CN115828771B - Performance evaluation method, system and medium for mechanical transmission element - Google Patents
Performance evaluation method, system and medium for mechanical transmission element Download PDFInfo
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- CN115828771B CN115828771B CN202310101203.9A CN202310101203A CN115828771B CN 115828771 B CN115828771 B CN 115828771B CN 202310101203 A CN202310101203 A CN 202310101203A CN 115828771 B CN115828771 B CN 115828771B
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Abstract
The invention relates to a performance evaluation method, a system and a medium of a mechanical transmission element, which belong to the technical field of transmission element evaluation, and the invention obtains processing factors which influence the performance of the mechanical transmission element in real time according to a processing factor database, and comprehensively evaluates and analyzes the processing factors which influence the performance of the mechanical transmission element in real time according to a gray system theory so as to obtain an analysis result; further predicting the performance of the mechanical transmission element according to the analysis result to obtain a performance prediction result of the mechanical transmission element; and when the performance prediction result of the mechanical transmission element is not more than the preset performance information, generating corresponding processing adjustment advice according to the analysis result. According to the invention, the corresponding equipment is evaluated according to the performance of the current mechanical transmission element to judge whether the current mechanical transmission element can be processed, and when the current mechanical transmission element cannot be processed, the corresponding processing equipment is recommended to a worker for reference.
Description
Technical Field
The present invention relates to the field of transmission element evaluation technologies, and in particular, to a performance evaluation method, system, and medium for a mechanical transmission element.
Background
Mechanical transmission is one of the very important technologies in the machine-building industry, since there is a transmission whenever there is a place of motion. Whatever form of energy is required to achieve a particular function, as intended for a particular motion, it must be achieved by a transmission. While mechanical transmission elements are often required to have reduced cost mechanical transmission mechanisms, increased load carrying capacity, life and transmission efficiency, reduced noise, weight, failure rate and running costs. However, mechanical transmission elements often have multiple staged processes during manufacture, such as gears, gears of the same size, and after carburization quenching or nitriding can transmit more torque than gears that have not been heat treated, i.e., the heat treated gears are much smaller in size than gears that have not been heat treated, without power change. Especially, the new materials such as 20CrMnTi, 17CrNiMn4V and the like which are currently appeared have better effect, a layer of very thin special high-quality material can be coated on the surface of the gear, and the wear resistance of the gear can be further improved through heat treatment. For example, the processing of the transmission shaft needs a series of work of forging, normalizing, rough turning, finish turning, deep hole drilling, gear hobbing, oil hole drilling, degreasing pretreatment, degreasing, hot water cleaning, cold water cleaning, activating treatment, ultrasonic rinsing, chemical agent cleaning and drying. Today, before machining, a worker needs to evaluate some transmission elements that need to achieve a predetermined performance, so as to provide accurate data for the worker to refer to, so that the corresponding worker knows that the required mechanical element to be machined can be machined, and in this process, different equipment can lead to different machining results, such as transmission elements with different precision, which can be machined by a numerical control machine. In the prior art, a performance evaluation method for a mechanical transmission element is lack, and the mechanical transmission element to be processed is rapidly evaluated, so that whether the mechanical transmission element can be processed according to the existing equipment of a company or not can be rapidly judged.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a performance evaluation method, a system and a medium for a mechanical transmission element.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the first aspect of the present invention provides a performance evaluation method for a mechanical transmission element, comprising the steps of:
acquiring processing factors influencing the performance of a mechanical transmission element through big data, screening the processing factors, and constructing a processing factor database according to the screened factors;
acquiring processing factors which influence the performance of the mechanical transmission element in real time according to the processing factor database, and comprehensively evaluating and analyzing the processing factors which influence the performance of the mechanical transmission element in real time through a gray system theory to acquire an analysis result;
predicting the performance of the mechanical transmission element according to the analysis result to obtain a performance prediction result of the mechanical transmission element;
and if the performance prediction result of the mechanical transmission element is not greater than the preset performance information, generating a corresponding processing adjustment suggestion according to the analysis result.
Further, in a preferred embodiment of the present invention, the processing factors affecting the performance of the mechanical transmission element are obtained through big data, and the processing factors are screened, which specifically includes the following steps:
Setting keyword information for influencing the performance of the mechanical transmission element, setting a search tag according to the keyword information, and searching through big data based on the search tag to obtain an initial processing factor for influencing the performance of the mechanical transmission element;
constructing a processing factor database, calculating attention scores among the initial processing factors through a local sensitive attention mechanism, and presetting a plurality of attention score ranges;
clustering and integrating the initial processing factors according to the attention score range to obtain a corresponding processing factor clustering subset, and selecting one initial processing factor in the corresponding processing factor clustering subset randomly to generate clustered initial processing factors;
and inputting the clustered initial processing factors into different spaces of the processing factor database.
