and dispatching platform, method, equipment and medium - Google Patents
Mining winch automatic management and dispatching platform, method, equipment and medium Download PDFInfo
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Abstract
The application belongs to the technical field of winches, and provides a mining winch automatic management and scheduling platform, a method, equipment and a medium, wherein the platform comprises: the system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for acquiring historical use records of each winch to be used, environmental information of the winch to be installed and historical training data, and each historical training data comprises the historical use record of each historical winch and a loss type corresponding to each historical use record; the screening module is used for constructing a loss analysis model; a calculation module for determining a quality score based on the loss type; and the scheduling module is used for scheduling each of the remaining winches to be used according to the comprehensive score. The winch with high quality score can be dispatched by the method of the application, which not only meets the field installation requirement, but also has high quality score.
Description
Technical Field
The application relates to the technical field of winches, in particular to an automatic management and scheduling platform, method, equipment and medium for a mining winch.
Background
At present, in the process of ore production and transportation, a winch is often utilized, and the winch plays a very important role in normal operation of a mine, namely, when the winch is scheduled, the winch with serious loss cannot be scheduled, and meanwhile, due to the condition of site construction, the winch in the past is scheduled to be installed; therefore, a dispatching method is needed to realize that the dispatched winch meets the field installation requirement and the quality requirement.
Disclosure of Invention
The application aims to provide a mining winch automatic management and scheduling platform, a mining winch automatic management and scheduling method, mining winch automatic management and scheduling equipment and a mining winch automatic management and scheduling medium for achieving the purposes.
In order to achieve the above object, the embodiment of the present application provides the following technical solutions:
in one aspect, the embodiment of the application provides an automatic management and scheduling platform for a mining winch, which comprises an acquisition module, a screening module, a calculation module and a scheduling module.
The system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for acquiring historical use records of each winch to be used, environmental information of the winch to be installed and historical training data, and each historical training data comprises the historical use record of each historical winch and a loss type corresponding to each historical use record;
the screening module is used for carrying out abnormal screening on the historical training data to obtain screened historical training data, and training a convolutional neural network model by utilizing the screened historical training data to obtain a loss analysis model;
the calculation module is used for inputting the historical use record of the winch to be used into the loss analysis model to obtain a loss type corresponding to the winch to be used, and determining a quality score according to the loss type;
the scheduling module is used for screening all the winches to be used according to the environmental information of the winches to be installed to obtain remaining winches to be used, calculating the matching degree score between each winch to be used in the remaining winches to be used and the environmental information, calculating the comprehensive score corresponding to each winch to be used in the remaining winches to be used according to the quality score and the matching degree score, and scheduling each winch to be used in the remaining winches to be used according to the comprehensive score.
In a second aspect, an embodiment of the present application provides a method for automatically managing and scheduling a mining winch, where the method includes:
acquiring historical use records of each winch to be used, environmental information of the winch to be installed and historical training data, wherein each historical training data comprises the historical use record of each historical winch and a loss type corresponding to each historical use record;
performing abnormal screening on the historical training data to obtain screened historical training data, and training a convolutional neural network model by using the screened historical training data to obtain a loss analysis model;
inputting the historical use record of the winch to be used into the loss analysis model to obtain a loss type corresponding to the winch to be used, and determining a quality score according to the loss type;
screening all the winches to be used according to the environmental information of the winches to be installed to obtain residual winches to be used, calculating the matching degree score between each winch to be used in the residual winches to be used and the environmental information, calculating the comprehensive score corresponding to each winch to be used in the residual winches to be used according to the quality score and the matching degree score, and scheduling each winch to be used in the residual winches to be used according to the comprehensive score.
In a third aspect, an embodiment of the present application provides an automated management and dispatch device for a mining winch, the device including a memory and a processor; the memory is used for storing a computer program; and the processor is used for realizing the automatic management and dispatching method of the mining winch when executing the computer program.
In a fourth aspect, an embodiment of the present application provides a readable storage medium having a computer program stored thereon, the computer program, when executed by a processor, implementing the steps of the above-described mining winch automated management and dispatch method.
