Use management method and device of intelligent grinding equipment, terminal and medium

Abstract

The invention discloses a use management method and device for intelligent grinding equipment, a terminal and a medium, and the method comprises the steps that first balance data is obtained based on cooling liquid information, grinding wheel information and machining workpiece information; second balance data are obtained based on the grinding wheel information, the grinding wheel control system information and the machining workpiece information; third balance data are obtained based on the grinding wheel control system information, the machine tool information and the machining workpiece information; and obtaining a target use rule based on the first balance data, the second balance data, the third balance data and the machining workpiece precision standard. The application aims to accurately provide the use rule of the intelligent grinding equipment, so that the processing quality of the intelligent grinding equipment is improved.

Description

Technical Field

[0001] This application relates to the technical field of grinding equipment management, and particularly to a method, device, terminal, and medium for the use management of intelligent grinding equipment. Background Art

[0002] Intelligent grinding equipment is a modern processing equipment that integrates advanced sensor technology, automation control technology, artificial intelligence technology, and communication technology on the basis of traditional grinding equipment. It is equipped with a variety of high-precision sensors, such as force sensors, displacement sensors, temperature sensors, etc., which can real-time sense various physical quantities during the grinding process, such as grinding force, workpiece size change, grinding zone temperature, etc. Through the automation control system, the grinding parameters, such as the grinding wheel speed, feed speed, grinding depth, etc., can be automatically adjusted according to the preset parameters and real-time monitoring data to achieve efficient and precise grinding processing.

[0003] However, a large amount of data generated by intelligent grinding equipment comes from different types of sensors and systems, and the data formats and standards are not unified, resulting in difficulties in data integration. Under complex working conditions, the adaptive ability of intelligent grinding equipment is not strong enough. For example, when the workpiece material, shape, and processing requirements change, the equipment may not be able to accurately adjust the grinding parameters, and still requires high-level technical personnel to intervene and adjust, which further leads to the inability to guarantee the processing quality of the grinding equipment. Summary of the Invention

[0004] The main purpose of this application is to provide a method, device, terminal, and medium for the use management of intelligent grinding equipment, aiming to accurately propose the usage rules of intelligent grinding equipment, and thus improve the processing quality of intelligent grinding equipment.

[0005] To achieve the above object, this application provides a method for the use management of intelligent grinding equipment, the method includes: Obtain the coolant information of the intelligent grinding equipment, the grinding wheel information of the intelligent grinding equipment, the machine tool information of the intelligent grinding equipment, the grinding wheel control system information of the intelligent grinding equipment, the processing workpiece information corresponding to the intelligent grinding equipment, and the machining workpiece accuracy standard corresponding to the intelligent grinding equipment, wherein the processing workpiece information is used to characterize the accuracy status of the workpiece processed by the intelligent grinding equipment; Based on the coolant information, the grinding wheel information, and the processing workpiece information, obtain first balance data, wherein the first balance data is used to characterize the dynamic balance relationship between the coolant and the grinding wheel; Based on the grinding wheel information, the grinding wheel control system information, and the processing workpiece information, obtain second balance data, wherein the second balance data is used to characterize the dynamic balance relationship between the grinding wheel control system and the grinding wheel; Based on the information of the grinding wheel control system, the information of the machine tool, and the information of the workpiece to be machined, third balance data is obtained, where the third balance data is used to characterize the dynamic balance relationship between the grinding wheel control system and the machine tool; Based on the first balance data, the second balance data, the third balance data, and the accuracy standard of the workpiece to be machined, a target usage rule is obtained to ensure that the accuracy of the workpiece machined by the intelligent grinding equipment meets the accuracy standard of the workpiece to be machined.

[0006] Specifically, the obtaining of the first balance data based on the coolant information, the grinding wheel information, and the workpiece information includes: Through a preset first model, the first balance data is obtained according to the coolant information, the grinding wheel information, and the workpiece information.

[0007] Specifically, the preset first model includes a first input layer, a first hidden layer, and a first output layer. The first hidden layer includes a first sub-hidden layer and a second sub-hidden layer. The number of neurons in the first sub-hidden layer is twice the number of the second sub-hidden layer, and the first output layer contains one neuron; The obtaining of the first balance data through the preset first model according to the grinding wheel information and the workpiece information includes: Through the first input layer, a first input vector is obtained according to the coolant information, the grinding wheel information, and the workpiece information; Through the first sub-hidden layer, a first intermediate vector is obtained according to the first input vector; Through the second sub-hidden layer, a second intermediate vector is obtained according to the first intermediate vector; Through the first output layer, the first balance data is obtained according to the first intermediate vector.

[0008] Specifically, the obtaining of the second balance data based on the grinding wheel information, the grinding wheel control system information, and the workpiece information includes: Through a preset second model, the second balance data is obtained according to the grinding wheel information, the grinding wheel control system information, and the workpiece information, where the preset second model includes a second input layer, a second hidden layer, and a second output layer. The second hidden layer includes a third sub-hidden layer and a fourth sub-hidden layer. The number of neurons in the third sub-hidden layer is twice the number of the fourth sub-hidden layer, and the second output layer contains one neuron.

[0009] Specifically, the obtaining of the third balance data based on the grinding wheel control system information, the machine tool information, and the workpiece information includes: By presetting a third model, the third balance data is obtained according to the grinding wheel control system information, the machine tool information, and the workpiece information to be processed.

[0010] Specifically, the preset third model includes a long short-term memory network layer, a first dense layer, and a second dense layer, and one neuron is included in the second dense layer; The step of obtaining the third balance data by presetting a third model according to the grinding wheel control system information, the machine tool information, and the workpiece information to be processed includes: Through the long short-term memory network layer, a sequence processing vector is obtained according to the grinding wheel control system information, the machine tool information, and the workpiece information to be processed; Through the first dense layer, a third intermediate vector is obtained according to the sequence processing vector; Through the second dense layer, the third balance data is obtained according to the third intermediate vector.

