CN114626291B - Model design method and system for transmission system demand adaptation evaluation - Google Patents
Model design method and system for transmission system demand adaptation evaluation Download PDFInfo
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Abstract
The application provides a model design method and system for transmission system demand adaptation evaluation. The method comprises the following steps: acquiring demand parameters of a target application scene on a transmission system; acquiring a preset transmission system model, deconstructing the preset transmission system model, and extracting model parameters of the transmission system model; inputting the demand parameters and the model parameters into a trained demand adaptation evaluation model of the transmission system, or calculating according to a formula to obtain the model matching degree, wherein the demand adaptation evaluation model of the transmission system is constructed on the basis of a neural network; determining that the transmission system model can be used in a target application scene according to the fact that the model matching degree is equal to or higher than a preset threshold value; and according to the fact that the model matching degree is lower than a preset threshold value, determining that the transmission system model cannot be used in the target application scene, and adjusting and updating model parameters until the model matching degree is equal to or higher than the preset threshold value. The method and the device aim at the requirements of the transmission system in an application scene, evaluate whether a preset model is matched with the preset model, realize the evaluation of the matching degree and search for the optimal solution.
Description
Technical Field
The application relates to the technical field of speed reducers, in particular to a model design method and system for demand adaptation assessment of a transmission system.
Background
At present, aiming at different requirements of different speed reducer customers, speed reducer manufacturing enterprises can only design different speed reducer types, structures, and select proper parts, working parameters and the like according to the requirements, and meanwhile, data conditions of multiple aspects such as a part supply side, a part demand side, assembly capacity, complete machine requirements and the like need to be manually coordinated, and if the supply or the demand of one side is reduced or increased, capacity digestion of other aspects can be influenced. The result of manual coordination is often long in design cycle, complex in design scheme, large in personnel investment, high in time, manpower, material resources and financial resources cost, and intelligent manufacturing is not realized.
At present, no method for intelligently matching the requirements of a transmission system to obtain a proper transmission model for a specific application scene exists.
Disclosure of Invention
In view of the above, an object of the present invention is to provide a model design method and system for adaptive evaluation of transmission system requirements, which can specifically solve the existing technical problems.
Based on the above purpose, the present application provides a model design method for demand adaptation evaluation of a transmission system, comprising:
acquiring demand parameters of a target application scene on a transmission system, wherein the demand parameters at least comprise: the system comprises a speed reducing motor demand parameter, a driver demand parameter, a controller demand parameter and a sensor demand parameter;
acquiring a preset transmission system model, deconstructing the preset transmission system model, and extracting model parameters of the transmission system model, wherein the model parameters comprise preset speed reducing motor parameters, preset driver parameters, preset controller parameters and preset sensor parameters;
inputting the demand parameters and the model parameters into a trained demand adaptation evaluation model of the transmission system, or calculating according to a formula to obtain the model matching degree, wherein the demand adaptation evaluation model of the transmission system is constructed on the basis of a neural network;
determining that the transmission system model can be used in the target application scenario according to the fact that the model matching degree is equal to or higher than a preset threshold value; determining that the transmission system model cannot be used in the target application scene according to the fact that the model matching degree is lower than a preset threshold value;
and adjusting and updating the model parameters according to the condition that the model matching degree is lower than a preset threshold value, then repeating the process of inputting the demand parameters and the model parameters into a trained transmission system demand adaptation evaluation model to obtain the model matching degree until the model matching degree is determined to be equal to or higher than the preset threshold value, and taking the adjusted transmission system model as an optimal solution.
Further, the demand parameters further include: types and numbers of breather, lubricator, and oil seals.
Further, the acquiring of the demand parameters of the target application scenario for the transmission system includes at least: gear motor demand parameter, driver demand parameter, controller demand parameter, sensor demand parameter include:
constructing three-dimensional space parameters and a mechanical model of a target application scene;
and obtaining a speed reduction motor demand parameter, a driver demand parameter, a controller demand parameter and a sensor demand parameter according to the three-dimensional space parameter and the mechanical model of the target application scene.
