Intelligent distribution method and device for heating furnaces of totally-enclosed vacuum isothermal forging system

Abstract

The invention provides an intelligent distribution method and device for a heating furnace of a totally-closed vacuum isothermal forging system, relates to the field of high-end intelligent device manufacturing, and is applied to the intelligent distribution device of the totally-closed vacuum isothermal forging system. The totally-closed vacuum isothermal forging system further comprises a plurality of heating furnaces in a vacuum environment and an intelligent robot for transferring blanks in the heating furnaces. The method comprises the following steps: acquiring attribute data of a to-be-distributed blank and performance data of an idle heating furnace; based on the attribute data of the to-be-distributed blank, the performance data of the idle heating furnace and a heating furnace distribution model, determining a target heating furnace for correspondingly heating the to-be-distributed blank; and the intelligent robot is controlled to transfer the blanks to be distributed from the originally filled heating furnace to the target heating furnace. The method is suitable for the heating furnace distribution process of the vacuum isothermal forging system and is used for improving the transfer efficiency during blank heating and improving the intelligent degree of the totally-closed vacuum isothermal forging system.

Description

Technical Field

[0001] The present application relates to the field of high-end intelligent device manufacturing, and in particular to a heating furnace intelligent allocation method and device for a fully enclosed vacuum isothermal forging system. Background Art

[0002] Near-isothermal forging means heating the billet and the die to similar temperatures and forging at a lower strain rate.

[0003] Due to the advantages of near-isothermal forging, such as improving material properties, improving processing accuracy and surface quality, and being able to adapt to complex shapes and high strength requirements, current aerospace parts are often forged using near-isothermal forging.

[0004] In traditional near-isothermal forging, since the die cannot be heated to the same high temperature as the billet, the billet temperature drops quickly, which easily leads to the formation of coarse grains in the surface area of ​​the billet, resulting in the inability to achieve superplastic forming of the billet. Summary of the invention

[0005] The present application provides a method and device for intelligently allocating heating furnaces in a fully enclosed vacuum isothermal forging system, which can improve the transfer efficiency when heating the billet, avoid the problem of coarse grains being easily formed in the surface area due to a drop in billet temperature, and can automatically allocate corresponding heating furnaces to different billets, thereby improving the intelligence level of the fully enclosed vacuum isothermal forging system.

[0006] In the first aspect, the present application provides a method for intelligently allocating heating furnaces in a fully enclosed vacuum isothermal forging system, the method being applied to an intelligent allocation device of the fully enclosed vacuum isothermal forging system, the fully enclosed vacuum isothermal forging system also comprising a plurality of heating furnaces in a vacuum environment, and an intelligent robot for transferring billets among the plurality of heating furnaces; the method comprising: obtaining attribute data of the billet to be allocated and performance data of an idle heating furnace; the idle heating furnace being a heating furnace in an idle state among the plurality of heating furnaces; determining a target heating furnace for heating the billet to be allocated based on the attribute data of the billet to be allocated, the performance data of the idle heating furnace, and a heating furnace allocation model; wherein the heating furnace allocation model is used to predict the heating furnace for heating the billet according to the attribute data of the billet and the performance data of the heating furnace; and controlling the intelligent robot to transfer the billet to be allocated from the originally loaded heating furnace to the target heating furnace.

[0007] In the intelligent allocation method of heating furnaces for a fully enclosed vacuum isothermal forging system provided in the present application, the fully enclosed vacuum isothermal forging system includes multiple heating furnaces in a vacuum environment, which can heat the billet in a vacuum environment. In this way, the heating furnace is convenient for directly docking with the billet vacuum forging unit without breaking the vacuum, thereby improving the transfer efficiency, avoiding the formation of coarse grains in the surface area due to the drop in billet temperature, and helping the target billet to achieve superplastic forming.

[0008] In addition, in the intelligent allocation method of heating furnaces for a fully enclosed vacuum isothermal forging system provided in the present application, the intelligent allocation device can determine the target heating furnace for heating the corresponding billet to be allocated based on the attribute data of the billet to be allocated, the performance data of the idle heating furnace, and the heating furnace allocation model. Compared with the traditional method of manually allocating heating furnaces, it can avoid incorrect allocation due to lack of experience or negligence of the staff, provide more scientific and accurate allocation results, reduce interference from human factors, reduce safety risks caused by operational errors, and improve the intelligence level of the fully enclosed vacuum isothermal forging system.

[0009] Optionally, the attribute data includes: the size, material, weight, and heating process requirements of the blank; the performance data includes: the power, operating time, heating efficiency, and airtightness reduction record of the heating furnace during the heating process.

[0010] Optionally, the method further includes: updating the target heating furnace from an idle state to an occupied state.