[0011] Specifically, the step of obtaining the target usage rule based on the first balance data, the second balance data, the third balance data, and the workpiece precision standard includes: Performing normalization processing on the first balance data, the second balance data, the third balance data, and the workpiece precision standard to obtain a normalized data matrix; According to the normalized data matrix, an initial usage rule is obtained, and based on the initial usage rule, a chromosome is encoded and generated, where the initial usage rule and the chromosome correspond one by one; Through a support vector machine, the initial usage rule corresponding to each chromosome is evaluated respectively, and the fitness value corresponding to the initial usage rule is calculated, where the fitness value is used to characterize the degree to which the workpiece precision meets the workpiece precision standard when the initial usage rule is applied; Based on the chromosome, an initial population is randomly generated; Select chromosomes from the initial population whose fitness values meet the preset fitness value standard, and perform crossover operations and mutation operations on the chromosomes whose fitness values meet the preset fitness value standard to obtain the chromosomes after the operations; Repeat the step of selecting chromosomes from the initial population whose fitness values meet the preset fitness value standard, and performing crossover operations and mutation operations on the chromosomes whose fitness values meet the preset fitness value standard to obtain the chromosomes after the operations until the fitness value of the chromosomes after the operations no longer increases, and a target chromosome is obtained; Based on the target chromosome, the target usage rule is determined.

[0012] To achieve the above object, the present application further provides a usage management device for an intelligent grinding device, the device comprising: A first unit, configured to obtain coolant information of the intelligent grinding device, grinding wheel information of the intelligent grinding device, machine tool information of the intelligent grinding device, grinding wheel control system information of the intelligent grinding device, workpiece information corresponding to the intelligent grinding device, and machining workpiece precision standards corresponding to the intelligent grinding device, wherein the workpiece information is used to characterize the precision status of the workpiece processed by the intelligent grinding device; A second unit, configured to obtain first balance data based on the coolant information, the grinding wheel information, and the workpiece information, wherein the first balance data is used to characterize the dynamic balance relationship between the coolant and the grinding wheel; A third unit, configured to obtain second balance data based on the grinding wheel information, the grinding wheel control system information, and the workpiece information, wherein the second balance data is used to characterize the dynamic balance relationship between the grinding wheel control system and the grinding wheel; A fourth unit, configured to obtain third balance data based on the grinding wheel control system information, the machine tool information, and the workpiece information, wherein the third balance data is used to characterize the dynamic balance relationship between the grinding wheel control system and the machine tool; A fifth unit, configured to obtain a target usage rule based on the first balance data, the second balance data, the third balance data, and the machining workpiece precision standards, so as to ensure that the precision status of the workpiece processed by the intelligent grinding device meets the machining workpiece precision standards.

[0013] To achieve the above object, the present application further provides a terminal, comprising a memory storing multiple instructions; the processor loads the instructions from the memory to execute the steps in any one of the methods provided by the present application.

[0014] To achieve the above object, the present application further provides a medium, the medium storing multiple instructions, and the instructions are suitable for being loaded by a processor to execute the steps in any one of the methods provided by the present application.

[0015] A method, device, terminal, and medium for the use management of an intelligent grinding device provided by the present application may first obtain the coolant information of the intelligent grinding device, the grinding wheel information of the intelligent grinding device, the machine tool information of the intelligent grinding device, the grinding wheel control system information of the intelligent grinding device, the workpiece information corresponding to the intelligent grinding device, and the machining workpiece accuracy standard corresponding to the intelligent grinding device. Among them, the workpiece information is used to characterize the accuracy status of the workpiece processed by the intelligent grinding device. Then, based on the coolant information, the grinding wheel information, and the workpiece information, first balance data is obtained, where the first balance data is used to characterize the dynamic balance relationship between the coolant and the grinding wheel. Then, based on the grinding wheel information, the grinding wheel control system information, and the workpiece information, second balance data is obtained, where the second balance data is used to characterize the dynamic balance relationship between the grinding wheel control system and the grinding wheel. After that, based on the grinding wheel control system information, the machine tool information, and the workpiece information, third balance data is obtained, where the third balance data is used to characterize the dynamic balance relationship between the grinding wheel control system and the machine tool. Finally, based on the first balance data, the second balance data, the third balance data, and the machining workpiece accuracy standard, a target usage rule is obtained to ensure that the accuracy status of the workpiece processed by the intelligent grinding device meets the machining workpiece accuracy standard, and then the processing quality of the intelligent grinding device is improved by implementing the target usage rule. Description of the Drawings

[0016] Figure 1 It is a schematic flowchart of the method provided by the embodiment of the present application; Figure 2 It is a schematic structural diagram of the device provided by the embodiment of the present application; Figure 3 It is a schematic structural diagram of the terminal provided by the embodiment of the present application; Description of the Drawings: 200 - Use management device of the intelligent grinding device, 201 - First unit, 202 - Second unit, 203 - Third unit, 204 - Fourth unit, 205 - Fifth unit, 300 - Terminal, 301 - Processor, 302 - Memory, 303 - Power supply, 304 - Input module, 305 - Communication module. Detailed Embodiments

[0017] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0018] Since a large amount of data generated by intelligent grinding equipment comes from different types of sensors and systems, the data formats and standards are not unified, resulting in difficulties in data integration. Under complex working conditions, the adaptive ability of intelligent grinding equipment is not strong enough. For example, when the workpiece material, shape, and processing requirements change, the equipment may not be able to accurately adjust the grinding parameters, and high-level technical personnel are still required to intervene and adjust, thus resulting in the inability to guarantee the processing quality of the grinding equipment.