The beneficial effects of the application are as follows:
in the application, the winch to be used is scored from two dimensions, firstly, the dimension is considered from the history use record, a large number of history use records of the history winch are obtained, the history use records are marked with loss types, the convolutional neural network model is trained after marking, noise data is screened from training data before training, the noise data is removed, the accuracy of the training data is improved, a loss analysis model is obtained after training, and when the loss type corresponding to the winch to be used is required to be calculated, the loss analysis model is only required to be input, so that the purpose of quickly and accurately calculating the loss type is realized; secondly, environmental information of the winch to be installed is considered, and the situation that the installation area is smaller than the occupied area of the winch to be used possibly exists in construction site installation is considered, so that the winch to be used is screened according to the relation between the final ground area and the occupied area, and the remaining winch to be used is obtained; and finally, calculating the comprehensive score of each winch to be used in the remaining winches to be used, and dispatching the winches to be used according to the comprehensive score.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a mining winch automated management and dispatch method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a mining winch automated management and dispatch platform according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of automatic management and dispatching equipment for mining winches according to an embodiment of the present application.
In the figure:
701. an acquisition module; 702. a screening module; 7021. a clustering unit; 7022. a first analysis unit; 70221. a second analysis unit; 70222. a third analysis unit; 703. a computing module; 704. a scheduling module; 7041. an acquisition unit; 7042. a cutting unit; 7043. a matching unit; 70431. an aggregation unit; 70432. a second calculation unit; 7044. a construction unit; 800. an apparatus; 801. a processor; an 802 memory; 803. a multimedia component; 804. an I/O interface; 805. a communication component.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that: like reference numerals or letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Example 1
As shown in fig. 1, the embodiment provides an automatic management and scheduling method for a mining winch, which comprises a step S1, a step S2, a step S3 and a step S4.
Step S1, acquiring historical use records of each winch to be used, environmental information of the winch to be installed and historical training data, wherein each historical training data comprises the historical use record of each historical winch and a loss type corresponding to each historical use record;
in the step, the winch to be used can be understood as a winch to be dispatched, and the historical use record of the winch to be used can be understood as information such as the historical use days of the winch to be used, whether faults occur, whether maintenance is performed, the maintenance times and the like; the environmental information of the winch to be installed can be understood as, for example, a picture of the construction site of the winch to be installed, which picture contains a picture of the ground on which the winch is installed; the loss type is marked by a staff with abundant experience, and can be, for example, light loss, general loss, heavy loss and the like;
s2, performing anomaly screening on the historical training data to obtain screened historical training data, and training a convolutional neural network model by using the screened historical training data to obtain a loss analysis model;
in the step, the manual mode is considered to be adopted for marking, so that some historical training data with wrong marks can exist, namely noise data can exist, and therefore, the historical training samples are screened in the step, the accuracy of the historical training data in the inflow model training can be improved through the mode, and the accuracy of model identification is further improved; the specific implementation steps of the step comprise a step S21 and a step S22;
s21, inputting the historical training data into a recurrent neural network model, recording data output by a hidden layer of the recurrent neural network model as first data, and carrying out clustering processing on all the historical training data according to the first data to obtain a plurality of clustering results;
in this step, a convolutional neural network model may be used instead of the recurrent neural network model; the clustering can adopt a K-Means clustering algorithm, a DBSCAN algorithm and the like;
step S22, analyzing the number of the historical training data contained in each loss type in each clustering result, marking the loss type with the largest corresponding number as a first type, marking the number of the historical training data contained in the first type as second data, marking the number of the historical training data contained in each clustering result as third data, dividing the second data by the third data to obtain fourth data, completing screening of the historical training data according to the fourth data, and obtaining the screened historical training data.
In this step, each clustering result corresponds to a fourth data, where the fourth data may be understood as the purity corresponding to each clustering result, and the specific implementation steps of this step include step S221 and step S222;
step S221, analyzing the fourth data, wherein if the fourth data is larger than a preset first threshold value, the number of loss types contained in the clustering result corresponding to the fourth data is analyzed, and if only one type of loss types is contained, each historical training data in the clustering result corresponding to the fourth data is recorded as a first sample; if two or more than two types of the data are included, the number of the historical training data included in each loss type in the clustering result corresponding to the fourth data is calculated to obtain fifth data, and each historical training data included in the loss type corresponding to the largest fifth data is recorded as a first sample;
step S222, analyzing the fourth data, wherein if the fourth data is smaller than or equal to the preset first threshold, a first sample is obtained again according to the number of the historical training data contained in each clustering result, and all the first samples are collected to obtain the filtered historical training data. The specific implementation steps of the step include step S2221;
step S2221, calculating the number of the historical training data contained in each clustering result to obtain sixth data; and calculating the number of all the historical training data to obtain seventh data, dividing the sixth data by the seventh data to obtain eighth data, judging the size between the eighth data and a preset second threshold value, and if the eighth data is larger than or equal to the preset second threshold value, marking all the historical training data contained in the clustering result corresponding to the eighth data as the first sample.