[0011] In this way, when allocating heating furnaces for other billets, the intelligent allocation device can avoid the heating furnaces that have already allocated billets, thereby reducing the amount of calculation and avoiding conflicts in heating furnace allocation.

[0012] Optionally, the method also includes: counting the predicted frequencies of multiple heating furnaces predicted by the heating furnace allocation model as target heating furnaces within a preset statistical period; and outputting maintenance reminder information for the idle heating furnaces based on the existence of idle heating furnaces among the multiple heating furnaces whose predicted frequencies are less than a frequency threshold.

[0013] In the intelligent allocation method for heating furnaces of a fully enclosed vacuum isothermal forging system provided in the present application, the intelligent allocation device can count the predicted frequencies of multiple heating furnaces predicted as target heating furnaces by the heating furnace allocation model within a preset statistical period, and idle heating furnaces with a low predicted frequency or even 0 may have defects that require maintenance. The intelligent allocation device can output maintenance reminder information for idle heating furnaces based on the existence of idle heating furnaces with a predicted frequency less than a frequency threshold among the multiple heating furnaces, thereby promptly reminding the staff to maintain the idle heating furnaces, which can prevent potential failures and extend the equipment service life of the heating furnaces.

[0014] Optionally, the heating furnace allocation model is trained in the following manner: obtaining a training sample set; the training sample set includes multiple training samples; each training sample includes attribute data of a training blank, performance data of a training heating furnace, and a heating furnace label; and training a preset neural network based on the training sample set to obtain a heating furnace allocation model.

[0015] Optionally, the preset neural network includes an error back propagation BP neural network or a convolutional neural network CNN.

[0016] In the second aspect, the present application provides an intelligent allocation device for heating furnaces of a fully enclosed vacuum isothermal forging system, the fully enclosed vacuum isothermal forging system also including multiple heating furnaces in a vacuum environment, and an intelligent robot for transferring billets among the multiple heating furnaces; the intelligent allocation device includes: an acquisition module and a processing module; the acquisition module acquires the attribute data of the billet to be allocated and the performance data of the idle heating furnace; the idle heating furnace is a heating furnace that is in an idle state among the multiple heating furnaces; the processing module determines the identification of the target heating furnace corresponding to the heating of the billet to be allocated based on the attribute data of the billet to be allocated, the performance data of the idle heating furnace, and the heating furnace allocation model; wherein the heating furnace allocation model is used to predict the identification of the heating furnace corresponding to the heating of the billet based on the attribute data of the billet and the performance data of the heating furnace; the intelligent robot is controlled to transfer the billet to be allocated from the originally loaded heating furnace to the target heating furnace.

[0017] Optionally, the attribute data includes: the size, material, weight, and heating process requirements of the blank; the performance data includes: the power, operating time, heating efficiency, and airtightness reduction record of the heating furnace during the heating process.

[0018] Optionally, the processing module is further used to update the target heating furnace from an idle state to an occupied state.

[0019] Optionally, the acquisition module is also used to count the predicted frequencies of multiple heating furnaces predicted as target heating furnaces by the heating furnace allocation model within a preset statistical period; the processing module is also used to output maintenance reminder information of idle heating furnaces based on the existence of idle heating furnaces among the multiple heating furnaces whose predicted frequencies are less than a frequency threshold.

[0020] Optionally, the acquisition module is also used to acquire a training sample set; the training sample set includes multiple training samples; each training sample includes attribute data of the training blank, performance data of the training heating furnace, and a heating furnace label; the processing module is also used to train a preset neural network based on the training sample set to obtain a heating furnace allocation model.

[0021] Optionally, the preset neural network includes an error back propagation BP neural network or a convolutional neural network CNN.

[0022] In the third aspect, the present application provides an intelligent distribution device for heating furnaces of a fully enclosed vacuum isothermal forging system, the fully enclosed vacuum isothermal forging system also including multiple heating furnaces in a vacuum environment, and an intelligent robot for transferring billets among the multiple heating furnaces; the intelligent distribution device includes: a processor and a memory; the memory stores instructions executable by the processor; when the processor is configured to execute the instructions, the intelligent distribution device implements the method described in the first aspect above.

[0023] In a fourth aspect, the present application provides a readable storage medium, comprising: software instructions; when the software instructions are executed in an intelligent distribution device, the intelligent distribution device implements the method described in the first aspect above.

[0024] In a fifth aspect, the present application provides a computer program product, comprising: computer instructions; when the computer instructions are executed in an intelligent distribution device, the intelligent distribution device implements the method described in the first aspect above.