Further, in a preferred embodiment of the present invention, processing factors that affect the performance of the mechanical transmission element in real time are obtained according to the processing factor database, and comprehensive evaluation analysis is performed on the processing factors that affect the performance of the mechanical transmission element in real time by using a gray system theory, so as to obtain an analysis result, and specifically includes the following steps:
Acquiring working information of processing equipment corresponding to the current mechanical transmission element, and inputting the working information into the processing factor database for matching so as to acquire processing factors which influence the performance of the mechanical transmission element in real time;
constructing a processing precision related characteristic factor sequence according to the processing factors which influence the performance of the mechanical transmission element in real time, and constructing a gray system characteristic factor sequence based on the processing precision related characteristic factor sequence;
calculating the association coefficient of each characteristic factor in the gray system characteristic factor sequence for the performance of the mechanical transmission element, and calculating the association degree corresponding to each characteristic factor according to the association coefficient of each characteristic factor for the performance of the mechanical transmission element;
judging whether the association degree is larger than a preset association degree, if so, acquiring processing factors corresponding to the association degree larger than the preset association degree, and outputting the processing factors corresponding to the association degree larger than the preset association degree as analysis results.
Further, in a preferred embodiment of the present invention, the performance of the mechanical transmission element is predicted according to the analysis result, so as to obtain a performance prediction result of the mechanical transmission element, which specifically includes the following steps:
Acquiring the processing technology information of the current mechanical transmission element, acquiring a large amount of processing state information of the mechanical transmission element corresponding to the analysis result through big data, and constructing a mechanical transmission element processing state prediction model based on a convolutional neural network;
inputting the machining condition information of the mechanical transmission element into the machining condition prediction model of the mechanical transmission element for coding learning until the machining condition prediction of the mechanical transmission element meets the preset requirement, and storing model parameters;
inputting the analysis result into the mechanical transmission element machining condition prediction model to acquire machining condition information of the mechanical transmission element corresponding to the analysis result, and comparing the machining condition information of the mechanical transmission element corresponding to the analysis result with the current machining process information of the mechanical transmission element to obtain a deviation rate;
judging whether the deviation rate is larger than a preset deviation rate threshold value, if so, marking the processing technology information corresponding to the deviation rate larger than the preset deviation rate threshold value as abnormal condition information, and acquiring a performance prediction result of the mechanical transmission element according to the abnormal condition information.
Further, in a preferred embodiment of the present invention, the method for obtaining the performance prediction result of the mechanical transmission element according to the abnormal situation information specifically includes the following steps:
acquiring functional information of the mechanical transmission element corresponding to the processing equipment, and extracting keywords according to the functional information of the mechanical transmission element corresponding to the processing equipment so as to acquire one or more pieces of functional information;
acquiring performance keyword data of a mechanical transmission element, performing relevance text calculation according to the functional information and the performance keyword data of the mechanical transmission element, acquiring performance keyword data of the mechanical transmission element with relevance text larger than the relevance text, and determining processing equipment which is likely to generate abnormality according to the performance keyword data of the mechanical transmission element with relevance text larger than the relevance text;
acquiring working information of the processing equipment which is likely to generate abnormality, and acquiring a mechanical transmission element performance data change range under different working information according to the working information of the processing equipment which is likely to generate abnormality;
and acquiring the maximum mechanical transmission element performance data in the mechanical transmission element performance variation range under the different working information, and outputting the maximum mechanical transmission element performance data as a mechanical transmission element performance prediction result.
Further, in a preferred embodiment of the present invention, if the performance prediction result of the mechanical transmission element is not greater than the preset performance information, a corresponding processing adjustment suggestion is generated according to the analysis result, which specifically includes the following steps:
judging whether the performance prediction result of the mechanical transmission element is not more than preset performance information, and if so, generating a device retrieval label according to the preset performance information;
performing equipment retrieval through a big data network according to the equipment retrieval tag, acquiring related equipment larger than the preset performance information, and generating recommendation equipment according to the related equipment larger than the preset performance information;
acquiring evaluation data information of each recommendation device, counting the evaluation data information, and taking the recommendation device with the highest evaluation rate in the evaluation data information as a final replacement device;
and generating corresponding processing adjustment suggestions according to the final replacement equipment.
A second aspect of the present invention provides a performance evaluation system for a mechanical transmission element, wherein the system includes a memory and a processor, the memory contains a performance evaluation method program for the mechanical transmission element, and when the performance evaluation method program for the mechanical transmission element is executed by the processor, the following steps are implemented:
Acquiring processing factors influencing the performance of a mechanical transmission element through big data, screening the processing factors, and constructing a processing factor database according to the screened factors;
acquiring processing factors which influence the performance of the mechanical transmission element in real time according to the processing factor database, and comprehensively evaluating and analyzing the processing factors which influence the performance of the mechanical transmission element in real time through a gray system theory to acquire an analysis result;
predicting the performance of the mechanical transmission element according to the analysis result to obtain a performance prediction result of the mechanical transmission element;
and if the performance prediction result of the mechanical transmission element is not greater than the preset performance information, generating a corresponding processing adjustment suggestion according to the analysis result.
