Heat treatment methods, apparatus, storage media and computer equipment for magnetic materials

Abstract

This invention discloses a heat treatment method, apparatus, storage medium, and computer equipment for magnetic materials, relating to the field of vacuum sintering technology. Its main purpose is to improve the special treatment effect and heat treatment efficiency of magnetic materials. The method includes: responding to a heat treatment signal from a target magnetic material; acquiring material property information, heat treatment requirement information, and furnace property information of the target magnetic material; determining the material feature vector of the material property information, the requirement feature vector of the heat treatment requirement information, and the furnace feature vector of the furnace property information; fusing the material feature vector, requirement feature vector, and furnace feature vector to obtain a fused heat treatment feature vector; inputting the fused heat treatment feature vector into a preset treatment mode prediction model to predict the heat treatment mode, thereby obtaining the target heat treatment mode; and controlling the target heat treatment furnace to perform heat treatment on the target magnetic material based on the target heat treatment mode.

Description

Technical Field

[0001] This invention relates to the field of vacuum sintering technology, and in particular to a heat treatment method, apparatus, storage medium, and computer equipment for magnetic materials. Background Technology

[0002] Heat treatment can alter the magnetic structure of magnetic materials, such as the arrangement of magnetic domains and the direction of magnetic moments, thereby increasing the magnetization and saturation magnetic induction of the magnetic materials and optimizing their magnetic properties to make them perform better in specific applications.

[0003] Currently, the heat treatment process for magnetic materials is typically performed manually. However, this method of manually setting the heat treatment process is greatly affected by subjective human factors, which can lead to poor heat treatment results and low heat treatment efficiency. Summary of the Invention

[0004] This invention provides a heat treatment method, apparatus, storage medium, and computer equipment for magnetic materials, which mainly improves the special treatment effect on magnetic materials and increases the heat treatment efficiency.

[0005] According to a first aspect of the present invention, a method for heat treatment of a magnetic material is provided, comprising:

[0006] In response to the heat treatment signal of the target magnetic material, the material property information of the target magnetic material, the heat treatment requirement information, and the heat treatment furnace property information of the target heat treatment furnace for heat treatment of the target magnetic material are obtained.

[0007] Determine the material feature vector corresponding to the material property information, the demand feature vector corresponding to the heat treatment demand information, and the heat treatment furnace feature vector corresponding to the heat treatment furnace property information, respectively.

[0008] The material feature vector, demand feature vector, and heat treatment furnace feature vector are fused to obtain the heat treatment fused feature vector;

[0009] The heat treatment fusion feature vector is input into a preset processing method prediction model to predict the heat treatment method, thereby obtaining the target heat treatment method corresponding to the target magnetic material.

[0010] Based on the target heat treatment method, the target heat treatment furnace is controlled to heat treat the target magnetic material to obtain the heat-treated magnetic material.

[0011] According to a second aspect of the present invention, a heat treatment apparatus for magnetic materials is provided, comprising:

[0012] The acquisition unit is used to acquire, in response to the heat treatment signal of the target magnetic material, the material property information of the target magnetic material, the heat treatment requirement information, and the heat treatment furnace property information of the target heat treatment furnace for heat treatment of the target magnetic material.

[0013] The determining unit is used to determine the material feature vector corresponding to the material property information, the demand feature vector corresponding to the heat treatment demand information, and the heat treatment furnace feature vector corresponding to the heat treatment furnace property information, respectively.

[0014] The fusion unit is used to fuse the material feature vector, demand feature vector, and heat treatment furnace feature vector to obtain a heat treatment fusion feature vector.

[0015] The prediction unit is used to input the heat treatment fusion feature vector into a preset processing method prediction model to predict the heat treatment method and obtain the target heat treatment method corresponding to the target magnetic material.

[0016] A heat treatment unit is used to control the target heat treatment furnace to heat treat the target magnetic material based on the target heat treatment method, so as to obtain the heat-treated magnetic material.

[0017] According to a third aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described heat treatment method for magnetic materials.

[0018] According to a fourth aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described heat treatment method for magnetic materials.

[0019] According to the present invention, a method, apparatus, storage medium, and computer device for heat treatment of magnetic materials, compared with the current method of manually setting the heat treatment process in the heat treatment of magnetic materials, the present invention obtains the material property information, heat treatment requirement information, and heat treatment furnace attribute information of the target magnetic material in response to the heat treatment signal of the target magnetic material; and determines the material feature vector corresponding to the material property information, the requirement feature vector corresponding to the heat treatment requirement information, and the heat treatment furnace feature vector corresponding to the heat treatment furnace attribute information, respectively; simultaneously, the material feature vector, the requirement feature vector, and the heat treatment furnace feature vector are fused to obtain a heat treatment fused feature vector; then, the heat treatment fused feature vector is input into a preset processing mode prediction model to predict the heat treatment mode, thereby obtaining the target heat treatment mode corresponding to the target magnetic material; finally, based on the target heat treatment mode, the target heat treatment furnace is controlled to heat treat the target magnetic material to obtain the heat-treated magnetic material. Therefore, by using a pre-defined processing method prediction model, the material properties, heat treatment requirements, and furnace properties of the target magnetic material are comprehensively analyzed to determine the heat treatment method. This method is then used to heat-treat the magnetic material, avoiding the low accuracy and efficiency issues associated with manually determining the heat treatment process. This invention improves the efficiency and accuracy of heat treatment method determination through modeling, thereby enhancing the heat treatment effect on magnetic materials. Furthermore, by fusing various feature vectors, this invention can uncover more latent and deeper features. Predicting the heat treatment method based on these latent and deeper features further improves the accuracy of heat treatment method determination, ultimately enhancing the heat treatment effect on magnetic materials and ensuring that the heat-treated magnetic material better meets performance requirements. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0021] Figure 1 A flowchart of a heat treatment method for a magnetic material provided by an embodiment of the present invention is shown;

[0022] Figure 2 A flowchart of another heat treatment method for magnetic materials provided in an embodiment of the present invention is shown;

[0023] Figure 3 A schematic diagram of a heat treatment apparatus for magnetic materials provided in an embodiment of the present invention is shown.

[0024] Figure 4 A schematic diagram of the structure of another heat treatment apparatus for magnetic materials provided in an embodiment of the present invention is shown;

[0025] Figure 5 A schematic diagram of the physical structure of a computer device provided in an embodiment of the present invention is shown. Detailed Implementation

[0026] The present invention will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the present application can be combined with each other.

[0027] Currently, the heat treatment process for magnetic materials is largely influenced by subjective human factors due to the manual setting of the heat treatment process, which can lead to poor heat treatment results and low heat treatment efficiency.

[0028] To address the aforementioned problems, embodiments of the present invention provide a heat treatment method for magnetic materials, such as... Figure 1 As shown, the method includes:

[0029] 101. In response to the heat treatment signal of the target magnetic material, acquire the material property information of the target magnetic material, the heat treatment requirement information, and the heat treatment furnace property information of the target heat treatment furnace for heat treatment of the target magnetic material.