[0025] The beneficial effects of the second to fifth aspects mentioned above can be referred to the first aspect and will not be elaborated on again. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 A schematic diagram of the composition of a fully enclosed vacuum isothermal forging system provided in an embodiment of the present application, viewed from above; Figure 2 A schematic diagram of a flow chart of a heating furnace intelligent allocation method for a fully enclosed vacuum isothermal forging system provided in an embodiment of the present application; Figure 3 A schematic diagram of the composition of a heating furnace intelligent distribution device for a fully enclosed vacuum isothermal forging system provided in an embodiment of the present application; Figure 4 A schematic diagram of the composition of another heating furnace intelligent distribution device of a fully enclosed vacuum isothermal forging system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0029] It should be noted that, in the embodiments of the present application, words such as "exemplarily" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplarily" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily" or "for example" is intended to present related concepts in a specific way.

[0030] In order to facilitate the clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, the words "first", "second", etc. are used to distinguish the same items or similar items with basically the same functions and effects. Those skilled in the art can understand that the words "first", "second", etc. are not limiting the quantity and execution order.

[0031] Near-isothermal forging means heating the billet and the die to similar temperatures and forging at a lower strain rate.

[0032] Due to the advantages of near-isothermal forging, such as improving material properties, improving processing accuracy and surface quality, and being able to adapt to complex shapes and high strength requirements, current aerospace parts are often forged using near-isothermal forging.

[0033] In traditional near-isothermal forging, since the die cannot be heated to the same high temperature as the billet, the billet temperature drops quickly, which easily leads to the formation of coarse grains in the surface area of ​​the billet, resulting in the inability to achieve superplastic forming of the billet.

[0034] Based on this, the embodiment of the present application provides a method and device for intelligently allocating heating furnaces in a fully enclosed vacuum isothermal forging system, which can heat the mold to the same high temperature as the billet and improve the transfer efficiency when the billet is heated, thereby avoiding the problem of coarse grains being easily formed in the surface area due to the drop in billet temperature, thereby solving the problem of superplastic forming of the billet and the mold under isothermal conditions, and can automatically allocate corresponding heating furnaces to different billets, thereby improving the intelligence level of the fully enclosed vacuum isothermal forging system.

[0035] The following is an introduction with reference to the accompanying drawings.

[0036] Figure 1 This is a schematic diagram of the top view of the fully enclosed vacuum isothermal forging system provided in the embodiment of the present application. Figure 1 As shown, the fully enclosed vacuum isothermal forging system may include: a billet vacuum heating unit 100 , a billet vacuum forging unit 200 , a vacuum pumping unit 300 , and an intelligent dispensing device 400 .

[0037] The vacuum heating unit 100 includes a vacuum tunnel 11. A plurality of heating furnaces 12 ( Figure 1 In the figure, three heating furnaces on each side, namely, heating furnace No. 1 1, heating furnace No. 2, heating furnace No. 3, heating furnace No. 4, heating furnace No. 5, and heating furnace No. 6, are shown as examples). A material rack 14 for placing blanks 13 is provided in each heating furnace 12.

[0038] Each heating furnace 12 may be provided with a heating device ( Figure 1(not shown in the figure), the heating device can be used to heat the blank 13 placed in the material rack 14.

[0039] In some embodiments, the heating device in the heating furnace 12 may have a communication connection (eg, wired connection or wireless connection) with the intelligent distribution device 400 , and the heating device may receive a driving signal from the intelligent distribution device 400 , thereby driving the heating device to start heating or end heating.

[0040] A track 15 is laid along the entire length of the vacuum tunnel 11 , and an intelligent robot 16 for clamping the blank 13 is placed on the track 15 . The intelligent robot 16 can slide back and forth along the track 15 .

[0041] Each heating furnace 12 is provided with a sliding gate 17 that can be opened and closed on the side wall of the vacuum tunnel 11 (or the side close to the intelligent robot 16 ).

[0042] In some embodiments, the sliding gate 17 may be provided with a driving device ( Figure 1 The driving device may have a communication connection (e.g., a wired connection or a wireless connection) with the intelligent dispensing device 400, and the driving device may be used to receive a driving signal from the intelligent dispensing device 400, thereby driving the sliding gate 17 to open (e.g., open to a certain degree or fully open) or close.

[0043] As an example, the driving device may include a reduction device and a driving motor, the driving device includes a driving gear and a driven rack, and an output gear is provided on the output shaft of the driving motor. The driving gear of the driving device meshes with the output gear of the driving motor, the driven rack is fixed on the sliding gate 17, and the driving gear meshes with the driven rack. The driving motor can be used to receive a driving signal from the intelligent dispensing device 400, and rotate forward or reverse according to the driving signal, thereby driving the sliding gate 17 to open or close through the driving device.

[0044] Each heating furnace 12 is also provided with an openable furnace door 12a on the outside (or the side away from the intelligent robot 16). When loading materials, the staff can place the blanks 13 from the outside on the material rack 14 of each heating furnace 12 through the furnace door 12a.

[0045] The blank vacuum forging unit 200 may include a forging machine 21 , a vacuum forging chamber 22 , and a connecting gate 23 .