Further, the acquiring a preset transmission system model, deconstructing the preset transmission system model, and extracting model parameters of the transmission system model, where the model parameters include preset speed reduction motor parameters, preset driver parameters, preset controller parameters, and preset sensor parameters, includes:
acquiring a preset transmission system model, wherein the transmission system model is pre-stored in a form of a knowledge graph, and the knowledge graph comprises nodes and a relation vector arrow;
deconstructing the preset transmission system model to obtain the knowledge graph;
and extracting to obtain preset speed reducing motor parameters, preset driver parameters, preset controller parameters and preset sensor parameters according to the knowledge graph.
Further, the inputting the requirement parameters and the model parameters into a trained transmission system requirement adaptation evaluation model to obtain a model matching degree, wherein the transmission system requirement adaptation evaluation model is constructed based on a neural network, and comprises:
importing the demand parameters of a large number of known scenes and the model parameters into a convolutional neural network to obtain the matching degree of each model; taking a feature vector formed by the demand parameters of the known scene and the model parameters as a training sample, and constructing a training sample set;
training an AKC model consisting of an automatic encoder model based on a fully-connected neural network and a K-means model by using a training sample set;
and inputting the requirement parameters of the target application scene and the model parameters into a trained transmission system requirement adaptation evaluation model to obtain the model matching degree.
Further, the model matching degree is calculated according to the following formula:
wherein P is the matching degree, n is the number of the individual parameters of the demand parameters,
weight coefficient for the ith individual parameter, in>
Is the number of the ith demand parameter, <' >>
For the type of the ith demand parameter, device for selecting or keeping>
For the number of the i-th model parameter,
for the type of the i-th model parameter, device for selecting or keeping>
To represent device for selecting or keeping>
And &>
Similarity between the two types.
Further, the adjusting and updating the model parameters according to the fact that the model matching degree is lower than a preset threshold, then repeating the process of inputting the demand parameters and the model parameters into a trained transmission system demand adaptation evaluation model to obtain the model matching degree until the model matching degree is determined to be equal to or higher than the preset threshold, includes:
according to the fact that the matching degree of the model is lower than a preset threshold value, determining the difference value of each single parameter between the demand parameter of the target application scene and the model parameter to obtain the single parameter with the maximum difference value;
adjusting the single parameter with the maximum difference in the model parameters to reduce the difference between the single parameter and the single parameter in the demand parameters of the target scene;
and repeating the process of inputting the adjusted model parameters and the adjusted demand parameters into a trained transmission system demand adaptation evaluation model to obtain the model matching degree until the model matching degree is determined to be equal to or higher than a preset threshold value.
Based on the above object, the present application further provides a model design system for demand adaptation evaluation of a transmission system, comprising:
the demand acquisition module is used for acquiring demand parameters of the application scene on the transmission system, and the demand parameters at least comprise: the system comprises a speed reducing motor demand parameter, a driver demand parameter, a controller demand parameter and a sensor demand parameter;
the system comprises a preset model acquisition module, a transmission system analysis module and a control module, wherein the preset model acquisition module is used for acquiring a preset transmission system model, deconstructing the preset transmission system model and extracting model parameters of the transmission system model, and the model parameters comprise preset speed reducing motor parameters, preset driver parameters, preset controller parameters and preset sensor parameters;
the model matching evaluation module is used for inputting the demand parameters and the model parameters into a trained transmission system demand adaptation evaluation model or calculating according to a formula to obtain the model matching degree, and the transmission system demand adaptation evaluation model is constructed on the basis of a neural network;
the judging module is used for determining that the transmission system model can be used for the target application scene according to the fact that the model matching degree is equal to or higher than a preset threshold value; determining that the transmission system model cannot be used in the target application scene according to the fact that the model matching degree is lower than a preset threshold value;
and the model parameter updating module is used for adjusting and updating the model parameters according to the condition that the model matching degree is lower than a preset threshold value, then repeating the process of inputting the demand parameters and the model parameters into a trained transmission system demand adaptation evaluation model to obtain the model matching degree until the model matching degree is determined to be equal to or higher than the preset threshold value, and taking the adjusted transmission system model as an optimal solution.