[0030] The material property information includes the type, shape, and size of the target magnetic material; the heat treatment requirement information refers to the expected requirements after heat treatment of the target magnetic material, such as the expected magnetic properties, mechanical properties, and chemical properties; and the heat treatment furnace property information includes the internal volume and type of the target heat treatment furnace.

[0031] In this embodiment of the invention, the heat treatment process of magnetic materials is achieved through a fully automatic multiple continuous sintering control system for a heat treatment furnace. This system is a visual human-machine interface system for operating and managing the heat treatment furnace. Through this control system, users can flexibly set various parameters on a touchscreen and temperature controller, and intuitively obtain equipment operating information and data, thereby achieving easy and effective equipment operation and management. Most heat treatment processes for magnetic materials require three or more high-temperature sintering and cooling processes to achieve the desired characteristics. This control system provides customers with an efficient solution for handling different products. Utilizing the time signal from the temperature controller (or storing the cooling process time signal in the PLC (Programmable Logic Controller) according to the number of steps), this system can implement multiple heating and cooling processes within a single temperature control curve. The number of heating and cooling cycles can be arbitrarily set, sufficient to meet customers' needs for customized production processes for different products. Combined with the parameter settings of the HMI (Human Machine Interface) and the logic program of the PLC, the fully automatic multiple continuous sintering function is easier to implement. The control system includes: Touchscreen: The Mitsubishi embedded touchscreen on the control cabinet serves as the main control touchscreen for the equipment, with functions such as operating the various mechanisms of the equipment, displaying operating status, editing process curves, setting process parameters, and recording operating data. The following describes the specific content and operation methods of each screen on the touchscreen in this control system: 1. Main Screen: The main screen displays the company's pre-set QR code. Scanning the QR code allows users to follow the company's official WeChat account and stay updated on company news. The main interface also includes buttons for switching between various windows. 2. Operating System: This contains the main operating area of ​​the equipment. The upper right corner of each mechanism indicates whether the system's operating conditions are met; "×" indicates not met, and "√" indicates met. For example, when the system's operating conditions are met, pressing and holding the button for 2 seconds will start operation, and pressing and holding the button again for 2 seconds will stop operation. When the operating conditions are not met, pressing and holding the button for 2 seconds will bring up the operating conditions window for that mechanism, clearly showing which conditions are not met. The lower half mainly displays real-time data, including heating curve data, furnace vacuum data, and temperature data. 3. Operating Conditions Window: For example, clicking the Operating Conditions button on the operation screen will open the Operating Conditions window. Clicking the buttons for each mechanism again will open the corresponding Operating Conditions window. Gray buttons indicate that the operating conditions for that mechanism are not met, while green buttons indicate that they are met.4. Temperature Control System: For example, the top of the interface displays a real-time temperature curve, and the right side shows a heating hold function button. Pressing the button will bring up a hold time setting window. Setting the time to 0 will hold the temperature permanently until manually canceled. When a specific value is set, the hold function will automatically cancel after the set value is reached. The right side also shows a heating curve skip function button. To prevent accidental activation, the button needs to be pressed and held for 2 seconds to execute the skip function. Real-time data, including heating curve data, furnace vacuum data, and temperature data, can be displayed at the bottom of the screen. 5. Password Window: A password is required to access parameter settings. 6. Parameter Setting Window: Parameter settings can be entered manually or automatically generated. 7. Historical Data Window: This window records important equipment data. For example, it can be set to record every 10 seconds and periodically saved to a database, or it can be saved manually. 8. Historical Curve Window: Allows precise searching of curves for a specific time period or data at a specific moment. 9. Running Time Window: Records the running time or number of runs for each mechanism, allowing for periodic maintenance of each mechanism based on these records. 10. Action Record Window: This window records the action status and limit positions of each mechanism, facilitating customer checks whether the operation is performed according to process requirements and aiding maintenance personnel in troubleshooting. 11. Alarm Screen: For example, a red alarm bar indicates an alarm has occurred; the electrode alarm button displays detailed alarm countermeasures. 12. Alarm Record Window: This window records all alarms that have occurred. Furthermore, this control system includes a temperature controller, which primarily utilizes its time signal function as a cooling signal. Multiple cooling signals can be set within a single temperature control curve, enabling multiple "sintering-cooling" processes. The fully automatic multi-stage continuous sintering control system for the heat treatment furnace in this embodiment of the invention consists of a human-machine interface configuration, PLC, temperature controller, and sensors, providing users with a safe, efficient, simple, and practical method for equipment control and management.

[0032] Specifically, when heat treatment is required on a target magnetic material, a heat treatment signal for the target magnetic material is triggered in the control system. The control system is linked to a database, and based on the identification information of the target magnetic material carried in the control signal, the material property information of the target magnetic material is obtained from the database. At the same time, the operator can select or input heat treatment requirement information in the corresponding interface of the control system. The control system will select a target heat treatment furnace that meets the heat treatment conditions from multiple heat treatment furnaces, and at the same time obtain the heat treatment furnace attribute information of the target heat treatment furnace from the database. Then, the material property information, heat treatment requirement information, and heat treatment furnace attribute information are comprehensively analyzed to determine the optimal heat treatment method for the target magnetic material. Through the optimal heat treatment method, a temperature control curve can be generated. The fully automatic multi-stage continuous sintering control system of the heat treatment furnace performs heat treatment on the magnetic material according to the process parameters in the temperature control curve. If multiple sintering and cooling operations are required for the magnetic material, they can all be achieved through this single temperature control curve, avoiding the time wasted by determining multiple heating process curves for multiple heating and cooling operations. Thus, the embodiments of the present invention can improve the heat treatment efficiency of magnetic materials. The embodiments of the present invention are mainly applied to the scenario of heat treatment of magnetic materials. The execution subject of the embodiments of the present invention is a device or equipment capable of heat treatment of magnetic materials, which can be set on the client or server side.

[0033] 102. Determine the material feature vector corresponding to the material property information, the demand feature vector corresponding to the heat treatment demand information, and the heat treatment furnace feature vector corresponding to the heat treatment furnace property information, respectively.

[0034] 103. The material feature vector, demand feature vector, and heat treatment furnace feature vector are fused to obtain the heat treatment fused feature vector.

[0035] In this embodiment of the invention, material property information can be transformed into material feature vectors, heat treatment requirement information into requirement feature vectors, and heat treatment furnace attribute information into heat treatment furnace feature vectors using word embedding and other methods. Then, the material feature vectors, requirement feature vectors, and heat treatment furnace feature vectors are fused. Finally, the fused feature vectors are used to predict the heat treatment method. By fusing the material feature vectors, requirement feature vectors, and heat treatment furnace feature vectors, multiple features can be combined and more latent features can be extracted, thereby improving the accuracy of heat treatment of magnetic materials. At the same time, fusing multiple features into one feature can reduce the input of the model, that is, reduce the input dimension of the model, thereby optimizing computing resources, improving the prediction efficiency of the model, and thus improving the heat treatment efficiency of magnetic materials.