[0019] Therefore, the embodiments of the present application provide a usage management method, device, terminal, and medium for intelligent grinding equipment to solve actual technical problems.

[0020] In some embodiments, the device may be specifically integrated in an electronic device, and the electronic device may be a device such as a terminal or a server.

[0021] In some embodiments, the server may also be implemented in the form of a terminal.

[0022] Among them, the server may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0023] Among them, the terminal may be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal and the server may be directly or indirectly connected through wired or wireless communication methods, and the present application does not limit this.

[0024] The following will be described in detail respectively. It should be noted that the serial numbers of the following embodiments do not limit the preferred order of the embodiments.

[0025] The embodiments of the present application provide a usage management method for intelligent grinding equipment, as Figure 1 ., the specific process of the method may be as follows: S110. Obtain the coolant information of the intelligent grinding equipment, the grinding wheel information of the intelligent grinding equipment, the machine tool information of the intelligent grinding equipment, the grinding wheel control system information of the intelligent grinding equipment, the machining workpiece information corresponding to the intelligent grinding equipment, and the machining workpiece accuracy standard corresponding to the intelligent grinding equipment, where the machining workpiece information is used to characterize the accuracy status of the workpiece processed by the intelligent grinding equipment.

[0026] In some embodiments, the coolant information may include the following contents: Temperature: The temperature of the coolant affects its cooling effect and chemical stability. Excessive temperature may lead to a decline in the performance of the coolant, making it unable to effectively carry away the heat generated during grinding, thereby affecting the surface quality of the workpiece and the service life of the grinding wheel. For example, when the coolant temperature exceeds a certain threshold, it may cause thermal deformation of the workpiece and reduce the machining accuracy. Concentration: Coolant is usually a mixture of a base fluid and additives, and its concentration has a significant impact on lubrication and rust prevention performance. Too high a concentration may increase costs and lead to a decline in cleaning performance, while too low a concentration may not provide sufficient lubrication and rust prevention protection, accelerating the wear of the grinding wheel and the rusting of the workpiece. Liquid level: The liquid level of the coolant needs to be maintained within an appropriate range. Too low a liquid level may result in insufficient coolant supply, affecting the cooling and lubrication effects; too high a liquid level may cause overflow, wasting coolant and polluting the working environment. pH value: The pH value of the coolant affects its corrosiveness to metals and the growth of microorganisms. Generally speaking, an appropriate pH value range can prevent the workpiece and equipment from rusting and inhibit the growth of microorganisms, ensuring the service life of the coolant. Additive content: The content of additives such as preservatives and defoamers in the coolant affects its various performances. For example, insufficient content of preservatives may cause the coolant to deteriorate easily, and inappropriate content of defoamers may generate excessive foam, affecting the cooling and lubrication effects.

[0027] In some embodiments, the grinding wheel information may include the following: Grain size: It refers to the size of the abrasive grains. The finer the grain size, the smoother the grinding surface, but the relatively lower the grinding efficiency; the coarser the grain size, the higher the grinding efficiency, but the larger the surface roughness. Different processing requirements require the selection of a grinding wheel with an appropriate grain size. Hardness: It reflects the ease with which the abrasive grains of the grinding wheel fall off under the action of the grinding force. If the hardness is too high, the abrasive grains are not easy to fall off after wear, which will lead to an increase in the grinding force and burning of the workpiece surface; if the hardness is too low, the abrasive grains are easy to fall off, and the grinding wheel wears quickly, affecting the machining accuracy. Binder type: Common binders include ceramics, resins, rubbers, etc. Grinding wheels with different binders have different performance characteristics. For example, ceramic-bonded grinding wheels have high heat resistance and chemical stability and are suitable for high-speed grinding; resin-bonded grinding wheels have good elasticity and are suitable for grinding thin-walled parts and profile grinding. Wear amount: It includes the radial wear and axial wear of the grinding wheel. Excessive wear amount may cause the outer diameter of the grinding wheel to become smaller and the thickness to become thinner, affecting the machining dimensional accuracy and surface quality. Wear form: Such as abrasive grain wear, binder wear, grinding wheel clogging, etc. Different wear forms will have different effects on the grinding process. For example, grinding wheel clogging will lead to an increase in the grinding force, an increase in the grinding temperature, and a reduction in the machining quality.

[0028] In some embodiments, the machine tool information may include the following: Guideway accuracy: Accuracy indicators such as the straightness and parallelism of the guideway affect the movement accuracy of the worktable, which in turn affects the machining accuracy of the workpiece. Low guideway accuracy may result in straightness errors, flatness errors, etc. on the workpiece surface; Spindle accuracy: Parameters such as the rotational accuracy, radial runout, and axial runout of the spindle have an important impact on grinding accuracy. Insufficient spindle accuracy may cause roundness errors, cylindricity errors, etc. on the workpiece surface.

[0029] In some embodiments, the grinding wheel control system information may include the following: Grinding wheel speed control: Precise control of the grinding wheel speed is crucial for ensuring grinding quality and efficiency. Different workpiece materials and processing requirements require different grinding wheel speeds. Excessive speed may lead to increased grinding wheel wear, while too low speed may affect grinding efficiency; Feed speed control: Includes the radial feed speed and axial feed speed of the grinding wheel. Too fast a feed speed may cause an increase in grinding force and workpiece surface burn; too slow a feed speed will reduce processing efficiency.