In general, the advantages of the present application and the experience brought to the user are:
according to the method and the device, whether the preset model is matched with the transmission system is evaluated according to the requirements of the transmission system in an application scene, so that the evaluation of the matching degree is realized, the intelligent decision optimization analysis is realized, and the intelligent manufacturing of the speed reducer is realized.
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In the drawings, like reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are therefore not to be considered limiting of its scope.
FIG. 1 shows the present application schematic diagram of system architecture principle.
FIG. 2 illustrates a flow chart of a model design method for transmission system demand adaptation evaluation according to an embodiment of the application.
Fig. 3 is a schematic diagram illustrating a process of inputting each individual demand parameter into a neural network to obtain a matching result.
FIG. 4 illustrates a block diagram of a model design system for adaptive evaluation of drive train requirements according to an embodiment of the present application.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
fig. 6 shows a schematic diagram of a storage medium provided in an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that, in the present application, the embodiments and features of the embodiments may be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 shows a schematic diagram of the system architecture of the present application. In the embodiment of the application, the demand parameters of a target application scene on a transmission system are obtained; acquiring a preset transmission system model, deconstructing the preset transmission system model, and extracting model parameters of the transmission system model; inputting the demand parameters and the model parameters into a trained demand adaptation evaluation model of the transmission system, obtaining the matching degree of the model, and constructing a transmission system demand adaptation evaluation model based on a neural network; determining that the transmission system model can be used in a target application scene according to the fact that the model matching degree is equal to or higher than a preset threshold value; and determining that the transmission system model cannot be used in a target application scene according to the condition that the model matching degree is lower than a preset threshold, adjusting and updating model parameters until the condition that the model matching degree is equal to or higher than the preset threshold is determined, and taking the adjusted transmission system model as an optimal solution. The method and the device aim at the requirements of the transmission system in an application scene, evaluate whether the preset model is matched with the transmission system, realize evaluation of the matching degree, realize intelligent decision optimization analysis and realize intelligent manufacturing of the transmission system.
FIG. 2 illustrates a flow chart of a model design method for transmission system demand adaptation evaluation according to an embodiment of the application. As shown in FIG. 2, the model design method for the adaptive evaluation of the transmission system requirement comprises the following steps:
step 101: acquiring demand parameters of a target application scene on a transmission system, wherein the demand parameters at least comprise: the system comprises a speed reducing motor demand parameter, a driver demand parameter, a controller demand parameter and a sensor demand parameter; further, the demand parameters further include: the type and number of breather, lubricator, and oil seal.
In the specific step, firstly, three-dimensional space parameters and a mechanical model of a target application scene are constructed;
and then, obtaining a speed reduction motor demand parameter, a driver demand parameter, a controller demand parameter and a sensor demand parameter according to the three-dimensional space parameter and the mechanical model of the target application scene.
The speed reducer can be used in different practical application scenes, the speed reducer is a mechanical transmission device in various fields of national economy, and the products related to the industry comprise various gear speed reducers, planetary gear speed reducers and worm speed reducers, and also comprise various special transmission devices, such as speed increasing devices, speed regulating devices, various composite transmission devices including flexible transmission devices and the like. The product service field relates to the industries of metallurgy, nonferrous metal, coal, building materials, ships, water conservancy, electric power, engineering machinery, petrifaction and the like.
In various fields of national economy and national defense industry, the speed reducer product has wide application. The speed reducer product has strong demand in the industries of light food industry, electric power machinery, construction machinery, metallurgical machinery, cement machinery, environmental protection machinery, electronic and electric appliances, road building machinery, water conservancy machinery, chemical engineering machinery, mining machinery, conveying machinery, building material machinery, rubber machinery, petroleum machinery and the like. For different application scenarios, different requirements are imposed on the size, power, performance and other aspects of the speed reducer. For different application scenarios, different speed reducers are often required to be designed, the present application is directed to addressing this need.