In this embodiment, the performance of the mechanical transmission element is predicted according to the analysis result, so as to obtain a performance prediction result of the mechanical transmission element, which specifically includes the following steps:
acquiring the processing technology information of the current mechanical transmission element, acquiring a large amount of processing state information of the mechanical transmission element corresponding to the analysis result through big data, and constructing a mechanical transmission element processing state prediction model based on a convolutional neural network;
Inputting the machining condition information of the mechanical transmission element into the machining condition prediction model of the mechanical transmission element for coding learning until the machining condition prediction of the mechanical transmission element meets the preset requirement, and storing model parameters;
inputting the analysis result into the mechanical transmission element machining condition prediction model to acquire machining condition information of the mechanical transmission element corresponding to the analysis result, and comparing the machining condition information of the mechanical transmission element corresponding to the analysis result with the current machining process information of the mechanical transmission element to obtain a deviation rate;
judging whether the deviation rate is larger than a preset deviation rate threshold value, if so, marking the processing technology information corresponding to the deviation rate larger than the preset deviation rate threshold value as abnormal condition information, and acquiring a performance prediction result of the mechanical transmission element according to the abnormal condition information.
In this embodiment, the performance prediction result of the mechanical transmission element is obtained according to the abnormal condition information, and specifically includes the following steps:
acquiring functional information of the mechanical transmission element corresponding to the processing equipment, and extracting keywords according to the functional information of the mechanical transmission element corresponding to the processing equipment so as to acquire one or more pieces of functional information;
Acquiring performance keyword data of a mechanical transmission element, performing relevance text calculation according to the functional information and the performance keyword data of the mechanical transmission element, acquiring performance keyword data of the mechanical transmission element with relevance text larger than the relevance text, and determining processing equipment which is likely to generate abnormality according to the performance keyword data of the mechanical transmission element with relevance text larger than the relevance text;
acquiring working information of the processing equipment which is likely to generate abnormality, and acquiring a mechanical transmission element performance data change range under different working information according to the working information of the processing equipment which is likely to generate abnormality;
and acquiring the maximum mechanical transmission element performance data in the mechanical transmission element performance variation range under the different working information, and outputting the maximum mechanical transmission element performance data as a mechanical transmission element performance prediction result.
A third aspect of the present invention provides a computer-readable storage medium containing therein a performance evaluation method program of a mechanical transmission element, which when executed by a processor, implements the steps of the performance evaluation method of a mechanical transmission element of any one of the above.
The invention solves the defects existing in the background technology, and has the following beneficial effects:
according to the invention, the processing factors influencing the performance of the mechanical transmission element are obtained through big data, the processing factors are screened, and a processing factor database is constructed according to the screened factors; further, processing factors which influence the performance of the mechanical transmission element in real time are obtained according to the processing factor database, and comprehensive evaluation analysis is carried out on the processing factors which influence the performance of the mechanical transmission element in real time through a gray system theory so as to obtain an analysis result; further predicting the performance of the mechanical transmission element according to the analysis result to obtain a performance prediction result of the mechanical transmission element; and when the performance prediction result of the mechanical transmission element is not more than the preset performance information, generating corresponding processing adjustment advice according to the analysis result. According to the invention, the corresponding equipment is evaluated according to the performance of the current mechanical transmission element to judge whether the current mechanical transmission element can be processed, and when the current mechanical transmission element cannot be processed, the corresponding processing equipment is recommended to a worker for reference.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other embodiments of the drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 illustrates a specific method flow diagram of a method of evaluating performance of a mechanical transmission element;
FIG. 2 illustrates a first method flow diagram of a method of evaluating performance of a mechanical transmission element;
FIG. 3 illustrates a second method flow chart of a method of evaluating performance of a mechanical transmission element;
FIG. 4 illustrates a system block diagram of a performance evaluation system for a mechanical transmission element.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
As shown in fig. 1, a first aspect of the present invention provides a performance evaluation method of a mechanical transmission element, including the steps of:
s102, acquiring processing factors influencing the performance of a mechanical transmission element through big data, screening the processing factors, and constructing a processing factor database according to the screened factors;
S104, acquiring processing factors which influence the performance of the mechanical transmission element in real time according to a processing factor database, and comprehensively evaluating and analyzing the processing factors which influence the performance of the mechanical transmission element in real time through a gray system theory to acquire an analysis result;
s106, predicting the performance of the mechanical transmission element according to the analysis result to obtain a performance prediction result of the mechanical transmission element;
s108, if the performance prediction result of the mechanical transmission element is not greater than the preset performance information, generating corresponding processing adjustment advice according to the analysis result.
It should be noted that, the present invention evaluates the corresponding device according to the performance of the currently standby mechanical transmission element, so as to determine whether the currently standby mechanical transmission element can be processed, and when the currently standby mechanical transmission element cannot be processed, the present invention recommends the corresponding processing device to the staff for reference.