[0030] In some embodiments, the processed workpiece information may include the following: Shape accuracy: Such as roundness, cylindricity, flatness, etc. Shape accuracy affects the service performance and assembly accuracy of the workpiece. For example, excessive cylindricity error may cause vibration during the rotation of the workpiece; Surface roughness: Refers to the microscopic geometric shape error of the workpiece surface. The smaller the surface roughness value, the smoother the surface. Appropriate surface roughness can improve the wear resistance, corrosion resistance, and mating accuracy of the workpiece; Surface defects: Such as cracks, scratches, burns, etc. Surface defects will reduce the strength and service life of the workpiece and affect product quality.

[0031] In some embodiments, the processed workpiece accuracy standards may include the following: Dimensional tolerance standard: Specifies the allowable deviation range for each dimension of the workpiece. For example, for a shaft with a diameter of φ50mm, its dimensional tolerance may be specified as φ50±0.05mm. As long as the diameter of the processed shaft is within this range, the dimensional accuracy is considered to meet the standard; Shape and position tolerance standards: Include the allowable deviation ranges of shape tolerances (such as roundness, cylindricity, etc.) and position tolerances (such as coaxiality, perpendicularity, etc.). These standards ensure that the shape and position accuracy of the workpiece meet the design requirements and guarantee the performance of the workpiece during assembly and use; Surface quality standards: Requirements for aspects such as the surface roughness and surface defects of workpieces. For example, some precision parts may require a surface roughness of less than Ra0.8μm and no cracks or scratches are allowed.

[0032] S120. Based on the coolant information, the grinding wheel information, and the workpiece information being processed, obtain first balance data, where the first balance data is used to characterize the dynamic balance relationship between the coolant and the grinding wheel.

[0033] In some embodiments, the obtaining of the first balance data based on the coolant information, the grinding wheel information, and the workpiece information being processed includes: Through a preset first model, obtain the first balance data according to the coolant information, the grinding wheel information, and the workpiece information being processed.

[0034] Specifically, the preset first model includes a first input layer, a first hidden layer, and a first output layer. The first hidden layer includes a first sub-hidden layer and a second sub-hidden layer. The number of neurons in the first sub-hidden layer is twice the number of the second sub-hidden layer, and the first output layer contains one neuron.

[0035] The obtaining of the first balance data through the preset first model according to the grinding wheel information and the workpiece information being processed includes the step contents of S121 to S124 shown below: S121. Through the first input layer, obtain a first input vector according to the coolant information, the grinding wheel information, and the workpiece information being processed.

[0036] S122. Through the first sub-hidden layer, obtain a first intermediate vector according to the first input vector.

[0037] S123. Through the second sub-hidden layer, obtain a second intermediate vector according to the first intermediate vector.

[0038] S124. Through the first output layer, obtain the first balance data according to the first intermediate vector.

[0039] Specifically, for the first input layer, assuming there are n1 features in the coolant information, n2 features in the grinding wheel information, and n3 features in the workpiece information being processed, then the dimension of the first input vector of the first input layer is n1 + n2 + n3.

[0040] Continuing with the above-described embodiment, the first sub-hidden layer may be a fully connected layer, including 64 neurons, and using ReLU (Rectified Linear Unit) as the activation function. A fully connected layer means that each neuron in this layer is connected to all neurons in the first input layer. There is a corresponding weight between each input neuron of the first input vector and the 64 neurons in this hidden layer. Each neuron in the first sub-hidden layer will perform a weighted sum of the inputs, and then perform a non-linear transformation through the ReLU activation function, finally outputting 64 values, so the output dimension is 64, and thus the first intermediate vector is obtained.

[0041] Continuing with the above-described embodiment, the second sub-hidden layer is also a fully connected layer, including 32 neurons, and also using ReLU as the activation function. The second sub-hidden layer further extracts and transforms the features output by the first sub-hidden layer to mine more advanced feature representations. The input of the second sub-hidden layer comes from the output of the first hidden layer, and the input dimension is 64. There are weight connections between each input value and the 32 neurons in this hidden layer. After the 32 neurons in the second sub-hidden layer perform a weighted sum of the inputs and are processed by the ReLU activation function, the output dimension is 32, and thus the second intermediate vector is obtained.

[0042] Continuing with the above-described embodiment, the first output layer is a fully connected layer, including 1 neuron, and no activation function is used (i.e., a linear activation function is used). Since the goal is to predict the first balance data, which is a continuous numerical value, using a linear activation function can directly output the prediction result. The input of the first output layer comes from the output of the second hidden layer, and the input dimension is 32. There are weight connections between each input value and the 1 neuron in the first output layer. After the neuron in the first output layer performs a weighted sum of the inputs, it directly outputs a value, and this value is the predicted first balance data, and the output dimension is 1.

[0043] S130. Based on the grinding wheel information, the grinding wheel control system information, and the workpiece information to be processed, obtain second balance data, where the second balance data is used to characterize the dynamic balance relationship between the grinding wheel control system and the grinding wheel.

[0044] Continuing with the above-described embodiment, the obtaining of the second balance data based on the grinding wheel information, the grinding wheel control system information, and the workpiece information to be processed includes: By presetting a second model, the second balance data is obtained according to the grinding wheel information, the grinding wheel control system information, and the workpiece information to be machined. Wherein, the preset second model includes a second input layer, a second hidden layer, and a second output layer. The second hidden layer includes a third sub-hidden layer and a fourth sub-hidden layer. The number of neurons in the third sub-hidden layer is twice the number of the fourth sub-hidden layer. The second output layer includes one neuron.

[0045] Specifically, the network structure of the preset second model is similar to that of the preset first model, which will not be elaborated here.

[0046] S140. Based on the grinding wheel control system information, the machine tool information, and the workpiece information to be machined, the third balance data is obtained, where the third balance data is used to characterize the dynamic balance relationship between the grinding wheel control system and the machine tool.