For example, in the food processing industry, first, according to the application position and purpose of the speed reducer in the food processing line, three-dimensional space parameters and a mechanical model are established, such as obtaining the specification of the size of the speed reducer, the rotating speed ratio of matching rotating speed between a prime motor and an executing mechanism, and the like;
then, according to the parameters and the model obtained above, a speed reduction motor demand parameter, a driver demand parameter, a controller demand parameter, and a sensor demand parameter are obtained. For example, according to the size specification of the speed reducer in the food processing line and the rotation speed ratio of the matched rotation speed between the prime motor and the executing mechanism, it can be obtained what the speed reducer needs several transmission sensors, speed reducing motors, drivers and controllers, and what the type of each speed reducing motor, driver, controller and sensor is, how to build the structure between each component, the connection between each component and other components, the installation relationship and the like according to the reasonable design and architecture of the speed reducer. For example, a preset database may be used to store models and mechanical structure parameters of various speed reducers, types and models of speed reducers, speed reduction motors, drivers, controllers, and sensors included in each speed reducer, and a mechanical structure drawing of the speed reducers, and installation positions, connection relationships, and installation relationships of various components in the drawing. The method comprises the steps of inputting a specification of the size of a speed reducer in a food processing production line and query conditions such as a rotating speed ratio of a matched rotating speed between a prime motor and an executing mechanism into a database, querying and providing a matching list in a preset database, searching the speed reducer with the highest matching degree in the list to serve as the most matched speed reducer, reading a speed reducer motor, a driver, a controller, the type and the model of a sensor corresponding to the speed reducer, a mechanical structure drawing of the speed reducer, and the installation position, the connection relation and the installation relation of each component in the drawing to serve as a speed reducer motor requirement parameter, a driver requirement parameter, a controller requirement parameter and a sensor requirement parameter.
Step 102: acquiring a preset transmission system model, deconstructing the preset transmission system model, extracting model parameters of the transmission system model, the model parameters comprise preset speed reducing motor parameters, preset driver parameters, preset controller parameters and preset sensor parameters, and the model parameters comprise:
acquiring a preset transmission system model, wherein the transmission system model is pre-stored in a form of a knowledge graph, and the knowledge graph comprises nodes and relation vector arrows; for example, the nodes of the knowledge-graph include the deceleration motor and the driver, and since the driver provides a driving function for the deceleration motor, the driver is directed to the deceleration motor through a vector arrow in the knowledge-graph. The transmission system model comprises a speed reducer, and the corresponding speed reducing motor, driver, controller, type and model of sensor, mechanical structure drawing of speed reducer, and installation position, connection relation and installation relation of each component in the drawing. In order to construct the knowledge graph of the transmission system model, the speed reducing motor, the driver, the controller and the sensor are used as nodes in the knowledge graph, and the installation positions, the connection relation and the installation relation of the speed reducing motor, the driver, the controller and the sensor are used as vector arrows, so that the whole complete knowledge graph is formed and is stored in a database in advance.
Deconstructing the preset transmission system model to obtain the knowledge graph; for example, the preset transmission system model includes a speed reducer, and a speed reduction motor, a driver, a controller, a type and a model of a sensor corresponding to the speed reducer, a mechanical structure drawing of the speed reducer, and an installation position, a connection relationship and an installation relationship of each component in the drawing, so that by analyzing the character content corresponding to the node and the vector arrow in the knowledge map as a feature, the type and the model of the corresponding speed reduction motor, the driver, the controller and the sensor, and the installation position, the connection relationship and the installation relationship of each component can be obtained as preset speed reduction motor parameters, preset driver parameters, preset controller parameters and preset sensor parameters.