[0046] After the blank 13 is heated in the heating furnace 12 , it can be taken out from the heating furnace 12 by the intelligent robot 16 and clamped to the connecting gate 23 . The intelligent robot 16 can open the connecting gate 23 , load the blank 13 into the mold in the vacuum forging chamber 22 , and then forge it by the forging press 21 .

[0047] In some embodiments, a heating device may be further provided in the vacuum forging chamber 22 , and the heating device may heat the mold in the vacuum forging chamber 22 , and the heating temperature of the mold is the same as the heating temperature of the blank 13 .

[0048] The vacuum unit 300 may include a Roots pump and a diffusion pump, and is respectively connected to each heating furnace 12 , the vacuum tunnel 11 and the vacuum forging chamber 22 through vacuum pipes.

[0049] The vacuum unit 300 can be used to vacuum each heating furnace 12, vacuum tunnel 11 and vacuum forging chamber 22. In this case, after the vacuum unit 300 vacuums each heating furnace 12 and vacuum tunnel 11, each heating furnace 12 can also be understood to be in a vacuum environment. The opening and closing of the sliding gate 17 will not break the vacuum of the heating furnace 12, and the opening of the furnace door 12a will break the vacuum of the heating furnace 12. Therefore, after the heating furnace 12 and the vacuum tunnel 11 are vacuumed, if the furnace door 12a of a heating furnace 12 needs to be opened for loading or unloading, the sliding gate 17 needs to be closed first to prevent the vacuum tunnel 11 and other heating furnaces 12 from breaking the vacuum.

[0050] In some embodiments, the vacuum unit 300 may further include a vacuum sensor, which may be used to measure the vacuum degree at each heating furnace 12 , vacuum tunnel 11 , and vacuum forging chamber 22 .

[0051] The intelligent allocation device 400 can be used to automatically control all operations of the vacuum heating unit 100, the billet vacuum forging unit 200, and the vacuum unit 300, and allocate corresponding heating furnaces to different billets. The specific process can be referred to the intelligent allocation method of heating furnaces for a fully enclosed vacuum isothermal forging system provided in the following embodiment, which will not be repeated here.

[0052] The execution subject of the intelligent allocation method for the heating furnace of the fully enclosed vacuum isothermal forging system provided in the embodiment of the present application is an intelligent allocation device (such as the above-mentioned intelligent allocation device 400). Optionally, the execution subject of the intelligent allocation method for the heating furnace of the fully enclosed vacuum isothermal forging system may also be a processor (such as a central processing unit (CPU)) in the aforementioned intelligent allocation device; or, the execution subject may also be a software system installed in the aforementioned intelligent allocation device for executing the intelligent allocation method; or, the execution subject may also be a functional module in the aforementioned intelligent allocation device for executing the intelligent allocation method, etc. The embodiment of the present application does not limit this.

[0053] For the sake of simplicity, the following will take the execution body of the heating furnace intelligent allocation method of the fully enclosed vacuum isothermal forging system provided in the embodiment of the present application as an example of an intelligent allocation device.

[0054] Figure 2 This is a flow chart of the intelligent allocation method for the heating furnace of the fully enclosed vacuum isothermal forging system provided in the embodiment of the present application. Figure 2 As shown, the method includes the following steps: S101, acquiring attribute data of the blank to be allocated and performance data of the idle heating furnace.

[0055] The idle heating furnace is an idle heating furnace among the multiple heating furnaces. For example, a pressure sensor is also provided on the rack in each heating furnace, and the intelligent allocation device can obtain the pressure signal collected by the pressure sensor, and determine whether there are blanks placed on the rack based on the pressure signal, and determine the heating furnace with blanks placed on the rack as occupied, and determine the heating furnace without blanks placed on the rack as idle.

[0056] Optionally, the attribute data may include: size, material, weight, and heating process requirements of the blank.

[0057] Optionally, the performance data may include: power of the heating furnace, operating time (or time in operation), heating efficiency, and airtightness reduction records during the heating process.

[0058] In a possible implementation, the intelligent distribution device may include an input / output interface, which may include a mouse, a keyboard, or a touch screen, etc. The intelligent distribution device may receive attribute data of the to-be-distributed blanks input by a staff member through the input / output interface.

[0059] In another possible implementation, the intelligent allocation device may also be connected to other devices or platforms for communication, and the intelligent allocation device may receive attribute data of the to-be-allocated blanks sent from other devices or platforms.

[0060] In a possible implementation, the power of the heating furnace can be directly determined according to the rated power marked on the nameplate of the heating furnace, and the intelligent distribution device can receive the rated power input by the staff as the power of the heating furnace.

[0061] In a possible implementation, the intelligent distribution device may maintain an operation log of each heating furnace, and the operation log may include the operation time of the heating furnace, the heating efficiency, and the record of the decrease in airtightness during the heating process.