In the step, if the eighth data is greater than or equal to a preset second threshold, the data contained in the clustering result is proved to be larger in number, and the data can be used as a first sample; according to the steps, the historical training samples can be cleaned and filtered, so that noise data can be removed, and the quality of the historical training samples is improved;
s3, inputting the history use record of the winch to be used into the loss analysis model to obtain a loss type corresponding to the winch to be used, and determining a quality score according to the loss type;
in the step, the historical use record of the winch to be used is input into a loss analysis model, so that the loss type corresponding to the winch to be used can be obtained; the loss type-quality score corresponding table can be constructed in advance, and after the loss type corresponding to the winch to be used is obtained, the corresponding quality score can be quickly found out according to the corresponding table;
and S4, screening all the winches to be used according to the environmental information of the winches to be installed to obtain remaining winches to be used, calculating the matching degree score between each winch to be used in the remaining winches to be used and the environmental information, calculating the comprehensive score corresponding to each winch to be used in the remaining winches to be used according to the quality score and the matching degree score, and scheduling each winch to be used in the remaining winches to be used according to the comprehensive score.
In the step, besides considering the quality score of each winch to be used, the environmental information of the winch to be installed is considered, so that the most suitable winch can be found for installation and use, and the specific implementation steps comprise step S41, step S42 and step S43;
s41, acquiring a plurality of first pictures contained in environmental information of a winch to be installed, wherein each first picture comprises a ground picture of the winch to be installed;
in the step, in order to improve the accuracy of the final ground area, a plurality of first pictures are acquired, wherein the first pictures can be construction site pictures of the winch to be installed, the construction site pictures comprise ground pictures for installing the winch, namely, the winch is installed in the ground picture area;
step S42, carrying out enhancement treatment on each first picture aiming at each first picture to obtain enhanced pictures, cutting the enhanced pictures to obtain ground pictures of the installation winch, and calculating the ground area according to the ground pictures of the installation winch;
after a plurality of first pictures are acquired, each first picture is subjected to enhancement treatment, so that the quality of the first picture can be improved, the cutting quality is improved, when the first picture is cut, the first picture can be cut in a manual mode, the corresponding area of the first picture can be calculated according to the minimum external matrix corresponding to the ground picture after the first picture is cut, in addition, other calculation methods can be adopted for calculating the ground area, and the method is not limited in the step;
step S43, analyzing the ground area corresponding to each first picture to obtain a final ground area, calculating the occupied area of each winch to be used, carrying out difference calculation on the final ground area and each occupied area, and processing the winch to be used according to a difference calculation result to obtain a residual winch to be used, wherein if the difference calculation result is negative, deleting the winch to be used corresponding to the difference calculation result to obtain the residual winch to be used;
and according to the calculation result of the difference value corresponding to each of the remaining winches to be used, matching the corresponding matching degree score of each of the remaining winches to be used.
In the step, in the remaining winch to be used, the final ground area is subtracted by the occupied area, and the matching degree score is larger as the obtained difference value calculation result is larger; meanwhile, in this step, in order to improve accuracy of the final ground area, this step analyzes the ground area corresponding to each first picture, and the specific implementation steps include step S431 and step S432;
step S431, collecting all ground areas to obtain a ground area set, carrying out cluster analysis on all the ground areas to obtain a plurality of first cluster results, sorting the number of the ground areas contained in each cluster result to obtain the sorted number, and calculating a first numerical value and a second numerical value corresponding to the sorted number, wherein the first numerical value comprises a quartile range, and the second numerical value comprises a quartile range;
step S432, adding the first value to a second value with a preset multiple to obtain a third value, comparing the number of the ground areas included in each first clustering result with the third value, if the number is greater than or equal to the third value, deleting the ground areas included in the first clustering result from the ground area set to obtain a deleted ground area set, and if the number is less than the third value, not performing any processing; and carrying out average value calculation on all the ground areas in the deleted ground area set to obtain the final ground area.