Step 103: inputting the demand parameters and the model parameters into a trained demand adaptation evaluation model of the transmission system, or calculating according to a formula, obtaining the matching degree of the model, wherein the transmission system demand adaptation evaluation model is constructed based on a neural network, and comprises the following steps:
importing the demand parameters of a large number of known scenes and the model parameters into a convolutional neural network to obtain the matching degree of each model; taking a feature vector formed by the demand parameters of the known scene and the model parameters as a training sample, and constructing a training sample set;
training an AKC model consisting of an automatic encoder model based on a fully-connected neural network and a K-means model by using a training sample set;
as shown in fig. 3, the demand parameters of the target application scenario (e.g. application scenario 1 or 2) and the model parameters are input into a trained transmission system demand adaptation evaluation model, and obtaining the matching degree of the model.
Alternatively, the model matching degree is calculated according to the following formula:
wherein P is the matching degree, n is the number of the individual parameters of the demand parameters,
weight coefficient for the ith individual parameter, in>
Is the number of the ith demand parameter, <' >>
For the type of the ith demand parameter>
For the number of the i-th model parameter,
for the type of the i-th model parameter, <' >>
Represents->
And &>
Similarity between the two types.
Therefore, the number, the type and the weight of each single parameter are considered, the matching degree between the requirement of the target application scene and the preset model is obtained by integrating all the single parameters, and whether the model is suitable for the target application scene or not can be scientifically evaluated to the maximum extent.
Step 104: determining that the transmission system model can be used in the target application scenario according to the fact that the model matching degree is equal to or higher than a preset threshold value; and determining that the transmission system model cannot be used in the target application scene according to the fact that the model matching degree is lower than a preset threshold value.
In the present application, the preset threshold is generally above 95% for most application scenarios, and the individual application scenarios can be reduced to 90%. A too low degree of matching indicates that the default conventional model cannot be used in the application scenario, and its internal parameters have to be adjusted to match the specific application scenario.
Step 105: adjusting and updating the model parameters according to the condition that the model matching degree is lower than a preset threshold value, then repeating the process of inputting the demand parameters and the model parameters into a trained transmission system demand adaptation evaluation model to obtain the model matching degree until the condition that the model matching degree is equal to or higher than the preset threshold value is determined, and taking the adjusted transmission system model as an optimal solution, wherein the process comprises the following steps:
determining the difference value of each single parameter between the demand parameter of the target application scene and the model parameter according to the condition that the model matching degree is lower than a preset threshold value, and obtaining the single parameter with the maximum difference value; through this step, the primary reason for the overall model matching degree being below the threshold is found. Therefore, the parameter is firstly adjusted, and the matching degree with the preset model can be optimized and improved.
Adjusting the single parameter with the maximum difference in the model parameters to reduce the difference between the single parameter and the single parameter in the demand parameters of the target scene; by this adjustment, the overall matching degree can be generally increased, but it should be noted that, in order to adapt to the target application scenario, the difference is not directly adjusted to zero, that is, the parameter in the required parameter is not directly adjusted to be equal to the parameter of the preset model, because if directly adjusted to be equal, the parameter may not match the current physical application scenario.
And repeating the process of inputting the adjusted model parameters and the adjusted demand parameters into a trained transmission system demand adaptation evaluation model to obtain the model matching degree until the model matching degree is determined to be equal to or higher than a preset threshold value.
Therefore, the parameter adjusting process of the present application may be a process of repeatedly adjusting each individual parameter according to a specific application physical scenario.
The transmission system disclosed by the embodiment mainly comprises: gear motor, driver, controller, sensor. The sensor is arranged on at least one part of the speed reducing motor, the driver and the controller and is used for detecting and uploading the sensing signal of the corresponding part. It will be appreciated that the transmission system may also include a breather, a lubricator, and an oil seal, which may also have sensors mounted thereon.
The neural network of the present application, in addition to the above convolutional neural network, may also adopt a Long Short-Term Memory network (LSTM), which is a time-cycle neural network, and is specially designed to solve the Long-Term dependence problem of general RNNs (cyclic neural networks), and all RNNs have a chain form of a repetitive neural network module. In the standard RNN, this repeated structure block has only a very simple structure, e.g. one tanh layer.