[0047] In some embodiments, the obtaining of the third balance data based on the grinding wheel control system information, the machine tool information, and the workpiece information to be machined includes: By presetting a third model, the third balance data is obtained according to the grinding wheel control system information, the machine tool information, and the workpiece information to be machined.

[0048] Specifically, the preset third model includes a long short-term memory network layer, a first dense layer, and a second dense layer. The second dense layer includes one neuron. The obtaining of the third balance data by presetting a third model according to the grinding wheel control system information, the machine tool information, and the workpiece information to be machined includes the step contents of S141 to S143 as shown below: S141. Through the long short-term memory network layer, a sequence processing vector is obtained according to the grinding wheel control system information, the machine tool information, and the workpiece information to be machined.

[0049] S142. Through the first dense layer, a third intermediate vector is obtained according to the sequence processing vector.

[0050] S143. Through the second dense layer, the third balance data is obtained according to the third intermediate vector.

[0051] Continuing with the above embodiments, the long short-term memory network layer can be an LSTM layer. The LSTM layer can contain 64 LSTM units. The LSTM units can learn the long-term dependencies in the sequence data and control the flow and memory of information through a gating mechanism (input gate, forget gate, output gate). The input dimension of the long short-term memory network layer is (number of samples, time steps, number of features). The output of the long short-term memory network layer is the hidden state of the last time step of each sample, and the output dimension is (number of samples, 64), which is the sequence processing vector.

[0052] Continuing with the above embodiments, the first dense layer can be a fully connected layer containing 32 neurons, using ReLU as the activation function to further extract and transform the output of the LSTM layer. The input of the first dense layer comes from the output of the LSTM layer, and the input dimension is (number of samples, 64), which is the sequence processing vector. After the 32 neurons of the first dense layer perform weighted summation on the input and are processed by the ReLU activation function, the output dimension is (number of samples, 32), which is the third intermediate vector.

[0053] Continuing with the above embodiments, the second dense layer can be a fully connected layer containing 1 neuron, without using an activation function (i.e., using a linear activation function). It is used to output the predicted third balance data. The input of the second dense layer comes from the output of the first fully connected layer, and the input dimension is (number of samples, 32), which is the third intermediate vector. After the neuron of the second dense layer performs weighted summation on the input, it directly outputs a value, and the output dimension is (number of samples, 1), which is the third balance data.

[0054] S150. Based on the first balance data, the second balance data, the third balance data, and the machining workpiece accuracy standard, obtain the target usage rule to ensure that the accuracy status of the workpiece processed by the intelligent grinding equipment meets the machining workpiece accuracy standard.

[0055] In some embodiments, the obtaining the target usage rule based on the first balance data, the second balance data, the third balance data, and the machining workpiece accuracy standard includes the step contents of S151 to S157 as follows: S151. Perform normalization processing on the first balance data, the second balance data, the third balance data, and the machining workpiece accuracy standard to obtain a normalized data matrix.

[0056] In some embodiments, the Min - Max normalization method is used to normalize the first balance data, the second balance data, the third balance data, and the machining workpiece accuracy standard, and scale the data to the interval [0, 1]. For example, for the coolant temperature in the first balance data, assuming its original value range is [20°C, 60°C], after normalization, the temperature value will be mapped between [0, 1]. The same applies to other data for similar normalization processing.

[0057] S152. According to the normalized data matrix, obtain the initial usage rules, and based on the initial usage rules, encode and generate chromosomes, where the initial usage rules and the chromosomes are in one - to - one correspondence.

[0058] Continuing the above - mentioned embodiments, according to the normalized data matrix, some simple initial usage rules can be formulated. For example, Rule 1: If the coolant temperature (after normalization) is less than 0.3 and the grinding wheel speed (after normalization) is greater than 0.7, it is considered that the machining accuracy standard may be met. Encode these rules into chromosomes, with each rule corresponding to one chromosome. Suppose 50 initial usage rules are formulated, and each rule is represented by a binary vector of length 12. Each element in the vector corresponds to a feature in the normalized data matrix, where 1 indicates that the feature participates in the rule judgment and 0 indicates non - participation.

[0059] S153. Through the support vector machine, evaluate the initial usage rules corresponding to each chromosome respectively, and calculate the fitness value corresponding to the initial usage rule, where the fitness value is used to characterize the degree to which the workpiece accuracy meets the machining workpiece accuracy standard when the initial usage rule is applied.

[0060] Continuing the above - mentioned embodiments, use the support vector machine (SVM) to evaluate the initial usage rules corresponding to each chromosome. For each chromosome, determine the features participating in the rule judgment according to its encoding, extract the corresponding data, train the SVM model and calculate its accuracy on the training data, and take the accuracy as the fitness value of the rule.

[0061] S154. Based on the chromosomes, randomly generate an initial population.

[0062] Continuing the above - mentioned embodiments, for example, the initial population can contain 50 chromosomes.

[0063] S155. Select the chromosomes whose fitness values meet the preset fitness value standard from the initial population, and perform crossover operations and mutation operations on the chromosomes whose fitness values meet the preset fitness value standard to obtain the chromosomes after the operations.

[0064] Continuing with the above embodiment, chromosomes with fitness values meeting a preset fitness value criterion (for example, the fitness value is greater than 0.8) can be selected from the initial population. Perform crossover operations on these chromosomes, such as randomly selecting crossover points and exchanging some genes of two chromosomes; perform mutation operations, and randomly change a certain gene in the chromosome with a certain probability (such as 0.01).

[0065] S156. Repeat the step of selecting chromosomes with fitness values meeting the preset fitness value criterion from the initial population, and perform crossover operations and mutation operations on the chromosomes with fitness values meeting the preset fitness value criterion to obtain the corresponding steps of the chromosomes after the operations, until the fitness value of the chromosomes after the operations no longer increases, and obtain the target chromosome.