[0036] 104. Input the heat treatment fusion feature vector into the preset processing method prediction model to predict the heat treatment method and obtain the target heat treatment method corresponding to the target magnetic material.

[0037] 105. Based on the target heat treatment method, control the target heat treatment furnace to heat treat the target magnetic material to obtain the heat-treated magnetic material.

[0038] The target heat treatment method includes: heating method, heat preservation method and cooling method. The heating method includes information such as heating form (e.g., electric heating), heating temperature, and heating time. The heat preservation method includes information such as heat preservation time. The cooling method includes information such as cooling form (water quenching, air cooling, etc.), cooling time, and cooling rate.

[0039] In this embodiment of the invention, to improve the prediction accuracy of the preset processing method prediction model, it is necessary to pre-train and construct the preset processing method prediction model. Based on this, the method includes: constructing multiple preset initial models and acquiring sample data, wherein the sample data includes sample material attribute information of the sample magnetic material, sample heat treatment requirement information, sample heat treatment furnace attribute information of the sample heat treatment furnace for heat treatment of the sample magnetic material, and acquiring the actual heat treatment method of the sample magnetic material; based on the number of preset initial models, dividing the sample data into multiple sets of training data and multiple sets of test data; using each of the training data to train the corresponding preset initial model to obtain the trained preset initial model, wherein the training process includes: determining the sample material attribute information in the training data... The sample material feature vector, sample demand feature vector, and sample furnace attribute vector are fused together to obtain a sample fused feature vector. This fused feature vector is used as the input data for a preset initial model, and the actual heat treatment method is used as the output data. The prediction accuracy of the corresponding trained preset initial models is tested using the test data to obtain the prediction accuracy of each trained preset initial model. The maximum prediction accuracy is determined among the prediction accuracies, and the trained preset initial model corresponding to the maximum prediction accuracy is determined as the preset processing method prediction model. Furthermore, after fusing the material feature vector, demand feature vector, and heat treatment furnace feature vector, the fused heat treatment feature vector is input into a preset processing method prediction model to predict the heat treatment method. Ultimately, the target heat treatment furnace is controlled to use the target heat treatment method output by the model to heat treat the target magnetic material. Thus, the model predicts the optimal heat treatment method, avoiding the time wasted and errors caused by manually determining the heat treatment method. Therefore, this embodiment of the invention can improve the heat treatment efficiency and accuracy of magnetic materials, thereby improving the heat treatment effect of magnetic materials and enabling the heat-treated magnetic materials to meet user requirements.

[0040] According to the present invention, a heat treatment method for magnetic materials, compared with the current method of manually setting the heat treatment process in the heat treatment of magnetic materials, the present invention obtains the material property information, heat treatment requirement information, and heat treatment furnace attribute information of the target magnetic material in response to the heat treatment signal of the target magnetic material; and determines the material feature vector corresponding to the material property information, the requirement feature vector corresponding to the heat treatment requirement information, and the heat treatment furnace feature vector corresponding to the heat treatment furnace attribute information, respectively; simultaneously, the material feature vector, the requirement feature vector, and the heat treatment furnace feature vector are fused to obtain a heat treatment fused feature vector; then, the heat treatment fused feature vector is input into a preset processing mode prediction model to predict the heat treatment mode, thereby obtaining the target heat treatment mode corresponding to the target magnetic material; finally, based on the target heat treatment mode, the target heat treatment furnace is controlled to heat treat the target magnetic material to obtain the heat-treated magnetic material. Therefore, by using a pre-defined processing method prediction model, the material properties, heat treatment requirements, and furnace properties of the target magnetic material are comprehensively analyzed to determine the heat treatment method. This method is then used to heat-treat the magnetic material, avoiding the low accuracy and efficiency issues associated with manually determining the heat treatment process. This invention improves the efficiency and accuracy of heat treatment method determination through modeling, thereby enhancing the heat treatment effect on magnetic materials. Furthermore, by fusing various feature vectors, this invention can uncover more latent and deeper features. Predicting the heat treatment method based on these latent and deeper features further improves the accuracy of heat treatment method determination, ultimately enhancing the heat treatment effect on magnetic materials and ensuring that the heat-treated magnetic material better meets performance requirements.

[0041] Furthermore, to better illustrate the above data classification process, and as a refinement and extension of the above embodiments, this invention provides another heat treatment method for magnetic materials, such as... Figure 2 As shown, the method includes:

[0042] 201. In response to the heat treatment signal of the target magnetic material, acquire the material property information of the target magnetic material, the heat treatment requirement information, and the heat treatment furnace property information of the target heat treatment furnace for heat treatment of the target magnetic material.

[0043] In this embodiment of the invention, after receiving a heat treatment signal for a target magnetic material, in order to heat treat the target magnetic material, it is first necessary to determine a target heat treatment furnace that meets the heat treatment conditions. Based on this, the method includes: determining the storage location information corresponding to the target magnetic material, and determining different idle heat treatment furnaces in the heat treatment site where the target magnetic material is located, and obtaining furnace attribute information for each idle heat treatment furnace; determining the attribute feature vector corresponding to the furnace attribute information, and performing clustering processing on each idle heat treatment furnace based on the attribute feature vector to obtain idle heat treatment furnaces under different cluster categories; determining a target cluster category that meets the heat treatment requirements in the different cluster categories based on the material attribute information and heat treatment requirement information; determining the furnace location information of each idle heat treatment furnace under the target cluster category, and determining the target heat treatment furnace among the idle heat treatment furnaces under the target cluster category based on the storage location information of the target magnetic material and the furnace location information of each idle heat treatment furnace.

[0044] An idle heat treatment furnace refers to a heat treatment furnace that, after receiving a heat treatment signal, is not undergoing heat treatment at the special treatment site where the target magnetic material is located, has no material inside the furnace that needs to be treated, and is capable of normal operation. The furnace attribute information of an idle heat treatment furnace includes: furnace type, controllable temperature range, heating method, furnace structure, furnace chamber volume, etc. Different cluster categories can be divided according to furnace type, controllable temperature range, heating method, furnace structure, furnace chamber volume, etc. This embodiment of the invention does not specifically limit the different cluster categories.

[0045] In this embodiment of the invention, to accurately and quickly select the target heat treatment furnace that meets the heat treatment requirements, it is first necessary to cluster the various idle heat treatment furnaces. Based on this, the method includes: initializing the centroid vectors corresponding to different clusters; calculating the cosine similarity between each attribute feature vector and the centroid vector corresponding to the different clusters, and classifying each idle heat treatment furnace into the different clusters based on the cosine similarity; determining the updated centroid vector corresponding to the different clusters based on the attribute feature vectors corresponding to the idle heat treatment furnaces in the different clusters; and reclassifying each idle heat treatment furnace into the different clusters based on the updated centroid vectors until the updated centroid vectors do not change. The idle heat treatment furnaces finally classified into the different clusters are determined as idle heat treatment furnaces under the different cluster categories.