Further, in a preferred embodiment of the present invention, the processing factors affecting the performance of the mechanical transmission element are obtained by big data, and the processing factors are screened, which specifically includes the following steps:
setting keyword information on the performance of the mechanical transmission element, setting a retrieval tag according to the keyword information, and retrieving through big data based on the retrieval tag to obtain an initial processing factor on the performance of the mechanical transmission element;
Constructing a processing factor database, calculating attention scores among initial processing factors through a local sensitive attention mechanism, and presetting a plurality of attention score ranges;
clustering and integrating the initial processing factors according to the attention score range to obtain a corresponding processing factor clustering subset, and selecting one random initial processing factor in the corresponding processing factor clustering subset to generate clustered initial processing factors;
the clustered initial process factors are entered into different spaces of a process factor database.
It should be noted that, the machining factors affecting the mechanical transmission element can improve the mechanical performance of the transmission shaft, for example, in the forging process, the internal stress of the mechanical transmission element can be eliminated by heat treatment under a certain temperature, and the machining factors affecting the performance of the mechanical transmission element can be quickly searched from a big data network by the method, so that the clustered initial machining factors are input into different spaces of a machining factor database, and the quick query and recognition functions of the machining factors in the evaluation system are improved, so that the operation speed of the evaluation system is improved.
Further, in a preferred embodiment of the present invention, processing factors that affect the performance of the mechanical transmission element in real time are obtained according to a processing factor database, and comprehensive evaluation analysis is performed on the processing factors that affect the performance of the mechanical transmission element in real time by using a gray system theory, so as to obtain an analysis result, and specifically includes the following steps:
Acquiring working information of the current mechanical transmission element corresponding to processing equipment, and inputting the working information into a processing factor database for matching so as to acquire processing factors which influence the performance of the mechanical transmission element in real time;
constructing a machining precision related characteristic factor sequence according to machining factors which influence the performance of the mechanical transmission element in real time, and constructing a gray system characteristic factor sequence based on the machining precision related characteristic factor sequence;
calculating the association coefficient of each characteristic factor in the gray system characteristic factor sequence for the performance of the mechanical transmission element, and calculating the association degree corresponding to each characteristic factor according to the association coefficient of each characteristic factor for the performance of the mechanical transmission element;
judging whether the association degree is larger than the preset association degree, if so, acquiring processing factors corresponding to the association degree larger than the preset association degree, and outputting the processing factors corresponding to the association degree larger than the preset association degree as analysis results.
It should be noted that a system is composed of many factors, and if the factors composing the system are clear, the relationship between the factors is clear, the structure composing the system is clear, and the principle of the system is clear, then the system is a white system. Such a system is a black system if the system information is completely lacking. Then the system between the black system and the white system, i.e. the system with part of the information known and part of the information unknown, is called the grey system. The relevance of each processing factor to the performance of the mechanical transmission element can be influenced through a gray system (gray prediction), so that the processing factor with the relevance being larger than that corresponding to the preset relevance is selected.
As shown in fig. 2, in a preferred embodiment of the present invention, the performance of the mechanical transmission element is predicted according to the analysis result, so as to obtain a performance prediction result of the mechanical transmission element, which specifically includes the following steps:
s202, acquiring processing technology information of a current mechanical transmission element, acquiring processing condition information of the mechanical transmission element corresponding to a large number of analysis results through big data, and constructing a mechanical transmission element processing condition prediction model based on a convolutional neural network;
s204, inputting the machining condition information of the mechanical transmission element into a machining condition prediction model of the mechanical transmission element to carry out coding learning until the machining condition prediction of the mechanical transmission element meets the preset requirement, and storing model parameters;
s206, inputting the analysis result into a mechanical transmission element machining condition prediction model to obtain machining condition information of the mechanical transmission element corresponding to the analysis result, and comparing the machining condition information of the mechanical transmission element corresponding to the analysis result with the current machining process information of the mechanical transmission element to obtain a deviation rate;
and S208, judging whether the deviation rate is larger than a preset deviation rate threshold value, if so, marking the processing technology information corresponding to the deviation rate larger than the preset deviation rate threshold value as abnormal condition information, and acquiring a performance prediction result of the mechanical transmission element according to the abnormal condition information.
It should be noted that the machining process information may be process information such as forging performance, mechanical performance, machining precision, etc. that the mechanical transmission element needs to achieve, and the machining conditions that the mechanical transmission element can achieve under different machining factors are inconsistent, that is, the performance of the mechanical element that the mechanical transmission element can achieve under different machining factors is inconsistent, for example, forging can improve strength of the transmission shaft, and heat treatment can change plastic deformation of the transmission shaft. The neural network consists of an input layer, an output layer and a hidden layer, wherein the hidden layer carries out data calculation by setting one or more layers of neurons, each layer of neurons can be provided with a plurality of nodes, and the neural network can be used for rapidly training the achievable processing conditions under various processing factors.