In this step, the third value may be understood as an abnormal threshold, and compared with manually setting the abnormal threshold, the setting method in this step may improve the accuracy of the third value, and the preset multiple may be 1.5 or 3; the abnormal ground area can be removed by the method in the step, and average value calculation is carried out after removal, so that the accuracy of the final ground area can be improved;
meanwhile, in step S4, calculating a composite score corresponding to each winch to be used according to the quality score and the matching degree score, and scheduling the winch to be used according to the composite score, which includes step S44;
s44, constructing a winch comprehensive scoring system, wherein the winch comprehensive scoring system comprises a first-level index layer and a second-level index layer, the first-level index layer is a winch comprehensive scoring index, and the second-level index layer comprises a quality scoring index and a matching degree scoring index; constructing a hierarchical structure according to the winch comprehensive scoring system, constructing a judgment matrix according to the weights of all factors in the hierarchical structure, and calculating to obtain the weights corresponding to the quality scoring indexes and the weights corresponding to the matching degree scoring indexes according to the judgment matrix; and carrying out weighted summation processing according to the quality score, the matching degree score and the weight corresponding to each quality score, so as to obtain the comprehensive score corresponding to each winch to be used in the remaining winches to be used.
In this step, after the composite score is calculated, the winch to be used is scheduled in the order from the top to the bottom of the composite score.
In the embodiment, the winch to be used is scored from two dimensions, the dimension is considered from the history use record, a large number of history use records of the history winch are obtained, the history use records are marked with loss types, the convolutional neural network model is trained after marking, noise data is screened from training data before training, the noise data is removed, the accuracy of the training data is improved, a loss analysis model is obtained after training, and the loss type is calculated only by inputting the history use records into the loss analysis model when the loss type corresponding to the winch to be used is required to be calculated, so that the purpose of quickly and accurately calculating the loss type is realized; secondly, environmental information of the winch to be installed is considered, and the situation that the installation area is smaller than the occupied area of the winch to be used possibly exists in construction site installation is considered, so that the winch to be used is screened according to the relation between the final ground area and the occupied area, and the remaining winch to be used is obtained; and finally, calculating the comprehensive score of the to-be-used winches in the remaining to-be-used winches, and dispatching the to-be-used winches according to the comprehensive score.
Example 2
As shown in fig. 2, the embodiment provides a mining winch automation management and dispatching platform, which comprises an acquisition module 701, a screening module 702, a calculation module 703 and a dispatching module 704.
The obtaining module 701 is configured to obtain a history usage record of each winch to be used, environmental information of the winch to be installed, and history training data, where each history training data includes a history usage record of each history winch and a loss type corresponding to each history usage record;
the screening module 702 is configured to perform anomaly screening on the historical training data to obtain screened historical training data, and train the convolutional neural network model by using the screened historical training data to obtain a loss analysis model;
the calculation module 703 is configured to input the history usage record of the winch to be used into the loss analysis model, obtain a loss type corresponding to the winch to be used, and determine a quality score according to the loss type;
and the scheduling module 704 is configured to screen all the winches to be used according to the environmental information of the winches to be installed, obtain remaining winches to be used, calculate a matching degree score between each of the remaining winches to be used and the environmental information, calculate a comprehensive score corresponding to each of the remaining winches to be used according to the quality score and the matching degree score, and schedule each of the remaining winches to be used according to the comprehensive score.
In a specific embodiment of the disclosure, the screening module 702 further includes a clustering unit 7021 and a first analysis unit 7022.
A clustering unit 7021, configured to input the historical training data into a recurrent neural network model, record data output by a hidden layer of the recurrent neural network model as first data, and perform clustering processing on all the historical training data according to the first data to obtain a plurality of clustering results;
the first analysis unit 7022 is configured to analyze the number of the historical training data included in each loss type in each clustering result, record the loss type corresponding to the largest number as a first type, record the number of the historical training data included in the first type as second data, record the number of the historical training data included in each clustering result as third data, divide the second data by the third data, obtain fourth data, complete screening of the historical training data according to the fourth data, and obtain screened historical training data.
In one embodiment of the present disclosure, the first analysis unit 7022 further includes a second analysis unit 70221 and a third analysis unit 70222.
A second analysis unit 70221, configured to analyze the fourth data, where if the fourth data is greater than a preset first threshold, analyze the number of loss types included in the clustering result corresponding to the fourth data, and if only one type of loss types is included, record each historical training data in the clustering result corresponding to the fourth data as a first sample; if two or more than two types of the data are included, the number of the historical training data included in each loss type in the clustering result corresponding to the fourth data is calculated to obtain fifth data, and each historical training data included in the loss type corresponding to the largest fifth data is recorded as a first sample;
and a third analysis unit 70222, configured to analyze the fourth data, where if the fourth data is less than or equal to the preset first threshold, obtain a first sample again according to the number of the historical training data included in each clustering result, and aggregate all the first samples to obtain the filtered historical training data.