For example, in the present application, an artificial neural network model is established, specifically: establishing a 3-layer feedforward neural network, defining a parameter P as a network input value, T as a network target value, S = (S1, S2, S3) as a hidden layer unit input vector, B = (B1, B2, B3) as a hidden layer unit output vector, L as an output layer unit input value, C as an output layer unit output value, W = (W1, W2, W3) as an input layer to hidden layer connection weight, V = (V1, V2, V3) as a hidden layer to output layer connection weight, O = (O1, O2, O3) as hidden layer unit output threshold values, R as output layer unit output threshold values, E = (E1, E2, E3) as hidden layer unit errors, and D as output layer unit errors.
According to the method and the device, partial data abnormity may occur by utilizing the matching result of the target application scene and the preset transmission system model, the abnormal data abnormity can be judged as an abnormal prediction result, and the prediction result of which the abnormity exceeds the preset threshold value is deleted.
According to the method and the device, whether the preset model is matched with the transmission system is evaluated according to the requirements of the transmission system in an application scene, so that the evaluation of the matching degree is realized, the intelligent decision optimization analysis is realized, and the intelligent manufacturing of the speed reducer is realized.
The application embodiment provides a model design system for adaptive evaluation of transmission system requirements, which is used for executing the model design method for adaptive evaluation of transmission system requirements described in the above embodiment, as shown in fig. 4, and the system includes:
a requirement obtaining module 501, configured to obtain requirement parameters of an application scenario for a transmission system, where the requirement parameters at least include: the system comprises a speed reducing motor demand parameter, a driver demand parameter, a controller demand parameter and a sensor demand parameter;
a preset model obtaining module 502, configured to obtain a preset transmission system model, deconstruct the preset transmission system model, and extract model parameters of the transmission system model, where the model parameters include a preset speed reduction motor parameter, a preset driver parameter, a preset controller parameter, and a preset sensor parameter;
a model matching evaluation module 503, configured to input the requirement parameters and the model parameters into a trained demand matching evaluation model of the transmission system, or calculating according to a formula to obtain the model matching degree, wherein the transmission system demand adaptation evaluation model is constructed based on a neural network;
a determining module 504, configured to determine that the transmission system model can be used in the target application scenario according to that the model matching degree is equal to or higher than a preset threshold; determining that the transmission system model cannot be used in the target application scene according to the fact that the model matching degree is lower than a preset threshold value;
and a model parameter updating module 505, configured to adjust and update the model parameter according to that the model matching degree is lower than a preset threshold, and then repeat a process of inputting the requirement parameter and the model parameter into a trained transmission system requirement adaptation evaluation model to obtain the model matching degree until it is determined that the model matching degree is equal to or higher than the preset threshold, and take the adjusted transmission system model as an optimal solution.
The model design system for the transmission system requirement adaptation evaluation provided by the embodiment of the application and the model design method for the transmission system requirement adaptation evaluation provided by the embodiment of the application are based on the same inventive concept, and have the same beneficial effects as the method adopted, operated or realized by the application program stored in the system.
The embodiment of the application also provides electronic equipment corresponding to the model design method for the demand adaptation evaluation of the transmission system provided by the embodiment, so as to execute the model design method for the demand adaptation evaluation of the transmission system. The embodiments of the present application are not limited.
Please refer to fig. 5, which illustrates a schematic diagram of an electronic device according to some embodiments of the present application. As shown in fig. 5, the electronic device 2 includes: the system comprises a processor 200, a memory 201, a bus 202 and a communication interface 203, wherein the processor 200, the communication interface 203 and the memory 201 are connected through the bus 202; the memory 201 stores a computer program that can be executed on the processor 200, and the processor 200 executes the computer program to execute the model design method for the adaptive evaluation of the requirement of the transmission system provided by any of the foregoing embodiments.
The Memory 201 may include a high-speed Random Access Memory (RAM) and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 203 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like may be used.