[0066] Continuing with the above embodiment, repeat step S155 until the fitness value of the chromosomes after the operations no longer increases. In each iteration, re-evaluate the fitness values of the newly generated chromosomes, select chromosomes with high fitness values for the next round of crossover and mutation operations until the fitness value of the chromosomes after the operations no longer increases.

[0067] S157. Determine the target usage rule based on the target chromosome.

[0068] Continuing with the above embodiment, according to the target chromosome, determine the features participating in the rule judgment, and combine with the SVM model to obtain the target usage rule. For example, the 2nd, 5th, and 8th bits in the target chromosome are 1, indicating that the three features of coolant temperature, rotational speed control accuracy of the grinding wheel control system, and guide rail accuracy of the machine tool participate in the rule judgment. By training the SVM model corresponding to these three features, a target usage rule based on these three features is obtained, which is used to guide the use of the intelligent grinding equipment to ensure that the workpiece accuracy meets the machining workpiece accuracy standard.

[0069] In summary, the present application provides a method for managing the use of an intelligent grinding equipment, accurately proposes the usage rules of the intelligent grinding equipment, and thereby improves the machining quality of the intelligent grinding equipment.

[0070] To better implement the above method, the embodiment of the present application further provides a device for managing the use of an intelligent grinding equipment. This device can be specifically integrated in an electronic device, and the electronic device can be a terminal, a server, or other devices. Among them, the terminal can be a mobile phone, a tablet computer, an intelligent Bluetooth device, a notebook computer, a personal computer, or other devices; the server can be a single server or a server cluster composed of multiple servers.

[0071] For example, in this embodiment, taking the device for managing the use of an intelligent grinding equipment being specifically integrated in a terminal as an example, the method of the embodiment of the present application will be described in detail.

[0072] For example, as Figure 2 shown, the usage management device 200 of the intelligent grinding equipment may include a first unit 201, a second unit 202, a third unit 203, a fourth unit 204, and a fifth unit 205. The device includes: A first unit, configured to obtain coolant information of the intelligent grinding equipment, grinding wheel information of the intelligent grinding equipment, machine tool information of the intelligent grinding equipment, grinding wheel control system information of the intelligent grinding equipment, workpiece information corresponding to the intelligent grinding equipment, and workpiece precision standards corresponding to the intelligent grinding equipment. Among them, the workpiece information is used to characterize the precision status of the workpiece processed by the intelligent grinding equipment; A second unit, configured to obtain first balance data based on the coolant information, the grinding wheel information, and the workpiece information. Among them, the first balance data is used to characterize the dynamic balance relationship between the coolant and the grinding wheel; A third unit, configured to obtain second balance data based on the grinding wheel information, the grinding wheel control system information, and the workpiece information. Among them, the second balance data is used to characterize the dynamic balance relationship between the grinding wheel control system and the grinding wheel; A fourth unit, configured to obtain third balance data based on the grinding wheel control system information, the machine tool information, and the workpiece information. Among them, the third balance data is used to characterize the dynamic balance relationship between the grinding wheel control system and the machine tool; A fifth unit, configured to obtain a target usage rule based on the first balance data, the second balance data, the third balance data, and the workpiece precision standard, so as to ensure that the precision status of the workpiece processed by the intelligent grinding equipment meets the workpiece precision standard.

[0073] In specific implementation, each of the above units may be implemented as an independent entity, or may be combined arbitrarily to be implemented as the same or several entities. For the specific implementation of each of the above units, reference may be made to the foregoing method embodiments, which will not be elaborated herein.

[0074] As can be seen from the above, the embodiments of the present application can accurately propose the usage rules of the intelligent grinding equipment, thereby improving the processing quality of the intelligent grinding equipment.

[0075] The embodiments of the present application further provide an electronic device, which may be a device such as a terminal or a server. Among them, the terminal may be a mobile phone, a tablet computer, a smart Bluetooth device, a notebook computer, a personal computer, etc.; the server may be a single server or a server cluster composed of multiple servers, etc.

[0076] In some embodiments, the product processing device may also be integrated in multiple electronic devices. For example, the product processing device may be integrated in multiple servers, and the multiple servers are used to implement the usage management method of the intelligent grinding device of the present application.

[0077] In this embodiment, the electronic device in this embodiment will be described in detail by taking the terminal as an example. For example, as Figure 3 shown, it shows a schematic structural diagram of the terminal 300 involved in the embodiment of the present application. Specifically: The terminal 300 may include a processor 301 with one or more processing cores, a memory 302 of one or more media, a power supply 303, an input module 304, a communication module 305 and other components. Those skilled in the art can understand that Figure 3 the structure of the terminal 300 shown in does not constitute a limitation on the terminal 300, and it may include more or fewer components than shown, or combine certain components, or have different component arrangements. Among them: The processor 301 is the processor of the terminal 300, and uses various interfaces and lines to connect various parts of the entire terminal 300. By running or executing the software programs and / or modules stored in the memory 302, and calling the data stored in the memory 302, it executes various functions of the terminal 300 and processes data, thereby monitoring the terminal 300 as a whole. In some embodiments, the processor 301 may include one or more processing cores; in some embodiments, the processor 301 may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, user interface, application programs, etc., and the modem processor mainly processes wireless communication. It can be understood that the above modem processor may not be integrated into the processor 301 either.

[0078] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and data processing by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. Among them, the program storage area may store the operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created according to the use of the terminal 300. In addition, the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices. Correspondingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0079] The terminal 300 further includes a power supply 303 for powering each component. In some embodiments, the power supply 303 can be logically connected to the processor 301 through a power management system, so as to manage functions such as charging, discharging, and power consumption management through the power management system. The power supply 303 can also include any components such as one or more DC or AC power supplies, a recharge system, a power failure detection circuit, a power converter or inverter, and a power status indicator.