In a specific embodiment of the disclosure, the third analyzing unit 70222 further includes a first calculating unit 702221.
A first calculating unit 702221, configured to calculate the number of historical training data included in each clustering result, to obtain sixth data; and calculating the number of all the historical training data to obtain seventh data, dividing the sixth data by the seventh data to obtain eighth data, judging the size between the eighth data and a preset second threshold value, and if the eighth data is larger than or equal to the preset second threshold value, marking all the historical training data contained in the clustering result corresponding to the eighth data as the first sample.
In a specific embodiment of the disclosure, the scheduling module 704 further includes an obtaining unit 7041, a cutting unit 7042, and a matching unit 7043.
An obtaining unit 7041, configured to obtain a plurality of first pictures included in environmental information of a winch to be installed, where each first picture includes a ground picture of the winch to be installed;
the cutting unit 7042 is configured to perform enhancement processing on each first picture to obtain enhanced pictures, cut the enhanced pictures to obtain ground pictures of the installation winch, and calculate a ground area according to the ground pictures of the installation winch;
the matching unit 7043 is configured to analyze a ground area corresponding to each first picture to obtain a final ground area, calculate a floor area of each winch to be used, perform difference calculation on the final ground area and each floor area, and process the winch to be used according to a difference calculation result to obtain a remaining winch to be used, where if the difference calculation result is a negative number, delete the winch to be used corresponding to the difference calculation result to obtain the remaining winch to be used; and according to the calculation result of the difference value corresponding to each of the remaining winches to be used, matching the corresponding matching degree score of each of the remaining winches to be used.
In a specific embodiment of the disclosure, the matching unit 7043 further includes a collecting unit 70431 and a second calculating unit 70432.
The aggregation unit 70431 is configured to aggregate all ground areas to obtain a ground area set, perform cluster analysis on all the ground areas to obtain a plurality of first cluster results, sort the number of the ground areas included in each cluster result to obtain a sorted number, calculate a first numerical value and a second numerical value corresponding to the sorted number, where the first numerical value includes a quartile range, and the second numerical value includes a quartile range;
a second calculating unit 70432, configured to add the first value to a second value of a preset multiple to obtain a third value, compare the number of the ground areas included in each first clustering result with the third value, delete the ground areas included in the first clustering result from the ground area set if the number is greater than or equal to the third value, and obtain a deleted ground area set, and do no processing if the number is less than the third value; and carrying out average value calculation on all the ground areas in the deleted ground area set to obtain the final ground area.
In one embodiment of the present disclosure, the scheduling module 704 further includes a construction unit 7044.
The construction unit 7044 is used for constructing a winch comprehensive scoring system, wherein the winch comprehensive scoring system comprises a first-level index layer and a second-level index layer, the first-level index layer is a winch comprehensive scoring index, and the second-level index layer comprises a quality scoring index and a matching degree scoring index; constructing a hierarchical structure according to the winch comprehensive scoring system, constructing a judgment matrix according to the weights of all factors in the hierarchical structure, and calculating to obtain the weights corresponding to the quality scoring indexes and the weights corresponding to the matching degree scoring indexes according to the judgment matrix; and carrying out weighted summation processing according to the quality score, the matching degree score and the weight corresponding to each quality score, so as to obtain the comprehensive score corresponding to each winch to be used in the remaining winches to be used.
It should be noted that, regarding the platform in the above embodiment, the specific manner in which the respective modules perform the operations has been described in detail in the embodiment regarding the method, and will not be described in detail herein.
Example 3
Corresponding to the above method embodiments, the embodiments of the present disclosure further provide a mining winch automation management and scheduling apparatus, and the mining winch automation management and scheduling apparatus described below and the mining winch automation management and scheduling method described above may be referred to correspondingly with each other.
Fig. 3 is a block diagram of a mining winch automated management and dispatch apparatus 800, according to an exemplary embodiment. As shown in fig. 3, the mining winch automated management and dispatch apparatus 800 may include: a processor 801, a memory 802. The mining winch automation management and dispatch apparatus 800 may also include one or more of a multimedia component 803, an I/O interface 804, and a communication component 805.