The processor 200 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 200. The Processor 200 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201 and completes the steps of the method in combination with the hardware thereof.
The electronic equipment provided by the embodiment of the application and the model design method for transmission system demand adaptation evaluation provided by the embodiment of the application have the same beneficial effects as the method adopted, operated or realized by the electronic equipment.
Referring to fig. 6, the computer readable storage medium is shown as an optical disc 30, on which a computer program (i.e., a program product) is stored, and when the computer program is executed by a processor, the computer program performs the model design method for adapting and evaluating the requirement of the transmission system according to any of the embodiments.
It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, a phase change memory (PRAM), a Static Random Access Memory (SRAM), a Dynamic Random Access Memory (DRAM), other types of Random Access Memories (RAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a flash memory, or other optical and magnetic storage media, which are not described in detail herein.
The computer-readable storage medium provided by the above-mentioned embodiment of the present application and the model design method for adaptive evaluation of the requirement of the transmission system provided by the embodiment of the present application have the same beneficial effects as the method adopted, operated or implemented by the application program stored in the computer-readable storage medium.
Need to explain the method comprises the following steps:
the algorithms and displays presented herein are not inherently related to any particular computer virtual systems or other devices have inherent relevance. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, this application is not intended to refer to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and any descriptions of specific languages are provided above to disclose the best modes of the present application.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the application and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the application and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in a virtual machine creation system according to embodiments of the present application. The present application may also be embodied as apparatus or system programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present application may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several systems, several of these systems may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various changes or substitutions within the technical scope of the present application, and these should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A model design method for adaptive evaluation of transmission system requirements is characterized by comprising the following steps:
acquiring demand parameters of a target application scene on a transmission system, wherein the demand parameters at least comprise: gear motor demand parameter, driver demand parameter, controller demand parameter, sensor demand parameter include: constructing three-dimensional space parameters and a mechanical model of a target application scene; obtaining a speed reduction motor demand parameter, a driver demand parameter, a controller demand parameter and a sensor demand parameter according to the three-dimensional space parameter and the mechanical model of the target application scene;
acquiring a preset transmission system model, deconstructing the preset transmission system model, and extracting model parameters of the transmission system model, wherein the model parameters comprise preset speed reducing motor parameters, preset driver parameters, preset controller parameters and preset sensor parameters, and the method comprises the following steps of: acquiring a preset transmission system model, wherein the transmission system model is pre-stored in a form of a knowledge graph, and the knowledge graph comprises nodes and a relation vector arrow; deconstructing the preset transmission system model to obtain the knowledge graph; extracting to obtain preset speed reducing motor parameters, preset driver parameters, preset controller parameters and preset sensor parameters according to the knowledge graph;
inputting the demand parameters and the model parameters into a trained transmission system demand adaptation evaluation model, or calculating according to a formula to obtain the model matching degree, wherein the transmission system demand adaptation evaluation model is constructed based on a neural network;
determining that the transmission system model can be used in the target application scenario according to the fact that the model matching degree is equal to or higher than a preset threshold value; determining that the transmission system model cannot be used in the target application scene according to the fact that the model matching degree is lower than a preset threshold value;
and adjusting and updating the model parameters according to the condition that the model matching degree is lower than a preset threshold, then repeatedly inputting the demand parameters and the model parameters into a trained transmission system demand adaptation evaluation model or calculating according to a formula to obtain the process of the model matching degree until the model matching degree is determined to be equal to or higher than the preset threshold, and taking the adjusted transmission system model as an optimal solution.
2. The method of claim 1,
the demand parameters further include: a ventilating device the type and number of lubricating devices and oil seals.