[0046] Specifically, the process involves selecting the centroid vectors corresponding to the initial centroids of K clusters, calculating the cosine similarity between each attribute feature vector and the K centroid vectors for each idle heat treatment furnace, and further, after calculating the cosine similarity between each idle heat treatment furnace and the centroid vectors of each cluster, assigning each attribute feature vector to the cluster corresponding to the centroid vector with the largest cosine similarity. Then, for each cluster, the centroid and its corresponding centroid vector are recalculated, and each idle heat treatment furnace is reclassified into different clusters. This process is repeated until the position of the centroid remains unchanged. Finally, the idle heat treatment furnaces classified into different clusters are determined as idle heat treatment furnaces under different cluster categories.

[0047] For example, different idle heat treatment furnaces are clustered according to the controllable temperature range and furnace volume to obtain idle heat treatment furnaces under different cluster categories. Then, based on the volume of the target magnetic material and the heating temperature requirements in the heat treatment demand information, a target cluster category that matches the volume and heating temperature requirements of the magnetic material is selected. Then, among the idle heat treatment furnaces under the target cluster category, the idle heat treatment furnace closest to the location of the target magnetic material is selected and determined as the target heat treatment furnace for heat treatment of the target magnetic material. If the heat treatment furnaces are not clustered and a suitable heat treatment furnace is selected based on the clustering results, but instead a heat treatment furnace is randomly selected, if a heat treatment furnace with a small volume is selected to heat treat the material, the material needs to be heat treated multiple times, thereby increasing the heat treatment time. Therefore, by clustering idle heat treatment furnaces, heat treatment furnaces that match and meet the heat treatment requirements can be found, thereby improving the heat treatment efficiency of the material and the heat treatment effect of the magnetic material.

[0048] 202. Determine the material feature vector corresponding to the material property information, the demand feature vector corresponding to the heat treatment demand information, and the heat treatment furnace feature vector corresponding to the heat treatment furnace property information, respectively.

[0049] 203. Determine the principal component eigenvectors between every two eigenvectors in the material eigenvector, demand eigenvector, and heat treatment furnace eigenvector, including: determining the first principal component eigenvector between the material eigenvector and demand eigenvector, the second principal component eigenvector between the material eigenvector and heat treatment furnace eigenvector, and the third principal component eigenvector between the demand eigenvector and heat treatment furnace eigenvector.

[0050] 204. The first principal component feature vector, the second principal component feature vector, and the third principal component feature vector are concatenated horizontally to obtain the concatenated feature vector.

[0051] 205. Determine the transformation coefficients corresponding to the spliced ​​feature vectors, and perform a linear transformation on the spliced ​​feature vectors based on the transformation coefficients to obtain the heat treatment fusion feature vectors.

[0052] In this embodiment of the invention, word embedding methods such as Word2Vec can be used to determine the material feature vector corresponding to material property information, the demand feature vector corresponding to heat treatment demand information, and the heat treatment furnace feature vector corresponding to heat treatment furnace attribute information, respectively. Then, in order to extract deeper features from the material property information, heat treatment demand information, and heat treatment furnace attribute information, it is necessary to perform a fusion process on the material feature vector, demand feature vector, and heat treatment furnace feature vector. Specifically, the fusion process involves determining the principal component feature vector between every two feature vectors in the material feature vector, demand feature vector, and heat treatment furnace feature vector. Specifically, the principal component feature vector between every two feature vectors is determined. The method for identifying principal component eigenvectors between eigenvectors includes: constructing a feature matrix based on every two eigenvectors; determining the mean of each element in the feature matrix and subtracting the mean from each element in the feature matrix to obtain a centered feature matrix; determining the covariance matrix corresponding to the centered feature matrix; performing eigenvalue decomposition on the covariance matrix to obtain matrix eigenvalues ​​and matrix eigenvectors; selecting a predetermined number of matrix eigenvectors from the matrix eigenvectors based on the magnitude of the matrix eigenvalues, and determining the predetermined number of matrix eigenvectors as the principal component eigenvectors between every two eigenvectors.

[0053] The preset quantity is a value set based on actual needs (experience); each pair of feature vectors includes a material feature vector and a demand feature vector, a material feature vector and a heat treatment furnace feature vector, and a demand feature vector and a heat treatment furnace feature vector. Specifically, if each pair of feature vectors is: a material feature vector and a demand feature vector, the material feature vector is [11 2 4 2], and the demand feature vector is [1 3 3 4 4], the feature matrix composed of the material feature vector and the demand feature vector is as follows:

[0054]

[0055] Where T represents the feature matrix, the mean of the elements in the first row of the feature matrix is ​​determined, and each element in the first row is subtracted from this mean to obtain the subtraction result for each element in the first row. At the same time, the mean of the elements in the second row is determined, and each element in the second row is subtracted from its corresponding mean to obtain the subtraction result for each element in the second row. Finally, the subtraction results of the elements in the first row and the subtraction results of the elements in the second row constitute the centered feature matrix as follows:

[0056]

[0057] Among them, T z Let X represent the centered feature matrix. Then, calculate the covariance between the elements in each row of the centered feature matrix. For example, the covariance between the elements in the first row, Cov(X1, X1) = [(-1)]. 2 +(-1) 2 +(0) 2 +(2) 2 +(0) 2 ] / (5-1)=1.5, the covariance Cov(X1,X2) between the elements of the first and second rows is =[(-1)×(-2)+(-1)×0+0×0+2×1+0×1] / (5-1)=1, thus we can calculate the covariance between all row elements. Then, we can form a covariance matrix from all the covariances and perform eigenvalue decomposition on the covariance matrix. The specific decomposition method is as follows: First, determine the eigenvector group of the covariance matrix, and calculate the corresponding eigenvalues ​​according to the eigenvector group. For example, if the obtained eigenvector group is k, then there are k corresponding eigenvalues. Each eigenvalue has its corresponding eigenvector. Then, sort the k eigenvalues ​​in descending order to obtain the sorted eigenvalues. Then, select the top n (preset number) eigenvalues ​​from the sorted eigenvalues ​​and determine the eigenvectors corresponding to the top n eigenvalues ​​as the principal component eigenvectors. The first principal component feature vector is the feature vector between the material feature vector and the demand feature vector. Similarly, the second principal component feature vector between the material feature vector and the heat treatment furnace feature vector, and the third principal component feature vector between the demand feature vector and the heat treatment furnace feature vector can be determined in the same way. Then, the first principal component feature vector, the second principal component feature vector, and the third principal component feature vector are horizontally concatenated to obtain the concatenated feature vector. For example, if the first principal component feature vector is f(a1, b1), the second principal component feature vector is f(a2, b2), and the third principal component feature vector is f(a3, b3), and the preset transformation coefficient is w, the above three principal component feature vectors are horizontally concatenated, and then transformed based on the transformation coefficient to obtain the heat treatment fusion feature vector f(w(a1, b1, a2, b2, a3, b3)). The transformation coefficient can be set according to actual needs. In the embodiments of the present invention, the material property information, heat treatment requirement information, and heat treatment furnace property information belong to data from different data sources. By fusing data from different data sources into more significant and deeper features, and using the model to analyze these more significant and deeper features, the predictive performance of the model can be improved, thereby improving the heat treatment effect of magnetic materials.