[0062] For example, the intelligent distribution device can control the heating furnace to start heating or stop heating. When controlling a heating furnace to start heating, the intelligent distribution device can simultaneously start a timer to record the duration from starting heating to stopping heating of the heating furnace, thereby obtaining the duration of one heating and accumulating the duration of multiple heatings to obtain the operating time of the heating furnace.

[0063] For another example, the intelligent distribution device can also record the actual cumulative energy consumption within a period of time (such as a week, a month, or a quarter) and the theoretical cumulative energy consumption of the executed heating tasks, and calculate the heating efficiency based on the actual cumulative energy consumption and the theoretical cumulative energy consumption.

[0064] For another example, a vacuum sensor may be provided in each heating furnace, and the intelligent distribution device may determine whether the air tightness decreases during the heating process according to the changes in the vacuum sensor detection data during the heating process of the heating furnace, and obtain a record of the decrease in air tightness during the heating process.

[0065] S102 : Determine a target heating furnace for heating the to-be-allocated blank based on the attribute data of the to-be-allocated blank, the performance data of the idle heating furnaces, and the heating furnace allocation model.

[0066] The heating furnace allocation model is used to predict the heating furnace (or the identification of the heating furnace) corresponding to the heating blank according to the property data of the blank and the performance data of the heating furnace. The specific training process of the heating furnace allocation model can be referred to the following embodiment, which will not be repeated here.

[0067] As an example, the intelligent allocation device may input the attribute data of the to-be-allocated blanks and the performance data of the idle heating furnaces into the heating furnace allocation model, obtain the identification of the target heating furnace output by the heating furnace allocation model, and determine the target heating furnace based on the identification.

[0068] S103, controlling the intelligent robot to transfer the blanks to be distributed from the originally loaded heating furnace to the target heating furnace.

[0069] For example, the intelligent distributing device can control the sliding gate of the original heating furnace and the sliding gate of the target heating furnace to open, and then control the intelligent robot to transfer the blanks to be distributed from the original loaded heating furnace to the target heating furnace.

[0070] In the intelligent allocation method of heating furnaces for a fully enclosed vacuum isothermal forging system provided in an embodiment of the present application, the fully enclosed vacuum isothermal forging system includes multiple heating furnaces in a vacuum environment, which can heat the billet in a vacuum environment. In this way, the heating furnace is convenient for directly docking with the billet vacuum forging unit without breaking the vacuum, thereby improving the transfer efficiency, avoiding the formation of coarse grains in the surface area due to the drop in billet temperature, and helping the target billet to achieve superplastic forming.

[0071] In addition, in the intelligent allocation method of heating furnaces for a fully enclosed vacuum isothermal forging system provided in an embodiment of the present application, the intelligent allocation device can determine the target heating furnace for heating the corresponding billet to be allocated based on the attribute data of the billet to be allocated, the performance data of the idle heating furnace, and the heating furnace allocation model. Compared with the traditional method of manually allocating heating furnaces, it can avoid incorrect allocation due to lack of experience or negligence of the staff, provide more scientific and accurate allocation results, reduce interference from human factors, reduce safety risks caused by operational errors, and improve the intelligence level of the fully enclosed vacuum isothermal forging system.

[0072] In some possible embodiments, after transferring the to-be-allocated blanks to the target heating furnace, or after determining the target heating furnace and before performing the transfer action, the intelligent allocation device may also update the target heating furnace from an idle state to an occupied state.

[0073] In this way, when allocating heating furnaces for other billets, the intelligent allocation device can avoid the heating furnaces that have already allocated billets, thereby reducing the amount of calculation and avoiding conflicts in heating furnace allocation.

[0074] In some possible embodiments, the intelligent allocation device can also count the frequency of the heating furnace being allocated, and determine the idle heating furnace with less allocation frequency for maintenance reminder. In this case, the method can also include the following steps: Step 1a, counting the predicted frequencies of multiple heating furnaces being predicted as target heating furnaces by the heating furnace allocation model within a preset statistical period.

[0075] The preset statistical period can be preset in the intelligent distribution device by the staff. For example, the preset statistical period can be set to: one day, one week, one month, or three months, etc. The embodiment of the present application does not limit the specific duration of the preset statistical period.

[0076] Step 2a: Based on the existence of an idle heating furnace among the multiple heating furnaces whose predicted frequency is less than a frequency threshold, output maintenance prompt information of the idle heating furnace.

[0077] The frequency threshold may also be preset in the intelligent distribution device by the staff. For example, the frequency threshold may be set to three times, five times, or ten times, etc. The embodiment of the present application does not limit the specific value of the frequency threshold.

[0078] Optionally, when the predicted frequency of the heating furnace is greater than the frequency threshold, the intelligent allocation device may not output maintenance reminder information.