3. The method of claim 1, wherein the first and second light sources are selected from the group consisting of, it is characterized in that the preparation method is characterized in that,
inputting the requirement parameters and the model parameters into a trained transmission system requirement adaptation evaluation model to obtain a model matching degree, wherein the transmission system requirement adaptation evaluation model is constructed based on a neural network and comprises the following steps:
importing the demand parameters of a large quantity of known application scenes and the model parameters into a convolutional neural network to obtain the matching degree of each model; taking a feature vector formed by the demand parameters of the known application scene and the model parameters as a training sample, and constructing a training sample set;
training an AKC model consisting of an automatic encoder model based on a fully-connected neural network and a K-means model by using a training sample set;
and inputting the requirement parameters of the target application scene and the model parameters into a trained transmission system requirement adaptation evaluation model to obtain the model matching degree.
4. The method of claim 1,
calculating the model matching degree according to the following formula:
wherein, P is the matching degree, n is the number of individual parameters of the demand parameter, a i Weight coefficient for the ith individual parameter, N i As to the number of the ith demand parameter, T is i For the type of the ith demand parameter, M i Is the number of i-th model parameters, Q i For the type of the i-th model parameter,
to represent T is i And Q i Similarity between the two types.
5. The method of claim 1,
the adjusting and updating of the model parameters according to the condition that the model matching degree is lower than a preset threshold value, and then repeating the process of inputting the demand parameters and the model parameters into a trained transmission system demand adaptation evaluation model to obtain the model matching degree until the model matching degree is determined to be equal to or higher than the preset threshold value, comprises:
according to the fact that the matching degree of the model is lower than a preset threshold value, determining the difference value of each single parameter between the demand parameter of the target application scene and the model parameter to obtain the single parameter with the maximum difference value;
adjusting the single parameter with the maximum difference in the model parameters to reduce the difference between the single parameter and the single parameter in the demand parameters of the target application scene;
repeating the process of inputting the adjusted model parameters and the adjusted demand parameters into the trained demand adaptation evaluation model of the transmission system to obtain the model matching degree, until it is determined that the degree of model matching is equal to or higher than a preset threshold.
6. A model design system for adaptive evaluation of transmission system requirements, comprising:
the demand acquisition module is used for acquiring demand parameters of the application scene on the transmission system, and the demand parameters at least comprise: gear motor demand parameter, driver demand parameter, controller demand parameter, sensor demand parameter include: constructing three-dimensional space parameters and a mechanical model of a target application scene; obtaining a speed reduction motor demand parameter, a driver demand parameter, a controller demand parameter and a sensor demand parameter according to the three-dimensional space parameter and the mechanical model of the target application scene;
the preset model acquisition module is used for acquiring a preset transmission system model, deconstructing the preset transmission system model and extracting the model parameters of the transmission system model, wherein the model parameters comprise preset speed reduction motor parameters, preset driver parameters, preset controller parameters and preset sensor parameters, and the preset model acquisition module comprises the following steps: acquiring a preset transmission system model, wherein the transmission system model is pre-stored in a form of a knowledge graph, and the knowledge graph comprises nodes and a relation vector arrow; deconstructing the preset transmission system model to obtain the knowledge graph; extracting to obtain preset speed reducing motor parameters, preset driver parameters, preset controller parameters and preset sensor parameters according to the knowledge graph;
a model matching evaluation module for inputting the demand parameters and the model parameters into a trained demand adaptation evaluation model of the transmission system, or calculating according to a formula to obtain the model matching degree, wherein the transmission system demand adaptation evaluation model is constructed based on a neural network;
a judging module for judging whether the model matching degree is equal to or higher than a preset threshold value, determining that the drivetrain model is available for the application scenario; determining that the transmission system model cannot be used in the application scene according to the fact that the model matching degree is lower than a preset threshold;
and the model parameter updating module is used for adjusting and updating the model parameters according to the condition that the model matching degree is lower than a preset threshold value, then repeatedly inputting the demand parameters and the model parameters into a trained transmission system demand adaptation evaluation model or calculating according to a formula to obtain the process of the model matching degree until the model matching degree is determined to be equal to or higher than the preset threshold value, and taking the adjusted transmission system model as an optimal solution.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method of any one of claims 1-5.
8. A computer-readable storage medium, on which a computer program is stored, characterized in that the program is executed by a processor to implement the method according to any of claims 1-5.
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