[0058] 206. Input the heat treatment fusion feature vector into the preset processing method prediction model to predict the heat treatment method and obtain the target heat treatment method corresponding to the target magnetic material.

[0059] In this embodiment of the invention, after fusing the material feature vector, demand feature vector, and heat treatment furnace feature vector to obtain a heat treatment fused feature vector, the heat treatment fused feature vector is input into a preset processing method prediction model to predict the heat treatment method. The preset processing method prediction model can output the target heat treatment method corresponding to the target magnetic material. Finally, the target heat treatment method is used to heat treat the target magnetic material. Thus, by using a pre-trained preset processing method prediction model to predict the heat treatment method, the time wasted and prediction errors caused by manually determining the heat treatment method can be avoided. Therefore, this embodiment of the invention can improve the prediction efficiency and accuracy of the heat treatment method, thereby improving the heat treatment efficiency and heat treatment effect of the magnetic material.

[0060] 207. Based on the target heat treatment method, control the target heat treatment furnace to heat treat the target magnetic material to obtain the heat-treated magnetic material.

[0061] The target heat treatment methods include: heating treatment, heat preservation treatment, and cooling treatment. Heating treatment methods include: heating method (resistance heating, etc.), heating time, heating temperature, etc. Heat preservation treatment methods include: heat preservation method, heat preservation time, etc. Cooling treatment methods include: cooling method (water cooling, air cooling, etc.), cooling rate, etc.

[0062] In this embodiment of the invention, the preset processing method prediction model can predict the heating treatment method, heat preservation treatment method, and cooling treatment method that meet the requirements of the heat treatment process. Then, the target magnetic material needs to be heat-treated according to the above methods. Based on this, step 207 specifically includes: controlling the target heat treatment furnace to heat the target magnetic material based on the heating method, heating time, and heating temperature to obtain the heat-treated magnetic material; controlling the target heat treatment furnace to heat-preserve the heat-treated magnetic material based on the heat preservation method and heat preservation time to obtain the heat-preserved magnetic material; and controlling the target heat treatment furnace to cool the heat-preserved magnetic material based on the cooling method and cooling rate to obtain the heat-treated magnetic material.

[0063] Specifically, firstly, the target heat treatment furnace is controlled to heat the target magnetic material according to the heating method, heating time, and heating temperature. Then, according to the holding method and holding time, the target heat treatment furnace is controlled to hold the heated magnetic material at a specific temperature. Finally, according to the cooling method and cooling rate, the target heat treatment furnace is controlled to cool the held magnetic material, resulting in the heat-treated magnetic material. By using a model to predict the heating, holding, and cooling methods during the heat treatment of magnetic materials, the entire heat treatment process can be automated, thereby improving the efficiency of heat treatment of magnetic materials.

[0064] Furthermore, in order to ensure the safety and stability of the heat treatment process and to guarantee the heat treatment effect, it is also necessary to monitor the heat treatment process in real time during the heat treatment of the target magnetic material. Based on this, the method includes: during the heat treatment of the target magnetic material, acquiring the heating attribute information of the target heat treatment furnace, wherein the heating attribute information includes at least one of the real-time temperature, real-time vacuum degree, and real-time pressure in the target heat treatment furnace; based on the heating attribute information, determining whether the target heat treatment furnace meets the preset alarm conditions; if the target heat treatment furnace meets the preset conditions, generating alarm information based on the heating attribute information, and calling a preset communication tool interface to send the alarm information to the monitoring terminal.

[0065] Specifically, during the heat treatment of the target magnetic material, the real-time temperature, real-time vacuum level, and real-time pressure of the target heat treatment furnace are acquired. The real-time temperature can be the temperature at various locations within the furnace. Based on the temperature at each location, it is determined whether the furnace meets the condition of uniform temperature distribution. If the temperature distribution is uneven, an alarm message is generated. Simultaneously, it is determined whether the real-time vacuum level is within a preset vacuum level range (set according to actual needs). If not, an alarm message is generated. Similarly, it is determined whether the real-time pressure is within a preset pressure range (also set according to actual needs). If not, an alarm message is generated. Finally, communication tools such as email and SMS are used to send the alarm messages to the monitoring personnel at the monitoring terminal. This allows the monitoring personnel to correct the heat treatment process based on the alarm information, thereby achieving stable operation of the heat treatment process and ensuring the heat treatment effect of the magnetic material.

[0066] Furthermore, to further improve the safety of heat treatment, the following settings can be implemented during the heat treatment process:

[0067] A. Furnace overpressure setting: When the pressure inside the furnace exceeds the set value, the system will alarm for overpressure.

[0068] B. Furnace over-temperature setting: When the temperature inside the furnace exceeds the set value, the system will alarm for over-temperature.

[0069] C. Roots pump starting pressure: The Roots pump is allowed to start below the set pressure.

[0070] D. Heating operating pressure: During heating start-up and operation, the furnace pressure must be below the set value; otherwise, heating will stop.

[0071] E. Heating to start vacuum: Heating is allowed to start below the set value. Once heating is started, it will not be limited by this vacuum level. Even if the vacuum level rises above the set value, heating will not stop.

[0072] F. Heating tolerable vacuum: After heating is turned on, heating will stop when the vacuum level rises above the set value.

[0073] G. Vent opening temperature: If the temperature is below this setting, the cooling temperature is in place, allowing the vent valve to open, the lock ring to loosen, and the slide valve to open.

[0074] H. Exhaust Gas Pressure: The pressure at which gas is supplied before discharge after heating and cooling is completed. This pressure is set to be equal to atmospheric pressure.

[0075] I. Door opening pressure: When the lock ring is loosened and the pressure is released to this level, the furnace door can be opened. This pressure is set to be equal to atmospheric pressure.

[0076] J. Inflation Timeout: The timer starts when the inflation valve is opened. An alarm will be triggered if the time taken to inflate to the upper limit of the air-cooled inflation pressure exceeds the set value.

[0077] K. Pressure divider inflation timeout: The timer starts when the pressure divider inflation valve is opened. An alarm will be triggered if the time to inflate to the upper pressure limit exceeds the set value.

[0078] L. Upper limit of air-cooled inflation pressure: Sets the upper limit of inflation pressure when air-cooled; Lower limit of air-cooled inflation pressure: Sets the lower limit of inflation pressure when air-cooled. First, inflate to the midpoint between the upper and lower limits (for example, if the midpoint exceeds 100, this value becomes 85). Then, turn on the fan and continue inflating to the upper limit. When the pressure drops to the lower limit, replenish the air again to the upper limit.