As shown in fig. 3, in a preferred embodiment of the present invention, the performance prediction result of the mechanical transmission element is obtained according to the abnormal condition information, which specifically includes the following steps:
s302, acquiring functional information of machining equipment corresponding to the mechanical transmission element, and extracting keywords according to the functional information of the machining equipment corresponding to the mechanical transmission element so as to acquire one or more pieces of functional information;
S304, acquiring performance keyword data of the mechanical transmission element, performing relevance text calculation according to the functional information and the performance keyword data of the mechanical transmission element, acquiring the performance keyword data of the mechanical transmission element with the relevance text being larger than the relevance text, and determining processing equipment which is likely to generate abnormality according to the performance keyword data of the mechanical transmission element with the relevance text being larger than the relevance text;
s306, acquiring working information of machining equipment which can generate abnormality, and acquiring a mechanical transmission element performance data change range under different working information according to the working information of the machining equipment which can generate abnormality;
s306, acquiring the largest mechanical transmission element performance data in the mechanical transmission element performance variation range under different working information, and outputting the largest mechanical transmission element performance data as a mechanical transmission element performance prediction result.
It should be noted that, in this embodiment, by the method, a processing device (a processing device that may generate an abnormality) that may not conform to the performance of the mechanical transmission element may be selected, where the working information may be a temperature, for example, when the mechanical performance that can be achieved under different temperatures is inconsistent during the heat treatment, and, for example, when the mechanical performance that can be achieved by the mechanical transmission element under different forging forces is inconsistent during the forging process, by the method, a processing device that may generate an abnormality may be effectively selected, so as to obtain a range of variation of the performance data of the mechanical transmission element that may generate an abnormality processing device.
Further, in a preferred embodiment of the present invention, if the performance prediction result of the mechanical transmission element is not greater than the preset performance information, a corresponding processing adjustment suggestion is generated according to the analysis result, which specifically includes the following steps:
judging whether the performance prediction result of the mechanical transmission element is not more than preset performance information, and if the performance prediction result of the mechanical transmission element is not more than the preset performance information, generating equipment retrieval labels according to the preset performance information;
performing equipment retrieval through a big data network according to the equipment retrieval tag, acquiring related equipment larger than preset performance information, and generating recommendation equipment according to the related equipment larger than the preset performance information;
acquiring evaluation data information of each recommendation device, counting the evaluation data information, and taking the recommendation device with the highest evaluation rate in the evaluation data information as a final replacement device;
and generating corresponding processing adjustment suggestions according to the final replacement equipment.
It should be noted that, in this embodiment, when it is determined that the performance prediction result of the mechanical transmission element is not greater than the preset performance information, the relevant device is obtained, the recommended device with the highest evaluation rate in the evaluation data information is selected as the final replacement device, and the corresponding device is recommended to the corresponding user.
In addition, the method can further comprise the following steps:
acquiring a recommendation device with the highest current good score, acquiring the failure rate of the recommendation device with the highest current good score under current working information according to the recommendation device with the highest current good score, and judging whether the failure rate is larger than a preset failure rate;
if the fault rate is greater than a preset fault rate, rejecting recommended equipment with the fault rate greater than the preset fault rate, and acquiring recommended equipment with the minimum fault rate as candidate equipment;
acquiring the success rate of machining the mechanical transmission elements with the same performance of the candidate equipment through a big data network, and constructing a success rate ranking table according to the success rate of machining the mechanical transmission elements with the same performance of the candidate equipment;
and acquiring the processing equipment with the highest success rate in the success rate ranking table, and recommending the processing equipment with the highest success rate as the final processing equipment.
By the method, equipment with higher processing success rate can be further screened out to be supplied for users for reference, and the rationality of processing adjustment suggestions is improved.
Furthermore, the method can further comprise the following steps:
if the performance prediction result of the mechanical transmission element is larger than the preset performance information, acquiring the working information of the processing equipment in the current area, and inputting the working information of the processing equipment in the current area into a processing factor database for storage;
Acquiring processing technology information of a current mechanical transmission element, and inputting the processing technology information of the current mechanical transmission element into the processing factor database for matching calculation;
acquiring working equipment with working information of the processing equipment in the current area, which is matched and calculated, wherein the working information of the processing equipment in the current area is larger than the working information of the processing equipment in the current area, and taking the working equipment as preselected equipment;
and sequencing according to the working information of the pre-selected equipment, and outputting the pre-selected equipment which acquires the minimum working information as field candidate processing equipment.
It should be noted that, in this embodiment, when the performance prediction result of the mechanical transmission element is greater than the preset performance information, the pre-selecting device of the minimum working information is selected to be output as the final processing device, so that the pre-selecting device of the larger working information can be used in other places with higher requirements, and the working device allocation of the evaluation system is more reasonable.