The processor 801 is configured to control the overall operation of the mining winch automated management and dispatch apparatus 800 to perform all or part of the steps of the mining winch automated management and dispatch method described above. Memory 802 is used to store various types of data to support operation at the mining winch automated management and dispatch equipment 800, which may include, for example, instructions for any application or method operating on the mining winch automated management and dispatch equipment 800, as well as application-related data, such as contact data, messages, pictures, audio, video, and the like. The Memory 802 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen, the audio component being for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted through the communication component 805. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 804 provides an interface between the processor 801 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the mining winch automated management and dispatch equipment 800 and other equipment. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near FieldCommunication, NFC for short), 2G, 3G or 4G, or a combination of one or more thereof, the respective communication component 805 may thus comprise: wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the mining winch automation management and dispatch apparatus 800 may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), digital signal processors (DigitalSignal Processor, abbreviated as DSP), digital signal processing devices (Digital Signal Processing Device, abbreviated as DSPD), programmable logic devices (Programmable Logic Device, abbreviated as PLD), field programmable gate arrays (Field Programmable Gate Array, abbreviated as FPGA), controllers, microcontrollers, microprocessors, or other electronic components for performing the mining winch automation management and dispatch method described above.
In another exemplary embodiment, a computer readable storage medium is also provided that includes program instructions that, when executed by a processor, implement the steps of the mining winch automated management and dispatch method described above. For example, the computer readable storage medium may be the memory 802 described above including program instructions executable by the processor 801 of the mining winch automation management and dispatch apparatus 800 to perform the mining winch automation management and dispatch method described above.
Example 4
Corresponding to the above method embodiments, the disclosed embodiments also provide a readable storage medium, and a readable storage medium described below and a mining winch automation management and scheduling method described above may be referred to correspondingly.
A readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the mining winch automation management and scheduling method of the above method embodiment.
The readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, and the like.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. An automated management and dispatch platform for a mining winch, comprising:
the system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for acquiring historical use records of each winch to be used, environmental information of the winch to be installed and historical training data, and each historical training data comprises the historical use record of each historical winch and a loss type corresponding to each historical use record;
the screening module is used for carrying out abnormal screening on the historical training data to obtain screened historical training data, and training a convolutional neural network model by utilizing the screened historical training data to obtain a loss analysis model;
the calculation module is used for inputting the historical use record of the winch to be used into the loss analysis model to obtain a loss type corresponding to the winch to be used, and determining a quality score according to the loss type;
the scheduling module is used for screening all the winches to be used according to the environmental information of the winches to be installed to obtain remaining winches to be used, calculating the matching degree score between each winch to be used in the remaining winches to be used and the environmental information, calculating the comprehensive score corresponding to each winch to be used in the remaining winches to be used according to the quality score and the matching degree score, and scheduling each winch to be used in the remaining winches to be used according to the comprehensive score.
2. The mining winch automated management and dispatch platform of claim 1, wherein the screening module comprises:
the clustering unit is used for inputting the historical training data into a recurrent neural network model, recording data output by a hidden layer of the recurrent neural network model as first data, and carrying out clustering processing on all the historical training data according to the first data to obtain a plurality of clustering results;
the first analysis unit is configured to analyze the number of the historical training data included in each loss type in each clustering result, record the loss type corresponding to the largest number as a first type, record the number of the historical training data included in the first type as second data, record the number of the historical training data included in each clustering result as third data, divide the second data by the third data to obtain fourth data, complete screening of the historical training data according to the fourth data, and obtain screened historical training data.
3. The mining winch automated management and dispatch platform of claim 2, wherein the first analysis unit comprises:
the second analysis unit is used for analyzing the fourth data, wherein if the fourth data is larger than a preset first threshold value, the number of loss types contained in the clustering result corresponding to the fourth data is analyzed, and if only one type of loss types is contained, each historical training data in the clustering result corresponding to the fourth data is recorded as a first sample; if two or more than two types of the data are included, the number of the historical training data included in each loss type in the clustering result corresponding to the fourth data is calculated to obtain fifth data, and each historical training data included in the loss type corresponding to the largest fifth data is recorded as a first sample;
and a third analysis unit, configured to analyze the fourth data, where if the fourth data is less than or equal to the preset first threshold, a first sample is obtained again according to the number of the historical training data included in each clustering result, and all the first samples are collected to obtain the filtered historical training data.