[0079] It should be noted that, when the predicted frequency of the heating furnace is equal to the frequency threshold, the intelligent allocation device may output maintenance reminder information of the heating furnace, or may not output maintenance reminder information of the heating furnace. The embodiment of the present application does not limit this.

[0080] As an example, the intelligent distribution device may be connected to a terminal device of a staff member for communication, and the intelligent distribution device may send maintenance reminder information of limiting the heating furnace to the terminal device of the staff member.

[0081] As another example, a status indicator light may be provided at each heating furnace, and the intelligent allocation device may control the status indicator light at an idle heating furnace to display a preset light color to indicate that the idle heating furnace requires maintenance.

[0082] In the intelligent allocation method for heating furnaces of a fully enclosed vacuum isothermal forging system provided in an embodiment of the present application, the intelligent allocation device can count the predicted frequencies of multiple heating furnaces predicted as target heating furnaces by the heating furnace allocation model within a preset statistical period, and idle heating furnaces with a low predicted frequency or even 0 may have defects that require maintenance. The intelligent allocation device can output maintenance reminder information for idle heating furnaces based on the existence of idle heating furnaces with a predicted frequency less than a frequency threshold among the multiple heating furnaces, thereby promptly reminding the staff to maintain the idle heating furnaces, which can prevent potential failures and extend the equipment service life of the heating furnaces.

[0083] In some possible embodiments, before the above S102, the intelligent allocation device may also obtain a trained heating furnace allocation model.

[0084] In one possible implementation, the intelligent allocation device can directly obtain the trained heating furnace allocation model from other devices.

[0085] For example, the intelligent allocation device can obtain the trained heating furnace allocation model from other devices by downloading or transferring from an intermediate storage medium.

[0086] In another possible implementation, the intelligent allocation device may also train a preset neural network based on a training sample set to obtain a heating furnace allocation model. In this case, the method may also include the following steps: Step 1b: Obtain a training sample set.

[0087] The training sample set includes a plurality of training samples, each of which includes attribute data of a training blank, performance data of a training heating furnace, and a heating furnace label. The heating furnace label can also be understood as an identifier of a heating furnace corresponding to heating the training blank in the case of the aforementioned attribute data of the training blank and the performance data of the training heating furnace. The training heating furnace may be the same as the heating furnace of the fully enclosed vacuum isothermal forging system, or it may be a heating furnace other than the heating furnace of the fully enclosed vacuum isothermal forging system, and the embodiments of the present application are not limited to this.

[0088] Step 2b: training the preset neural network based on the training sample set to obtain a heating furnace allocation model.

[0089] Optionally, the preset neural network may be a back-propagation (BP) neural network or a convolutional neural network (CNN), etc. The embodiment of the present application does not limit the specific type of the preset neural network.

[0090] For example, as described above, the training sample set may include multiple training samples. In this case, the intelligent allocation device may input one or more training samples into a preset neural network for training each time, obtain a predicted label output by the preset neural network, calculate a loss function based on the heating furnace label in the training sample and the preset label, and adjust the parameters in the preset neural network based on the loss function, and repeat the training until a preset iteration stop condition is reached.

[0091] Optionally, the preset iteration stop condition may include: the number of times the intelligent allocation device inputs the training sample into the preset neural network reaches a number threshold, and / or the error between the predicted label output by the preset neural network and the heating furnace label in the training sample is less than an error threshold.

[0092] Among them, the number threshold can be preset in the intelligent allocation device by the staff. For example, the number threshold can be set to 500 times, 1000 times, or 5000 times, etc. The embodiment of the present application does not limit the specific value of the number threshold. The error threshold can be preset in the intelligent allocation device by the staff. For example, the error threshold can be set to (the proportion of prediction errors) 5%, 10%, or 15%, etc. The embodiment of the present application does not limit the specific number of the error threshold.

[0093] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the method. In order to realize the above functions, the intelligent distribution device may include hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical objectives can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0094] In an exemplary embodiment, the present application also provides an intelligent distribution device for a heating furnace of a fully enclosed vacuum isothermal forging system. Figure 3 A schematic diagram of the composition of the heating furnace intelligent distribution device of the fully enclosed vacuum isothermal forging system provided in the embodiment of the present application. Figure 3 As shown, the intelligent distribution device includes: an acquisition module 301 and a processing module 302 .

[0095] The acquisition module 301 is used to acquire the attribute data of the blank to be allocated and the performance data of the idle heating furnace; the idle heating furnace is a heating furnace in an idle state among the multiple heating furnaces; The processing module 302 is used to determine the target heating furnace that heats the blank to be allocated based on the attribute data of the blank to be allocated, the performance data of the idle heating furnace, and the heating furnace allocation model; wherein the heating furnace allocation model is used to predict the heating furnace that heats the blank according to the attribute data of the blank and the performance data of the heating furnace; and control the intelligent robot to transfer the blank to be allocated from the originally loaded heating furnace to the target heating furnace.