Furthermore, the method can further comprise the following steps:
acquiring service data information of the field candidate processing equipment, and inputting the service data information into a Bayesian network to acquire predicted fault time of the field candidate processing equipment;
acquiring estimated machining time of a single mechanical transmission element and machining quantity information of the mechanical transmission element, and calculating total machining time information of the mechanical transmission element according to the estimated machining time of the single mechanical transmission element and the machining quantity information of the mechanical transmission element;
Judging whether the total machining time information of the mechanical transmission element is coincident with the predicted fault time of the field candidate machining equipment or not, and if the total machining time information of the mechanical transmission element is not coincident with the predicted fault time of the field candidate machining equipment, outputting the field candidate machining equipment as final candidate equipment;
if the total processing time information of the mechanical transmission element is coincident with the predicted fault time of the field candidate processing equipment, generating early warning information according to the predicted fault time of the field candidate processing equipment, and generating related maintenance suggestions according to the early warning information.
When the total processing time information of the mechanical transmission elements is not overlapped with the predicted fault time of the field candidate processing equipment, the mechanical transmission elements to be processed in the batch are not faulted in the processing process, and when the total processing time information of the mechanical transmission elements is overlapped with the predicted fault time of the field candidate processing equipment, the prompt of the current processing equipment can be rapidly maintained, and more reasonable processing equipment can be selected to supply the mechanical transmission elements for processing by the method, so that the distribution of the processing equipment and the processing process are more reasonable, and the equipment management of processing production is more reasonable.
As shown in fig. 4, the second aspect of the present invention provides a performance evaluation system for a mechanical transmission element, wherein the system includes a memory 41 and a processor 62, and the memory 41 contains a performance evaluation method program for the mechanical transmission element, and when the performance evaluation method program for the mechanical transmission element is executed by the processor 62, the following steps are implemented:
acquiring processing factors influencing the performance of the mechanical transmission element through big data, screening the processing factors, and constructing a processing factor database according to the screened factors;
acquiring processing factors which influence the performance of the mechanical transmission element in real time according to a processing factor database, and comprehensively evaluating and analyzing the processing factors which influence the performance of the mechanical transmission element in real time through a gray system theory to acquire an analysis result;
predicting the performance of the mechanical transmission element according to the analysis result to obtain a performance prediction result of the mechanical transmission element;
and if the performance prediction result of the mechanical transmission element is not greater than the preset performance information, generating corresponding processing adjustment advice according to the analysis result.
In this embodiment, the performance of the mechanical transmission element is predicted according to the analysis result, so as to obtain a performance prediction result of the mechanical transmission element, which specifically includes the following steps:
Acquiring the processing technology information of the current mechanical transmission element, acquiring the processing condition information of the mechanical transmission element corresponding to a large amount of analysis results through big data, and constructing a mechanical transmission element processing condition prediction model based on a convolutional neural network;
inputting the machining condition information of the mechanical transmission element into a machining condition prediction model of the mechanical transmission element for coding learning until the machining condition prediction of the mechanical transmission element meets the preset requirement, and storing model parameters;
inputting the analysis result into a mechanical transmission element machining condition prediction model to obtain machining condition information of a mechanical transmission element corresponding to the analysis result, and comparing the machining condition information of the mechanical transmission element corresponding to the analysis result with the current machining process information of the mechanical transmission element to obtain a deviation rate;
judging whether the deviation rate is larger than a preset deviation rate threshold value, if so, marking the processing technology information corresponding to the deviation rate larger than the preset deviation rate threshold value as abnormal condition information, and acquiring a performance prediction result of the mechanical transmission element according to the abnormal condition information.
In this embodiment, the performance prediction result of the mechanical transmission element is obtained according to the abnormal condition information, and specifically includes the following steps:
Acquiring functional information of the mechanical transmission element corresponding to the processing equipment, and extracting keywords according to the functional information of the mechanical transmission element corresponding to the processing equipment so as to acquire one or more pieces of functional information;
acquiring performance keyword data of a mechanical transmission element, performing relevance text calculation according to the functional information and the performance keyword data of the mechanical transmission element, acquiring the performance keyword data of the mechanical transmission element with the relevance text being larger than the relevance text, and determining processing equipment which is likely to generate abnormality according to the performance keyword data of the mechanical transmission element with the relevance text being larger than the relevance text;
acquiring working information of machining equipment which can generate abnormality, and acquiring a performance data change range of a mechanical transmission element under different working information according to the working information of the machining equipment which can generate abnormality;
and acquiring the maximum mechanical transmission element performance data in the mechanical transmission element performance variation range under different working information, and outputting the maximum mechanical transmission element performance data as a mechanical transmission element performance prediction result.