[0080] The terminal 300 may further include an input module 304, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0081] The terminal 300 may further include a communication module 305. In some embodiments, the communication module 305 can include a wireless module. The terminal 300 can perform short-distance wireless transmission through the wireless module of the communication module 305, thereby providing users with wireless broadband Internet access. For example, the communication module 305 can be used to help users send and receive emails, browse web pages, and access streaming media, etc.

[0082] Although not shown, the terminal 300 may further include a display unit, etc., which will not be elaborated here. Specifically, in this embodiment, the processor 301 in the terminal 300 will load the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 will run the application programs stored in the memory 302 to implement various functions as follows: Obtain the coolant information of the intelligent grinding equipment, the grinding wheel information of the intelligent grinding equipment, the machine tool information of the intelligent grinding equipment, the grinding wheel control system information of the intelligent grinding equipment, the workpiece information corresponding to the intelligent grinding equipment, and the machining workpiece accuracy standard corresponding to the intelligent grinding equipment, wherein the workpiece information is used to characterize the accuracy status of the workpiece processed by the intelligent grinding equipment; Based on the coolant information, the grinding wheel information, and the workpiece information, obtain first balance data, wherein the first balance data is used to characterize the dynamic balance relationship between the coolant and the grinding wheel; Based on the grinding wheel information, the grinding wheel control system information, and the workpiece information, obtain second balance data, wherein the second balance data is used to characterize the dynamic balance relationship between the grinding wheel control system and the grinding wheel; Based on the grinding wheel control system information, the machine tool information, and the workpiece information, obtain third balance data, wherein the third balance data is used to characterize the dynamic balance relationship between the grinding wheel control system and the machine tool; Based on the first balance data, the second balance data, the third balance data, and the machining workpiece precision standard, a target usage rule is obtained to ensure that the precision status of the workpiece machined by the intelligent grinding equipment meets the machining workpiece precision standard.

[0083] For the specific implementation of each of the above operations, reference can be made to the previous embodiments, which will not be elaborated here.

[0084] As can be seen from the above, the embodiments of the present application can accurately propose the usage rules of the intelligent grinding equipment, thereby improving the machining quality of the intelligent grinding equipment.

[0085] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by instructions controlling related hardware. The instructions can be stored in a medium and loaded and executed by a processor.

[0086] For this reason, the embodiments of the present application provide a medium in which multiple instructions are stored. The instructions can be loaded by a processor to execute the steps in any of the usage management methods of the intelligent grinding equipment provided by the embodiments of the present application. For example, the instructions can execute the following steps: Obtain the coolant information of the intelligent grinding equipment, the grinding wheel information of the intelligent grinding equipment, the machine tool information of the intelligent grinding equipment, the grinding wheel control system information of the intelligent grinding equipment, the machining workpiece information corresponding to the intelligent grinding equipment, and the machining workpiece precision standard corresponding to the intelligent grinding equipment, where the machining workpiece information is used to characterize the precision status of the workpiece machined by the intelligent grinding equipment; Based on the coolant information, the grinding wheel information, and the machining workpiece information, first balance data is obtained, where the first balance data is used to characterize the dynamic balance relationship between the coolant and the grinding wheel; Based on the grinding wheel information, the grinding wheel control system information, and the machining workpiece information, second balance data is obtained, where the second balance data is used to characterize the dynamic balance relationship between the grinding wheel control system and the grinding wheel; Based on the grinding wheel control system information, the machine tool information, and the machining workpiece information, third balance data is obtained, where the third balance data is used to characterize the dynamic balance relationship between the grinding wheel control system and the machine tool; Based on the first balance data, the second balance data, the third balance data, and the machining workpiece precision standard, a target usage rule is obtained to ensure that the precision status of the workpiece machined by the intelligent grinding equipment meets the machining workpiece precision standard.

[0087] Among them, the medium may include: read-only memory (ROM, Read Only Memory), random access memory (RAM), magnetic disk, optical disc, etc.

[0088] According to one aspect of the present application, there is provided a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a medium. A processor of a computer device reads the computer instructions from the medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the various alternative implementations provided in the above embodiments.

[0089] Since the instructions stored in the medium can execute the steps in any of the usage management methods of the intelligent grinding device provided in the embodiments of the present application, the beneficial effects achievable by any of the usage management methods of the intelligent grinding device provided in the embodiments of the present application can be achieved. For details, see the previous embodiments and will not be elaborated here.

[0090] The above has introduced in detail a usage management method, device, terminal and medium of an intelligent grinding device provided in the embodiments of the present application. Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for the use and management of an intelligent grinding device, characterized in that, The method includes: Obtaining coolant information of the intelligent grinding equipment, grinding wheel information of the intelligent grinding equipment, machine tool information of the intelligent grinding equipment, grinding wheel control system information of the intelligent grinding equipment, workpiece information corresponding to the intelligent grinding equipment, and machining workpiece precision standards corresponding to the intelligent grinding equipment, wherein the workpiece information is used to characterize the precision status of the workpiece processed by the intelligent grinding equipment; Based on the coolant information, the grinding wheel information, and the workpiece information, obtaining first balance data, wherein the first balance data is used to characterize the dynamic balance relationship between the coolant and the grinding wheel; Based on the grinding wheel information, the grinding wheel control system information, and the workpiece information, obtaining second balance data, wherein the second balance data is used to characterize the dynamic balance relationship between the grinding wheel control system and the grinding wheel; Based on the grinding wheel control system information, the machine tool information, and the workpiece information, obtaining third balance data, wherein the third balance data is used to characterize the dynamic balance relationship between the grinding wheel control system and the machine tool; Based on the first balance data, the second balance data, the third balance data, and the machining workpiece precision standards, obtaining target usage rules to ensure that the precision status of the workpiece processed by the intelligent grinding equipment meets the machining workpiece precision standards.