4. The mining winch automated management and dispatch platform of claim 3, wherein the third analysis unit comprises:
the first calculation unit is used for calculating the number of the historical training data contained in each clustering result to obtain sixth data; and calculating the number of all the historical training data to obtain seventh data, dividing the sixth data by the seventh data to obtain eighth data, judging the size between the eighth data and a preset second threshold value, and if the eighth data is larger than or equal to the preset second threshold value, marking all the historical training data contained in the clustering result corresponding to the eighth data as the first sample.
5. The mining winch automated management and dispatch platform of claim 1, wherein the dispatch module comprises:
the device comprises an acquisition unit, a display unit and a display unit, wherein the acquisition unit is used for acquiring a plurality of first pictures contained in the environmental information of a winch to be installed, and each first picture comprises a ground picture of the winch to be installed;
the cutting unit is used for carrying out enhancement treatment on each first picture aiming at each first picture to obtain enhanced pictures, cutting the enhanced pictures to obtain ground pictures of the installation winch, and calculating the ground area according to the ground pictures of the installation winch;
the matching unit is used for analyzing the ground area corresponding to each first picture to obtain a final ground area, calculating the occupied area of each winch to be used, carrying out difference calculation on the final ground area and each occupied area, and processing the winch to be used according to a difference calculation result to obtain the remaining winch to be used, wherein if the difference calculation result is negative, deleting the winch to be used corresponding to the difference calculation result to obtain the remaining winch to be used; and according to the calculation result of the difference value corresponding to each of the remaining winches to be used, matching the corresponding matching degree score of each of the remaining winches to be used.
6. The mining winch automated management and dispatch platform of claim 5, wherein the matching unit comprises:
the aggregation unit is used for aggregating all the ground areas to obtain a ground area set, carrying out cluster analysis on all the ground areas to obtain a plurality of first cluster results, sequencing the number of the ground areas contained in each cluster result to obtain the sequenced number, and calculating a first numerical value and a second numerical value corresponding to the sequenced number, wherein the first numerical value comprises a quartile range, and the second numerical value comprises a quartile range;
the second calculation unit is used for adding the first numerical value and a second numerical value of a preset multiple to obtain a third numerical value, comparing the number of the ground areas contained in each first clustering result with the third numerical value, deleting the ground areas contained in the first clustering result from the ground area set if the number is larger than or equal to the third numerical value, and obtaining a deleted ground area set, and if the number is smaller than the third numerical value, performing no processing; and carrying out average value calculation on all the ground areas in the deleted ground area set to obtain the final ground area.
7. The mining winch automated management and dispatch platform of claim 1, wherein the dispatch module comprises:
the system comprises a construction unit, a matching unit and a matching unit, wherein the construction unit is used for constructing a winch comprehensive scoring system, the winch comprehensive scoring system comprises a first-level index layer and a second-level index layer, the first-level index layer is a winch comprehensive scoring index, and the second-level index layer comprises a quality scoring index and a matching degree scoring index; constructing a hierarchical structure according to the winch comprehensive scoring system, constructing a judgment matrix according to the weights of all factors in the hierarchical structure, and calculating to obtain the weights corresponding to the quality scoring indexes and the weights corresponding to the matching degree scoring indexes according to the judgment matrix; and carrying out weighted summation processing according to the quality score, the matching degree score and the weight corresponding to each quality score, so as to obtain the comprehensive score corresponding to each winch to be used in the remaining winches to be used.
8. An automated management and dispatch method for a mining winch is characterized by comprising the following steps:
acquiring historical use records of each winch to be used, environmental information of the winch to be installed and historical training data, wherein each historical training data comprises the historical use record of each historical winch and a loss type corresponding to each historical use record;
performing abnormal screening on the historical training data to obtain screened historical training data, and training a convolutional neural network model by using the screened historical training data to obtain a loss analysis model;
inputting the historical use record of the winch to be used into the loss analysis model to obtain a loss type corresponding to the winch to be used, and determining a quality score according to the loss type;
screening all the winches to be used according to the environmental information of the winches to be installed to obtain residual winches to be used, calculating the matching degree score between each winch to be used in the residual winches to be used and the environmental information, calculating the comprehensive score corresponding to each winch to be used in the residual winches to be used according to the quality score and the matching degree score, and scheduling each winch to be used in the residual winches to be used according to the comprehensive score.
9. An automated management and dispatch device for mining winches, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the mining winch automated management and dispatch method of claim 8 when executing the computer program.