A third aspect of the present invention provides a computer-readable storage medium containing therein a performance evaluation method program of a mechanical transmission element, which when executed by a processor, implements the steps of the performance evaluation method of a mechanical transmission element of any one of the above.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (9)
1. A method of evaluating the performance of a mechanical transmission element, comprising the steps of:
acquiring processing factors influencing the performance of a mechanical transmission element through big data, screening the processing factors, and constructing a processing factor database according to the screened factors;
acquiring processing factors which influence the performance of the mechanical transmission element in real time according to the processing factor database, and comprehensively evaluating and analyzing the processing factors which influence the performance of the mechanical transmission element in real time through a gray system theory to acquire an analysis result;
predicting the performance of the mechanical transmission element according to the analysis result to obtain a performance prediction result of the mechanical transmission element;
if the performance prediction result of the mechanical transmission element is not greater than the preset performance information, generating a corresponding processing adjustment suggestion according to the analysis result;
If the performance prediction result of the mechanical transmission element is not greater than the preset performance information, generating a corresponding processing adjustment suggestion according to the analysis result, wherein the processing adjustment suggestion specifically comprises the following steps:
judging whether the performance prediction result of the mechanical transmission element is not more than preset performance information, and if so, generating a device retrieval label according to the preset performance information;
performing equipment retrieval through a big data network according to the equipment retrieval tag, acquiring related equipment larger than the preset performance information, and generating recommendation equipment according to the related equipment larger than the preset performance information;
acquiring evaluation data information of each recommendation device, counting the evaluation data information, and taking the recommendation device with the highest evaluation rate in the evaluation data information as a final replacement device;
and generating corresponding processing adjustment suggestions according to the final replacement equipment.
2. A method of evaluating the performance of a mechanical transmission element according to claim 1, wherein the processing factors affecting the performance of the mechanical transmission element are obtained by big data and the processing factors are screened, comprising the steps of:
Setting keyword information for influencing the performance of the mechanical transmission element, setting a search tag according to the keyword information, and searching through big data based on the search tag to obtain an initial processing factor for influencing the performance of the mechanical transmission element;
constructing a processing factor database, calculating attention scores among the initial processing factors through a local sensitive attention mechanism, and presetting a plurality of attention score ranges;
clustering and integrating the initial processing factors according to the attention score range to obtain a corresponding processing factor clustering subset, and selecting one initial processing factor in the corresponding processing factor clustering subset randomly to generate clustered initial processing factors;
and inputting the clustered initial processing factors into different spaces of the processing factor database.
3. The method for evaluating the performance of a mechanical transmission element according to claim 1, wherein the processing factors which affect the performance of the mechanical transmission element in real time are obtained according to the processing factor database, and the processing factors which affect the performance of the mechanical transmission element in real time are comprehensively evaluated and analyzed by a gray system theory to obtain an analysis result, and the method specifically comprises the following steps:
Acquiring working information of processing equipment corresponding to the current mechanical transmission element, and inputting the working information into the processing factor database for matching so as to acquire processing factors which influence the performance of the mechanical transmission element in real time;
constructing a processing precision related characteristic factor sequence according to the processing factors which influence the performance of the mechanical transmission element in real time, and constructing a gray system characteristic factor sequence based on the processing precision related characteristic factor sequence;
calculating the association coefficient of each characteristic factor in the gray system characteristic factor sequence for the performance of the mechanical transmission element, and calculating the association degree corresponding to each characteristic factor according to the association coefficient of each characteristic factor for the performance of the mechanical transmission element;
judging whether the association degree is larger than a preset association degree, if so, acquiring processing factors corresponding to the association degree larger than the preset association degree, and outputting the processing factors corresponding to the association degree larger than the preset association degree as analysis results.
4. The method according to claim 1, wherein the performance of the mechanical transmission element is predicted according to the analysis result to obtain a performance prediction result of the mechanical transmission element, and specifically comprising the steps of:
Acquiring the processing technology information of the current mechanical transmission element, acquiring a large amount of processing state information of the mechanical transmission element corresponding to the analysis result through big data, and constructing a mechanical transmission element processing state prediction model based on a convolutional neural network;
inputting the machining condition information of the mechanical transmission element into the machining condition prediction model of the mechanical transmission element for coding learning until the machining condition prediction of the mechanical transmission element meets the preset requirement, and storing model parameters;
inputting the analysis result into the mechanical transmission element machining condition prediction model to acquire machining condition information of the mechanical transmission element corresponding to the analysis result, and comparing the machining condition information of the mechanical transmission element corresponding to the analysis result with the current machining process information of the mechanical transmission element to obtain a deviation rate;
judging whether the deviation rate is larger than a preset deviation rate threshold value, if so, marking the processing technology information corresponding to the deviation rate larger than the preset deviation rate threshold value as abnormal condition information, and acquiring a performance prediction result of the mechanical transmission element according to the abnormal condition information.