2. The method according to claim 1, wherein The obtaining the first balance data based on the coolant information, the grinding wheel information, and the workpiece information includes: Obtaining the first balance data according to the coolant information, the grinding wheel information, and the workpiece information through a preset first model.

3. The method according to claim 2, wherein The preset first model includes a first input layer, a first hidden layer, and a first output layer. The first hidden layer includes a first sub-hidden layer and a second sub-hidden layer. The number of neurons in the first sub-hidden layer is twice the number of the second sub-hidden layer. The first output layer includes one neuron; The obtaining the first balance data according to the grinding wheel information and the workpiece information through the preset first model includes: Obtaining a first input vector according to the coolant information, the grinding wheel information, and the workpiece information through the first input layer; Obtaining a first intermediate vector according to the first input vector through the first sub-hidden layer; Obtaining a second intermediate vector according to the first intermediate vector through the second sub-hidden layer; Obtaining the first balance data according to the first intermediate vector through the first output layer.

4. The method according to claim 1, characterized in that The obtaining the second balance data based on the grinding wheel information, the grinding wheel control system information, and the workpiece information includes: Obtaining the second balance data according to the grinding wheel information, the grinding wheel control system information, and the workpiece information through a preset second model, wherein the preset second model includes a second input layer, a second hidden layer, and a second output layer. The second hidden layer includes a third sub-hidden layer and a fourth sub-hidden layer. The number of neurons in the third sub-hidden layer is twice the number of the fourth sub-hidden layer. The second output layer includes one neuron.

5. The method according to claim 1, wherein Obtaining third balance data based on the information of the grinding wheel control system, the information of the machine tool, and the information of the workpiece to be machined, includes: Obtaining the third balance data according to the information of the grinding wheel control system, the information of the machine tool, and the information of the workpiece to be machined through a preset third model.

6. The method according to claim 5, wherein The preset third model includes a long short-term memory network layer, a first dense layer, and a second dense layer, and a neuron is included in the second dense layer; The obtaining the third balance data according to the information of the grinding wheel control system, the information of the machine tool, and the information of the workpiece to be machined through a preset third model, includes: Obtaining a sequence processing vector according to the information of the grinding wheel control system, the information of the machine tool, and the information of the workpiece to be machined through the long short-term memory network layer; Obtaining a third intermediate vector according to the sequence processing vector through the first dense layer; Obtaining the third balance data according to the third intermediate vector through the second dense layer.

7. The method according to claim 1, wherein Obtaining a target usage rule based on the first balance data, the second balance data, the third balance data, and the accuracy standard of the workpiece to be machined, includes: Performing normalization processing on the first balance data, the second balance data, the third balance data, and the accuracy standard of the workpiece to be machined to obtain a normalized data matrix; Obtaining an initial usage rule according to the normalized data matrix, and encoding to generate a chromosome based on the initial usage rule, wherein the initial usage rule and the chromosome are in one-to-one correspondence; Evaluating the initial usage rule corresponding to each chromosome respectively through a support vector machine, and calculating a fitness value corresponding to the initial usage rule, wherein the fitness value is used to characterize the degree to which the workpiece accuracy meets the accuracy standard of the workpiece to be machined when the initial usage rule is applied; Randomly generating an initial population based on the chromosome; Selecting chromosomes with fitness values meeting a preset fitness value standard from the initial population, and performing crossover operation and mutation operation on the chromosomes with fitness values meeting the preset fitness value standard to obtain the chromosomes after the operation; Repeating the step of selecting chromosomes with fitness values meeting a preset fitness value standard from the initial population, and performing crossover operation and mutation operation on the chromosomes with fitness values meeting the preset fitness value standard to obtain the chromosomes after the operation until the fitness value of the chromosomes after the operation no longer increases, to obtain a target chromosome; Determining the target usage rule based on the target chromosome.

8. An operation management device for an intelligent grinding equipment, characterized in that, The device includes: A first unit, configured to obtain the coolant information of the intelligent grinding device, the grinding wheel information of the intelligent grinding device, the machine tool information of the intelligent grinding device, the grinding wheel control system information of the intelligent grinding device, the workpiece information corresponding to the intelligent grinding device, and the accuracy standard of the workpiece corresponding to the intelligent grinding device, wherein the workpiece information is used to characterize the accuracy status of the workpiece machined by the intelligent grinding device; A second unit, configured to obtain first balance data based on the coolant information, the grinding wheel information, and the workpiece information, wherein the first balance data is used to characterize the dynamic balance relationship between the coolant and the grinding wheel; A third unit, configured to obtain second balance data based on the grinding wheel information, the grinding wheel control system information, and the workpiece information, wherein the second balance data is used to characterize the dynamic balance relationship between the grinding wheel control system and the grinding wheel; A fourth unit, configured to obtain third balance data based on the grinding wheel control system information, the machine tool information, and the workpiece information, wherein the third balance data is used to characterize the dynamic balance relationship between the grinding wheel control system and the machine tool; A fifth unit, configured to obtain a target usage rule based on the first balance data, the second balance data, the third balance data, and the workpiece precision standard, so as to ensure that the precision condition of the workpiece processed by the intelligent grinding device meets the workpiece precision standard.

9. A terminal, characterized in that, It includes a processor and a memory, and the memory stores multiple instructions; the processor loads the instructions from the memory to execute the steps in the method according to any one of claims 1 to 7.

10. A medium, characterized in that, The medium stores multiple instructions, and the instructions are suitable for being loaded by the processor to execute the steps in the method according to any one of claims 1 to 7.

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