[0079] M. Small pressure, large pressure divider pressure setting: Inflate to the upper pressure limit, then open the exhaust valve to exhaust to the lower pressure limit, repeat the "inflation-exhaust" action.

[0080] N. The following three parameters are for automatic operation: Cooling inflation temperature: Inflation is allowed below this setting value; Fan start temperature: Inflation is complete, air cooling is allowed below this setting value; Air cooling end temperature: Air cooling ends when the setting value is below this setting value.

[0081] O. Temperature difference measurement curve: When the temperature difference between the three zones exceeds the set value, an alarm is triggered and the heating curve is maintained. When the temperature difference is lower than the set value, the heating curve maintenance is canceled and the temperature continues to rise. This function is invalid when the set value is 0. Starting temperature: This function is valid when the starting temperature is exceeded.

[0082] P. Temperature difference measured by curve: When the difference between the actual temperature value and the measured temperature value exceeds the set value, an alarm is triggered and the heating curve is maintained. When the difference is lower than the set value, the heating curve maintenance is canceled and the temperature continues to rise. This function is invalid when the set value is 0. Starting temperature: This function is valid when the starting temperature is exceeded.

[0083] Q. Heating Curve Setting: Setting the heating curve number. You can enter it manually or generate it automatically.

[0084] According to another heat treatment method for magnetic materials provided by the present invention, compared with the current method of manually setting the heat treatment process in the heat treatment of magnetic materials, the present invention obtains the material property information, heat treatment requirement information, and heat treatment furnace attribute information of the target magnetic material in response to the heat treatment signal of the target magnetic material; and determines the material feature vector corresponding to the material property information, the requirement feature vector corresponding to the heat treatment requirement information, and the heat treatment furnace feature vector corresponding to the heat treatment furnace attribute information, respectively; simultaneously, the material feature vector, the requirement feature vector, and the heat treatment furnace feature vector are fused to obtain a heat treatment fused feature vector; then, the heat treatment fused feature vector is input into a preset processing mode prediction model to predict the heat treatment mode, thereby obtaining the target heat treatment mode corresponding to the target magnetic material; finally, based on the target heat treatment mode, the target heat treatment furnace is controlled to heat treat the target magnetic material to obtain the heat-treated magnetic material. Therefore, by using a pre-defined processing method prediction model, the material properties, heat treatment requirements, and furnace properties of the target magnetic material are comprehensively analyzed to determine the heat treatment method. This method is then used to heat-treat the magnetic material, avoiding the low accuracy and efficiency issues associated with manually determining the heat treatment process. This invention improves the efficiency and accuracy of heat treatment method determination through modeling, thereby enhancing the heat treatment effect on magnetic materials. Furthermore, by fusing various feature vectors, this invention can uncover more latent and deeper features. Predicting the heat treatment method based on these latent and deeper features further improves the accuracy of heat treatment method determination, ultimately enhancing the heat treatment effect on magnetic materials and ensuring that the heat-treated magnetic material better meets performance requirements.

[0085] Furthermore, as Figure 1In a specific implementation, embodiments of the present invention provide a heat treatment apparatus for magnetic materials, such as... Figure 3 As shown, the device includes: an acquisition unit 31, a determination unit 32, a fusion unit 33, a prediction unit 34, and a heat treatment unit 35.

[0086] The acquisition unit 31 can be used to acquire material property information, heat treatment requirement information, and heat treatment furnace attribute information of the target magnetic material in response to the heat treatment signal of the target magnetic material.

[0087] The determining unit 32 can be used to determine the material feature vector corresponding to the material property information, the demand feature vector corresponding to the heat treatment demand information, and the heat treatment furnace feature vector corresponding to the heat treatment furnace property information, respectively.

[0088] The fusion unit 33 can be used to fuse the material feature vector, demand feature vector, and heat treatment furnace feature vector to obtain a heat treatment fusion feature vector.

[0089] The prediction unit 34 can be used to input the heat treatment fusion feature vector into a preset processing method prediction model to predict the heat treatment method and obtain the target heat treatment method corresponding to the target magnetic material.

[0090] The heat treatment unit 35 can be used to control the target heat treatment furnace to heat treat the target magnetic material based on the target heat treatment method, so as to obtain the heat-treated magnetic material.

[0091] In specific application scenarios, in order to perform heat treatment on the target magnetic material, such as Figure 4 As shown, the heat treatment unit 35 includes a heating module 351, a heat preservation module 352, and a cooling module 353.

[0092] The heating module 351 can be used to control the target heat treatment furnace to heat the target magnetic material based on the heating method, heating time and heating temperature, so as to obtain the heat-treated magnetic material.

[0093] The heat preservation module 352 can be used to control the target heat treatment furnace to perform heat preservation treatment on the heated magnetic material based on the heat preservation method and heat preservation time, so as to obtain the heat-preserved magnetic material.

[0094] The cooling module 353 can be used to control the target heat treatment furnace to cool the heat-insulated magnetic material based on the cooling method and cooling rate, so as to obtain the heat-treated magnetic material.

[0095] In specific application scenarios, in order to monitor the heat treatment process in real time, the device also includes a judgment unit 36 ​​and an alarm unit 37.

[0096] The acquisition unit 31 can also be used to acquire heating attribute information of the target heat treatment furnace during the heat treatment of the target magnetic material, wherein the heating attribute information includes at least one of the real-time temperature, real-time vacuum degree, and real-time pressure inside the target heat treatment furnace.

[0097] The judgment unit 36 ​​can be used to determine whether the target heat treatment furnace meets the preset conditions for alarm based on the heating attribute information.

[0098] The alarm unit 37 can be used to generate alarm information based on the heating attribute information if the target heat treatment furnace meets the preset conditions, and call the preset communication tool interface to send the alarm information to the monitoring terminal.

[0099] In specific application scenarios, in order to perform feature vector fusion processing, the fusion unit 33 includes a determination module 331, a splicing module 332, and a transformation module 333.

[0100] The determining module 331 can be used to determine the principal component feature vector between every two feature vectors in the material feature vector, demand feature vector, and heat treatment furnace feature vector, including: determining the first principal component feature vector between the material feature vector and the demand feature vector, the second principal component feature vector between the material feature vector and the heat treatment furnace feature vector, and the third principal component feature vector between the demand feature vector and the heat treatment furnace feature vector.

[0101] The splicing module 332 can be used to horizontally splice the first principal component feature vector, the second principal component feature vector, and the third principal component feature vector to obtain a spliced ​​feature vector.

[0102] The transformation module 333 can be used to determine the transformation coefficients corresponding to the spliced ​​feature vector, and perform a linear transformation on the spliced ​​feature vector based on the transformation coefficients to obtain the heat treatment fusion feature vector.