[0096] In some possible embodiments, the attribute data includes: the size, material, weight, and heating process requirements of the blank; the performance data includes: the power, operating time, heating efficiency, and airtightness reduction record of the heating furnace during the heating process.

[0097] In some other possible embodiments, the processing module 302 is further configured to update the target heating furnace from an idle state to an occupied state.

[0098] In some other possible embodiments, the acquisition module 301 is also used to count the predicted frequencies of multiple heating furnaces predicted as target heating furnaces by the heating furnace allocation model within a preset statistical period; the processing module 302 is also used to output maintenance reminder information of idle heating furnaces based on the existence of idle heating furnaces among the multiple heating furnaces whose predicted frequencies are less than a frequency threshold.

[0099] In some other possible embodiments, the acquisition module 301 is also used to acquire a training sample set; the training sample set includes multiple training samples; each training sample includes attribute data of the training blank, performance data of the training heating furnace, and a heating furnace label; the processing module 302 is also used to train a preset neural network based on the training sample set to obtain a heating furnace allocation model.

[0100] In some other possible embodiments, the preset neural network includes an error back propagation BP neural network or a convolutional neural network CNN.

[0101] It should be noted that Figure 3 The division of modules in the example is schematic and is only a logical function division. There may be other division methods in actual implementation. For example, two or more functions may be integrated into one processing module. The above integrated modules may be implemented in the form of hardware or in the form of software functional units.

[0102] In an exemplary embodiment, the present application also provides an intelligent distribution device for a heating furnace of a fully enclosed vacuum isothermal forging system. Figure 4 A schematic diagram of the composition of another heating furnace intelligent distribution device of a fully enclosed vacuum isothermal forging system provided in an embodiment of the present application. Figure 4 As shown, the intelligent distribution device includes: a processor 10 , a memory 20 , a communication line 30 , a communication interface 40 , and an input / output interface 50 .

[0103] The processor 10 , the memory 20 , the communication interface 40 and the input / output interface 50 may be connected via a communication line 30 .

[0104] The processor 10 is used to execute the instructions stored in the memory 20 to implement the intelligent allocation method of the heating furnace of the fully enclosed vacuum isothermal forging system provided in the above-mentioned embodiment of the present application. The processor 10 can be a CPU, a general-purpose processor network processor (network processor, NP), a digital signal processor (digital signal processing, DSP), a microprocessor, a microcontroller (micro control unit, MCU) / single chip microcomputer (single chip microcomputer) / single chip microcomputer, a programmable logic device (programmable logic device, PLD) or any combination thereof. The processor 10 can also be any other device with processing functions, such as a circuit, a device or a software module, which is not limited in the embodiments of the present application. In one example, the processor 10 may include one or more CPUs, such as Figure 3As an optional implementation, the intelligent allocation device may include multiple processors, for example, in addition to the processor 10, it may also include a processor 60 ( Figure 3 ) is shown by the dotted line as an example.

[0105] The memory 20 is used to store instructions. For example, the instructions may be computer programs. Optionally, the memory 20 may be a read-only memory (ROM) or other types of static storage devices that can store static information and / or instructions, or a random access memory (RAM) or other types of dynamic storage devices that can store information and / or instructions, or an electrically erasable programmable read-only memory (EEPROM), a CD-ROM or other optical disk storage, an optical disk storage (including a compressed optical disk, a laser disk, an optical disk, a digital versatile disk, a Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage devices, etc., and the embodiments of the present application are not limited to this.

[0106] It should be noted that the memory 20 may exist independently of the processor 10, or may be integrated with the processor 10. The memory 20 may be located inside the intelligent distribution device, or may be located outside the intelligent distribution device, which is not limited in the embodiment of the present application.

[0107] The communication line 30 is used to transmit information between the various components included in the intelligent distribution device.

[0108] The communication interface 40 is used to communicate with other devices or other communication networks. The other communication networks may be Ethernet, radio access network (RAN), wireless local area network (WLAN), etc. The communication interface 40 may be a module, a circuit, a transceiver or any device capable of achieving communication.

[0109] The input / output interface 50 is used to realize human-computer interaction between the user and the intelligent distribution device, for example, to realize action interaction, text interaction or voice interaction between the user and the intelligent distribution device.

[0110] For example, the input / output interface 50 may be a physical keyboard or a touch screen, etc. The physical keyboard or the touch screen may be used to implement action interaction or text interaction between the user and the intelligent dispensing device.

[0111] It should be noted that Figure 4The structure shown in the figure does not constitute a limitation on the intelligent distribution device. Figure 4 In addition to the components shown, the intelligent dispensing device may include more or fewer components than shown, or a combination of certain components, or a different arrangement of components.