10. A readable storage medium, characterized by:
the readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the mining winch automated management and dispatch method of claim 8.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117411383A (en) * | 2023-12-15 | 2024-01-16 | 华夏天信智能物联股份有限公司 | Integrated mining variable-frequency speed regulation equipment control method and device and electronic equipment |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030171972A1 (en) * | 2002-01-28 | 2003-09-11 | James Heskin | Scheduling system and method |
| CN109918205A (en) * | 2019-03-25 | 2019-06-21 | 深圳市网心科技有限公司 | A kind of edge device dispatching method, system, device and computer storage medium |
| US20200218270A1 (en) * | 2019-01-07 | 2020-07-09 | Wing Aviation Llc | Using machine learning techniques to estimate available energy for vehicles |
| WO2021130298A1 (en) * | 2019-12-26 | 2021-07-01 | Siemens Mobility GmbH | System, apparatus and method for managing energy consumption at a technical installation |
| CN115081584A (en) * | 2022-05-16 | 2022-09-20 | 合肥科大智能机器人技术有限公司 | Power equipment health management method, system and medium based on machine learning |
| US20220365240A1 (en) * | 2021-05-12 | 2022-11-17 | Halliburton Energy Services, Inc. | Determination Of Material State Behind Casing Using Multi-Receiver Ultrasonic Data And Machine Learning |
| WO2022267085A1 (en) * | 2021-06-25 | 2022-12-29 | 浙江海瑞网络科技有限公司 | Artificial-intelligence-based data management method and system for data center |
| CN115827797A (en) * | 2022-12-02 | 2023-03-21 | 湖南科创信息技术股份有限公司 | Environmental data analysis and integration method and system based on big data |
| US20230229986A1 (en) * | 2020-07-28 | 2023-07-20 | Construct A.I. Inc. | Machine Learning Based Construction Methods, Techniques, and Algorithms |
-
2023
- 2023-08-15 CN CN202311020221.0A patent/CN116739317B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030171972A1 (en) * | 2002-01-28 | 2003-09-11 | James Heskin | Scheduling system and method |
| US20200218270A1 (en) * | 2019-01-07 | 2020-07-09 | Wing Aviation Llc | Using machine learning techniques to estimate available energy for vehicles |
| CN109918205A (en) * | 2019-03-25 | 2019-06-21 | 深圳市网心科技有限公司 | A kind of edge device dispatching method, system, device and computer storage medium |
| WO2021130298A1 (en) * | 2019-12-26 | 2021-07-01 | Siemens Mobility GmbH | System, apparatus and method for managing energy consumption at a technical installation |
| US20230229986A1 (en) * | 2020-07-28 | 2023-07-20 | Construct A.I. Inc. | Machine Learning Based Construction Methods, Techniques, and Algorithms |
| US20220365240A1 (en) * | 2021-05-12 | 2022-11-17 | Halliburton Energy Services, Inc. | Determination Of Material State Behind Casing Using Multi-Receiver Ultrasonic Data And Machine Learning |
| WO2022267085A1 (en) * | 2021-06-25 | 2022-12-29 | 浙江海瑞网络科技有限公司 | Artificial-intelligence-based data management method and system for data center |
| CN115081584A (en) * | 2022-05-16 | 2022-09-20 | 合肥科大智能机器人技术有限公司 | Power equipment health management method, system and medium based on machine learning |
| CN115827797A (en) * | 2022-12-02 | 2023-03-21 | 湖南科创信息技术股份有限公司 | Environmental data analysis and integration method and system based on big data |
Non-Patent Citations (2)
| Title |
|---|
| 胥军等: "一种船用绞车故障诊断方法", 《起重运输机械》, no. 8, pages 85 - 88 * |
| 黄国勤主编: "《Python数据分析与机器学习》", vol. 1, 中国农业出版社, pages: 172 - 174 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117411383A (en) * | 2023-12-15 | 2024-01-16 | 华夏天信智能物联股份有限公司 | Integrated mining variable-frequency speed regulation equipment control method and device and electronic equipment |
| CN117411383B (en) * | 2023-12-15 | 2024-02-20 | 华夏天信智能物联股份有限公司 | Integrated mining variable-frequency speed regulation equipment control method and device and electronic equipment |
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|---|---|
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Denomination of invention: An automated management and scheduling platform, method, equipment, and medium for mining winches Granted publication date: 20231031 Pledgee: China Postal Savings Bank Limited by Share Ltd. Zoucheng branch Pledgor: SHANDONG YUFEI TRANSMISSION TECHNOLOGY Co.,Ltd. Registration number: Y2024980017086 |