[0103] In specific application scenarios, in order to determine the principal component eigenvectors, the determining module 331 can be used to construct a feature matrix based on every two eigenvectors; determine the mean of each element in the feature matrix, and subtract the mean from each element in the feature matrix to obtain a centered feature matrix; determine the covariance matrix corresponding to the centered feature matrix; perform eigenvalue decomposition on the covariance matrix to obtain matrix eigenvalues ​​and matrix eigenvectors; and select a preset number of matrix eigenvectors from the matrix eigenvectors based on the magnitude of the matrix eigenvalues, and determine the preset number of matrix eigenvectors as the principal component eigenvectors between every two eigenvectors.

[0104] In specific application scenarios, in order to determine the target heat treatment furnace that meets the heat treatment requirements, the device also includes a clustering unit 38.

[0105] The determining unit 32 can also be used to determine the storage location information corresponding to the target magnetic material, as well as to determine different idle heat treatment furnaces in the heat treatment site where the target magnetic material is located, and to obtain the furnace attribute information of each idle heat treatment furnace.

[0106] The clustering unit 38 can be used to determine the attribute feature vector corresponding to the furnace attribute information, and based on the attribute feature vector, to perform clustering processing on each idle heat treatment furnace to obtain idle heat treatment furnaces under different clustering categories.

[0107] The determining unit 32 can be specifically used to determine the target cluster category that meets the heat treatment requirements among the different cluster categories based on the material property information and heat treatment requirement information.

[0108] The determining unit 32 can specifically be used to determine the furnace location information of each idle heat treatment furnace under the target cluster category, and to determine the target heat treatment furnace among the idle heat treatment furnaces under the target cluster category based on the storage location information of the target magnetic material and the furnace location information of each idle heat treatment furnace.

[0109] In specific application scenarios, in order to perform clustering processing on each idle heat treatment furnace, the clustering unit 38 can be used to initialize the centroid vectors corresponding to different clusters; calculate the cosine similarity between each attribute feature vector and the centroid vectors corresponding to the different clusters, and classify each idle heat treatment furnace into the different clusters based on the cosine similarity; determine the updated centroid vectors corresponding to the different clusters based on the attribute feature vectors corresponding to the idle heat treatment furnaces in the different clusters; and reclassify each idle heat treatment furnace into the different clusters based on the updated centroid vectors until the updated centroid vectors do not change. The idle heat treatment furnaces finally classified into the different clusters are determined as idle heat treatment furnaces under the different cluster categories.

[0110] It should be noted that other corresponding descriptions of the functional modules involved in the heat treatment apparatus for magnetic materials provided in this embodiment of the invention can be found in [reference needed]. Figure 1 The corresponding description of the method shown will not be repeated here.

[0111] Based on the above, Figure 1 Accordingly, this embodiment of the invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the following steps: responding to a heat treatment signal for a target magnetic material, acquiring material property information, heat treatment requirement information, and heat treatment furnace attribute information of a target heat treatment furnace for heat treatment of the target magnetic material; determining, respectively, the material feature vector corresponding to the material property information, the requirement feature vector corresponding to the heat treatment requirement information, and the heat treatment furnace feature vector corresponding to the heat treatment furnace attribute information; fusing the material feature vector, the requirement feature vector, and the heat treatment furnace feature vector to obtain a heat treatment fusion feature vector; inputting the heat treatment fusion feature vector into a preset processing method prediction model to predict the heat treatment method, thereby obtaining the target heat treatment method corresponding to the target magnetic material; and, based on the target heat treatment method, controlling the target heat treatment furnace to heat treat the target magnetic material to obtain the heat-treated magnetic material.

[0112] Based on the above, Figure 1 The method shown and as Figure 3 The embodiment of the device shown in the invention also provides a physical structure diagram of a computer device, such as... Figure 5As shown, the computer device includes: a processor 41, a memory 42, and a computer program stored in the memory 42 and executable on the processor. Both the memory 42 and the processor 41 are mounted on a bus 43. When the processor 41 executes the program, it performs the following steps: In response to a heat treatment signal for a target magnetic material, it acquires material property information, heat treatment requirement information, and heat treatment furnace attribute information of the target magnetic material; it determines the material feature vector corresponding to the material property information, the requirement feature vector corresponding to the heat treatment requirement information, and the heat treatment furnace feature vector corresponding to the heat treatment furnace attribute information; it fuses the material feature vector, the requirement feature vector, and the heat treatment furnace feature vector to obtain a heat treatment fusion feature vector; it inputs the heat treatment fusion feature vector into a preset processing method prediction model to predict the heat treatment method, thereby obtaining the target heat treatment method corresponding to the target magnetic material; based on the target heat treatment method, it controls the target heat treatment furnace to perform heat treatment on the target magnetic material, thereby obtaining the heat-treated magnetic material.

[0113] Through the technical solution of this invention, in response to the heat treatment signal of the target magnetic material, this invention acquires the material property information, heat treatment requirement information, and heat treatment furnace attribute information of the target heat treatment furnace for heat treatment of the target magnetic material; and determines the material feature vector corresponding to the material property information, the requirement feature vector corresponding to the heat treatment requirement information, and the heat treatment furnace feature vector corresponding to the heat treatment furnace attribute information, respectively; simultaneously, the material feature vector, requirement feature vector, and heat treatment furnace feature vector are fused to obtain a heat treatment fused feature vector; then, the heat treatment fused feature vector is input into a preset processing method prediction model to predict the heat treatment method, thereby obtaining the target heat treatment method corresponding to the target magnetic material; finally, based on the target heat treatment method, the target heat treatment furnace is controlled to heat treat the target magnetic material to obtain the heat-treated magnetic material. Therefore, by using a pre-defined processing method prediction model, the material properties, heat treatment requirements, and furnace properties of the target magnetic material are comprehensively analyzed to determine the heat treatment method. This method is then used to heat-treat the magnetic material, avoiding the low accuracy and efficiency issues associated with manually determining the heat treatment process. This invention improves the efficiency and accuracy of heat treatment method determination through modeling, thereby enhancing the heat treatment effect on magnetic materials. Furthermore, by fusing various feature vectors, this invention can uncover more latent and deeper features. Predicting the heat treatment method based on these latent and deeper features further improves the accuracy of heat treatment method determination, ultimately enhancing the heat treatment effect on magnetic materials and ensuring that the heat-treated magnetic material better meets performance requirements.