5. The method for evaluating the performance of a mechanical transmission element according to claim 4, wherein the step of obtaining the performance prediction result of the mechanical transmission element based on the abnormal-condition information comprises the steps of:
acquiring functional information of the mechanical transmission element corresponding to the processing equipment, and extracting keywords according to the functional information of the mechanical transmission element corresponding to the processing equipment so as to acquire one or more pieces of functional information;
acquiring performance keyword data of a mechanical transmission element, performing relevance text calculation according to the functional information and the performance keyword data of the mechanical transmission element, acquiring performance keyword data of the mechanical transmission element with relevance text larger than the relevance text, and determining processing equipment which is likely to generate abnormality according to the performance keyword data of the mechanical transmission element with relevance text larger than the relevance text;
acquiring working information of the processing equipment which is likely to generate abnormality, and acquiring a mechanical transmission element performance data change range under different working information according to the working information of the processing equipment which is likely to generate abnormality;
and acquiring the maximum mechanical transmission element performance data in the mechanical transmission element performance variation range under the different working information, and outputting the maximum mechanical transmission element performance data as a mechanical transmission element performance prediction result.
6. A performance evaluation system for a mechanical transmission element, the system comprising a memory and a processor, the memory containing a performance evaluation method program for the mechanical transmission element, the performance evaluation method program for the mechanical transmission element, when executed by the processor, performing the steps of:
acquiring processing factors influencing the performance of a mechanical transmission element through big data, screening the processing factors, and constructing a processing factor database according to the screened factors;
acquiring processing factors which influence the performance of the mechanical transmission element in real time according to the processing factor database, and comprehensively evaluating and analyzing the processing factors which influence the performance of the mechanical transmission element in real time through a gray system theory to acquire an analysis result;
predicting the performance of the mechanical transmission element according to the analysis result to obtain a performance prediction result of the mechanical transmission element;
if the performance prediction result of the mechanical transmission element is not greater than the preset performance information, generating a corresponding processing adjustment suggestion according to the analysis result;
if the performance prediction result of the mechanical transmission element is not greater than the preset performance information, generating a corresponding processing adjustment suggestion according to the analysis result, wherein the processing adjustment suggestion specifically comprises the following steps:
Judging whether the performance prediction result of the mechanical transmission element is not more than preset performance information, and if so, generating a device retrieval label according to the preset performance information;
performing equipment retrieval through a big data network according to the equipment retrieval tag, acquiring related equipment larger than the preset performance information, and generating recommendation equipment according to the related equipment larger than the preset performance information;
acquiring evaluation data information of each recommendation device, counting the evaluation data information, and taking the recommendation device with the highest evaluation rate in the evaluation data information as a final replacement device;
and generating corresponding processing adjustment suggestions according to the final replacement equipment.
7. The system according to claim 6, wherein the performance of the mechanical transmission element is predicted based on the analysis result to obtain a performance prediction result of the mechanical transmission element, and specifically comprising the steps of:
acquiring the processing technology information of the current mechanical transmission element, acquiring a large amount of processing state information of the mechanical transmission element corresponding to the analysis result through big data, and constructing a mechanical transmission element processing state prediction model based on a convolutional neural network;
Inputting the machining condition information of the mechanical transmission element into the machining condition prediction model of the mechanical transmission element for coding learning until the machining condition prediction of the mechanical transmission element meets the preset requirement, and storing model parameters;
inputting the analysis result into the mechanical transmission element machining condition prediction model to acquire machining condition information of the mechanical transmission element corresponding to the analysis result, and comparing the machining condition information of the mechanical transmission element corresponding to the analysis result with the current machining process information of the mechanical transmission element to obtain a deviation rate;
judging whether the deviation rate is larger than a preset deviation rate threshold value, if so, marking the processing technology information corresponding to the deviation rate larger than the preset deviation rate threshold value as abnormal condition information, and acquiring a performance prediction result of the mechanical transmission element according to the abnormal condition information.
8. The system for evaluating the performance of a mechanical transmission element according to claim 7, wherein the method for obtaining the performance prediction result of the mechanical transmission element according to the abnormal situation information comprises the steps of:
Acquiring functional information of the mechanical transmission element corresponding to the processing equipment, and extracting keywords according to the functional information of the mechanical transmission element corresponding to the processing equipment so as to acquire one or more pieces of functional information;
acquiring performance keyword data of a mechanical transmission element, performing relevance text calculation according to the functional information and the performance keyword data of the mechanical transmission element, acquiring performance keyword data of the mechanical transmission element with relevance text larger than the relevance text, and determining processing equipment which is likely to generate abnormality according to the performance keyword data of the mechanical transmission element with relevance text larger than the relevance text;
acquiring working information of the processing equipment which is likely to generate abnormality, and acquiring a mechanical transmission element performance data change range under different working information according to the working information of the processing equipment which is likely to generate abnormality;
and acquiring the maximum mechanical transmission element performance data in the mechanical transmission element performance variation range under the different working information, and outputting the maximum mechanical transmission element performance data as a mechanical transmission element performance prediction result.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium contains therein a performance evaluation method program of a mechanical transmission element, which, when executed by a processor, implements the steps of the performance evaluation method of a mechanical transmission element according to any one of claims 1-5.
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