[0112] In an exemplary embodiment, the present application also provides a computer program product, which includes computer instructions; when the computer instructions are executed in the intelligent distribution device, the intelligent distribution device implements the method described in the aforementioned method embodiment.

[0113] In an exemplary embodiment, the present application also provides a readable storage medium, which includes: software instructions. When the software instructions are executed in the intelligent distribution device, the intelligent distribution device implements the method described in the aforementioned method embodiment. The computer-readable storage medium may be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0114] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using a software program, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer-executable instructions. When the computer-executable instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer-executable instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer-executable instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0115] Although the present application is described herein in conjunction with various embodiments, in the process of implementing the claimed application, those skilled in the art may understand and implement other changes to the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "one" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in a claim. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0116] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely exemplary illustrations of the present application as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

[0117] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A heating furnace intelligent allocation method for a fully enclosed vacuum isothermal forging system, characterized in that: The method is applied to an intelligent distribution device of a fully enclosed vacuum isothermal forging system, wherein the fully enclosed vacuum isothermal forging system further comprises a plurality of heating furnaces in a vacuum environment and an intelligent robot for transferring blanks in the plurality of heating furnaces; The method comprises: Acquire the attribute data of the blank to be allocated and the performance data of the idle heating furnace; the idle heating furnace is a heating furnace in an idle state among the multiple heating furnaces; Based on the attribute data of the to-be-allocated blank, the performance data of the idle heating furnace, and the heating furnace allocation model, a target heating furnace corresponding to heating the to-be-allocated blank is determined; wherein the heating furnace allocation model is used to predict the heating furnace corresponding to heating the blank according to the attribute data of the blank and the performance data of the heating furnace; The intelligent robot is controlled to transfer the blanks to be distributed from the originally loaded heating furnace to the target heating furnace.

2. The method according to claim 1, characterized in that The attribute data include: size, material, weight, and heating process requirements of the blank; The performance data include: power, operation time, heating efficiency of the heating furnace, and airtightness reduction records during the heating process.

3. The method according to claim 1, characterized in that The method further comprises: The target heating furnace is updated from an idle state to an occupied state.

4. The method according to claim 1, characterized in that The method further comprises: Counting the predicted frequencies of the plurality of heating furnaces being predicted as target heating furnaces by the heating furnace allocation model within a preset statistical period; Based on the existence of an idle heating furnace among the plurality of heating furnaces whose predicted frequency is less than a frequency threshold, maintenance prompt information of the idle heating furnace is output.

5. The method according to claim 1, characterized in that The heating furnace allocation model is trained in the following way: Acquire a training sample set; the training sample set includes a plurality of training samples; each training sample includes attribute data of a training blank, performance data of a training heating furnace, and a heating furnace label; The preset neural network is trained based on the training sample set to obtain the heating furnace allocation model.

6. The method according to claim 5, characterized in that The preset neural network includes an error back propagation BP neural network or a convolutional neural network CNN.

7. An intelligent distribution device for a heating furnace of a fully enclosed vacuum isothermal forging system, characterized in that: The fully enclosed vacuum isothermal forging system also includes a plurality of heating furnaces in a vacuum environment and an intelligent robot for transferring blanks in the plurality of heating furnaces; The intelligent distribution device comprises: an acquisition module and a processing module; The acquisition module acquires the attribute data of the blank to be allocated and the performance data of the idle heating furnace; the idle heating furnace is a heating furnace in an idle state among the multiple heating furnaces; The processing module determines the identification of the target heating furnace corresponding to the heating of the blank to be allocated based on the attribute data of the blank to be allocated, the performance data of the idle heating furnace, and the heating furnace allocation model; wherein the heating furnace allocation model is used to predict the identification of the heating furnace corresponding to the heating of the blank according to the attribute data of the blank and the performance data of the heating furnace; and controls the intelligent robot to transfer the blank to be allocated from the originally loaded heating furnace to the target heating furnace.

8. An intelligent distribution device for a heating furnace of a fully enclosed vacuum isothermal forging system, characterized in that: The fully enclosed vacuum isothermal forging system also includes a plurality of heating furnaces in a vacuum environment and an intelligent robot for transferring blanks in the plurality of heating furnaces; The intelligent distribution device includes: a processor and a memory; The memory stores instructions executable by the processor; When the processor is configured to execute the instructions, the intelligent distribution device implements the method according to any one of claims 1-6.

9. A readable storage medium, characterized in that: include: Software instructions; When the software instructions are executed in the intelligent distribution device, the intelligent distribution device is enabled to implement the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that include: Computer instructions; When the computer instructions are executed in the intelligent distribution device, the intelligent distribution device is enabled to implement the method according to any one of claims 1 to 6.

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