[0114] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0115] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A heat treatment method for a magnetic material, characterized in that, include: In response to the heat treatment signal of the target magnetic material, the material property information of the target magnetic material, the heat treatment requirement information, and the heat treatment furnace property information of the target heat treatment furnace for heat treatment of the target magnetic material are obtained. Determine the material feature vector corresponding to the material property information, the demand feature vector corresponding to the heat treatment demand information, and the heat treatment furnace feature vector corresponding to the heat treatment furnace property information, respectively. The material feature vector, demand feature vector, and heat treatment furnace feature vector are fused to obtain the heat treatment fused feature vector; The heat treatment fusion feature vector is input into a preset processing method prediction model to predict the heat treatment method, thereby obtaining the target heat treatment method corresponding to the target magnetic material. Based on the target heat treatment method, the target heat treatment furnace is controlled to heat treat the target magnetic material to obtain the heat-treated magnetic material. The step of fusing the material feature vector, demand feature vector, and heat treatment furnace feature vector to obtain a heat treatment fused feature vector includes: The process involves determining the principal component feature vectors between every two feature vectors in the material feature vector, demand feature vector, and heat treatment furnace feature vector. This includes: determining a first principal component feature vector between the material feature vector and the demand feature vector, a second principal component feature vector between the material feature vector and the heat treatment furnace feature vector, and a third principal component feature vector between the demand feature vector and the heat treatment furnace feature vector; horizontally concatenating the first, second, and third principal component feature vectors to obtain a concatenated feature vector; determining the transformation coefficients corresponding to the concatenated feature vector; and performing a linear transformation on the concatenated feature vector based on the transformation coefficients to obtain a heat treatment fusion feature vector.

2. The method according to claim 1, characterized in that, The target heat treatment method includes: a heating treatment method, a holding treatment method, and a cooling treatment method. The heating treatment method includes: a heating method, a heating time, and a heating temperature. The holding treatment method includes: a holding method and a holding time. The cooling treatment method includes: a cooling method and a cooling rate. Based on the target heat treatment method, controlling the target heat treatment furnace to heat treat the target magnetic material to obtain the heat-treated magnetic material includes: Based on the heating method, heating time, and heating temperature, the target heat treatment furnace is controlled to heat the target magnetic material to obtain the heat-treated magnetic material. Based on the heat preservation method and heat preservation time, the target heat treatment furnace is controlled to perform heat preservation treatment on the heated magnetic material to obtain the heat-preserved magnetic material. Based on the cooling method and cooling rate, the target heat treatment furnace is controlled to cool the heat-insulated magnetic material to obtain the heat-treated magnetic material.

3. The method according to claim 1, characterized in that, The method further includes: During the heat treatment of the target magnetic material, the heating attribute information of the target heat treatment furnace is obtained, wherein the heating attribute information includes at least one of the following: real-time temperature, real-time vacuum degree, and real-time pressure inside the target heat treatment furnace. Based on the heating attribute information, determine whether the target heat treatment furnace meets the preset alarm conditions; If the target heat treatment furnace meets the preset conditions, an alarm message is generated based on the heating attribute information, and a preset communication tool interface is called to send the alarm message to the monitoring terminal.

4. The method according to claim 1, characterized in that, The process of determining the principal component eigenvector between every two eigenvectors in the material feature vector, demand feature vector, and heat treatment furnace feature vector includes: Construct a feature matrix based on every two feature vectors; The mean of each element in the feature matrix is ​​determined, and each element in the feature matrix is ​​subtracted from the mean of the elements to obtain the centered feature matrix; Determine the covariance matrix corresponding to the centered feature matrix; The covariance matrix is ​​decomposed into eigenvalues ​​to obtain matrix eigenvalues ​​and matrix eigenvectors. Based on the magnitude of the matrix eigenvalues, a preset number of matrix eigenvectors are selected from the matrix eigenvectors, and the preset number of matrix eigenvectors are determined as the principal component eigenvectors between every two eigenvectors.

5. The method according to claim 1, characterized in that, Before acquiring the material property information, heat treatment requirement information, and heat treatment furnace property information of the target magnetic material and the target heat treatment furnace for heat treatment of the target magnetic material, the method further includes: The storage location information corresponding to the target magnetic material is determined, and the different idle heat treatment furnaces in the heat treatment site where the target magnetic material is located are determined, and the furnace attribute information of each idle heat treatment furnace is obtained. Determine the attribute feature vector corresponding to the attribute information of the furnace, and based on the attribute feature vector, perform clustering processing on each idle heat treatment furnace to obtain idle heat treatment furnaces under different clustering categories; Based on the material property information and heat treatment requirement information, a target cluster category that meets the heat treatment requirements is determined from the different cluster categories; The furnace location information of each idle heat treatment furnace under the target cluster category is determined, and the target heat treatment furnace is determined among the idle heat treatment furnaces under the target cluster category based on the storage location information of the target magnetic material and the furnace location information of each idle heat treatment furnace.

6. The method according to claim 5, characterized in that, The process of clustering each idle heat treatment furnace based on the attribute feature vector to obtain idle heat treatment furnaces under different cluster categories includes: Initialize the centroid vectors corresponding to different clusters; Calculate the cosine similarity between each attribute feature vector and the centroid vector corresponding to the different clusters, and classify each idle heat treatment furnace into the different clusters based on the cosine similarity. Based on the attribute feature vectors corresponding to the idle heat treatment furnaces in the different clusters, the updated centroid vectors corresponding to the different clusters are determined. Based on the updated centroid vector, each idle heat treatment furnace is reassigned to a different cluster until the updated centroid vector remains unchanged. The idle heat treatment furnaces that are finally assigned to different clusters are then identified as idle heat treatment furnaces under the different cluster categories.

7. A heat treatment apparatus for magnetic materials, characterized in that, include: The acquisition unit is used to acquire, in response to the heat treatment signal of the target magnetic material, the material property information of the target magnetic material, the heat treatment requirement information, and the heat treatment furnace property information of the target heat treatment furnace for heat treatment of the target magnetic material. The determining unit is used to determine the material feature vector corresponding to the material property information, the demand feature vector corresponding to the heat treatment demand information, and the heat treatment furnace feature vector corresponding to the heat treatment furnace property information, respectively. A fusion unit is used to fuse the material feature vector, demand feature vector, and heat treatment furnace feature vector to obtain a heat treatment fusion feature vector. The fusion of the material feature vector, demand feature vector, and heat treatment furnace feature vector to obtain the heat treatment fusion feature vector includes: determining the principal component feature vector between every two feature vectors in the material feature vector, demand feature vector, and heat treatment furnace feature vector; determining the first principal component feature vector between the material feature vector and the demand feature vector, the second principal component feature vector between the material feature vector and the heat treatment furnace feature vector, and the third principal component feature vector between the demand feature vector and the heat treatment furnace feature vector; horizontally concatenating the first principal component feature vector, the second principal component feature vector, and the third principal component feature vector to obtain a concatenated feature vector; determining the transformation coefficients corresponding to the concatenated feature vector, and performing a linear transformation on the concatenated feature vector based on the transformation coefficients to obtain the heat treatment fusion feature vector. The prediction unit is used to input the heat treatment fusion feature vector into a preset processing method prediction model to predict the heat treatment method and obtain the target heat treatment method corresponding to the target magnetic material. A heat treatment unit is used to control the target heat treatment furnace to heat treat the target magnetic material based on the target heat treatment method, so as to obtain the heat-treated magnetic material.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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