Automatic hot-pressing tank discharging method and device, computer device and readable storage medium

Abstract

The embodiment of the application provides a kind of hot pressing tank automatic tank arrangement method, device, computer equipment and readable storage medium, it is related to neural network field, by correcting the temperature data of the collected parts, and draw the temperature curve of part, calculate the target value and eigenvalue of temperature curve parameter, establish temperature curve parameter module, by temperature curve parameter prediction model, the temperature curve parameter prediction of current pre-put parts and already enter tank part is carried out, corresponding temperature change curve is obtained;According to the temperature change curve, the temperature fitness and action space fitness of the current pre-put parts are calculated, and the comprehensive fitness of the current pre-put parts is obtained;The part with the maximum comprehensive fitness is selected to join the tank arrangement scheme, and the list of parts to be arranged is updated until the list of parts to be arranged is empty, the automatic tank arrangement is ended and the final tank arrangement scheme is output, the prediction accuracy of the temperature change of the pre-put parts is improved, the verification of the rationality of the part combination is realized, and the efficiency of the automatic tank arrangement is improved.

Description

Technical Field

[0001] This invention relates to the field of neural network technology, and in particular to an automatic autoclave discharge method, apparatus, computer equipment, and readable storage medium. Background Technology

[0002] In the parts manufacturing process, the autoclave is a key piece of equipment in the curing and molding process of composite materials. Multiple parts using the same curing procedure are simultaneously placed into the autoclave for heating and pressurization to complete the curing and molding process. Therefore, developing an autoclave loading and unloading plan in advance can not only greatly improve the utilization rate of the autoclave and increase production efficiency, but also allow for the rational allocation of personnel to make full use of production resources and reduce production and operating costs.

[0003] Current automated autoclave racking methods simplify the autoclave space into a plane, abstracting it as a rectangle. Parts are then abstracted as small rectangular blocks. A two-dimensional packing algorithm based on motion space is used to place as many small rectangular blocks as possible within the autoclave rectangle, maximizing the filling of prefabricated rectangular components and improving autoclave utilization and production scheduling efficiency. However, in actual production, this racking scheme is not directly adopted but requires evaluation by experienced workers to determine the rationality of the part combinations and placement. Manual adjustments are then made to arrive at a usable racking scheme, a process that is both time-consuming and requires highly experienced workers. Therefore, existing automated racking methods need to be optimized to enable automatic evaluation and adjustment. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to overcome the shortcomings of the prior art and provide an automatic autoclave discharge method, apparatus, computer equipment and readable storage medium.

[0005] This invention provides the following technical solution:

[0006] In a first aspect, this disclosure provides an automatic tank discharge method for an autoclave, the method comprising:

[0007] Collect component temperature data transmitted by each sensor and correct any abnormal component temperature data;

[0008] A temperature curve is plotted based on the corrected part temperature data. The temperature curve is then converted into a polynomial through polynomial fitting. The target value of the temperature curve parameter prediction model is then calculated using the polynomial.

[0009] The temperature curve parameter prediction model is established based on the characteristic values ​​and target values ​​in the temperature curve parameter prediction model.

[0010] The temperature curve parameter prediction model is used to predict the temperature curve parameters of the currently pre-placed parts and the parts already in the tank, and the corresponding temperature change curves are calculated.

[0011] Based on the temperature change curve, calculate the temperature adaptability and motion space adaptability of the currently pre-placed part to obtain the overall adaptability of the currently pre-placed part;

[0012] The part with the highest overall adaptability is selected and added to the tank arrangement scheme, and the list of parts to be arranged is updated until the list of parts to be arranged is empty. The automatic tank arrangement ends and the final tank arrangement scheme is output.

[0013] According to a specific embodiment disclosed in this application, the step of collecting component temperature data transmitted by each sensor and correcting abnormal component temperature data includes:

[0014] Collect part temperature data transmitted by each sensor, the part temperature data including tooling placement information;

[0015] The average value correction method is used to correct the part temperature data that is less than the abnormal time interval threshold, and the part temperature data that is greater than or equal to the abnormal time interval threshold is discarded.

[0016] According to a specific embodiment disclosed in this application, the step of establishing the temperature curve parameter prediction model based on each feature value and the target value in the temperature curve parameter prediction model includes:

[0017] Calculate the correlation coefficient between each feature in the temperature curve parameter prediction model and the target value, and select preset feature values ​​based on the correlation coefficient;

[0018] The preset feature value is used as the number of nodes in the input layer of the neural network, and the number of coefficients of the polynomial is used as the number of nodes in the output layer of the neural network.

[0019] The number of nodes and layers in the hidden layer are randomly combined by random search, and the model error value is calculated using cross-validation. The number of nodes and layers in the hidden layer with the smallest model error value is selected to establish the temperature curve parameter prediction model.

[0020] According to a specific embodiment disclosed in this application, before the step of predicting the temperature curve parameters of the currently pre-placed parts and the parts already in the tank using the temperature curve parameter prediction model, and calculating the corresponding temperature change curves, the method further includes:

[0021] The heat melting of the pre-placed part, the heat melting of the tooling, the distance of the tooling from the tank door, the distance from the central axis of the autoclave, the heating rate of the autoclave, the cooling rate, and the isothermal time are input into the temperature curve parameter prediction model to obtain the coefficients of the temperature polynomial at each moment of the pre-placed part.

[0022] The heating time of the pre-placed part is used as the independent variable of the temperature polynomial of the pre-placed part at each time moment, and the temperature of the pre-placed part at each time moment is calculated.

[0023] According to a specific embodiment disclosed in this application, the step of predicting the temperature curve parameters of the currently pre-placed parts and the parts already in the tank using the temperature curve parameter prediction model, and calculating the corresponding temperature change curves, includes:

[0024] The temperature curve parameter prediction model is used to predict the temperature curve parameters of the pre-placed parts and the parts already in the tank, so as to obtain the temperature change curves of the pre-placed parts and the parts already in the tank.

[0025] Using temperature constraint formula Determine whether the temperature change of the pre-placed part is acceptable, where T is the predicted temperature of the pre-placed part. 标 The target temperature for pre-inserting parts. To determine the time required for pre-insertion of parts to reach the specified temperature, t 标 The required temperature to reach the standard for qualified pre-placed parts.

[0026] According to a specific embodiment disclosed in this application, the step of calculating the temperature adaptability and motion space adaptability of the currently pre-placed part based on the temperature change curve to obtain the overall adaptability of the currently pre-placed part includes:

[0027] Using the temperature adaptation formula Calculate the temperature difference between the pre-placed parts and the already placed parts, where n is the number of already placed parts, and t... total T represents the total processing time. t Let T be the temperature at which the part is pre-placed at time t. (i)t Let t be the temperature of part i that has been placed in the tank at time t.

[0028] According to a specific embodiment disclosed in this application, the step of calculating the temperature adaptability and motion space adaptability of the currently pre-placed part based on the temperature change curve to obtain the overall adaptability of the currently pre-placed part includes:

[0029] Using the spatial fitness formula Calculate the motion space adaptability of the pre-placed part, where n i s represents the number of motion spaces after the pre-placed parts are inserted. iTo determine the flatness of the remaining space after the pre-placed parts are inserted;

[0030] Using the comprehensive fitness formula Calculate the overall fitness of the pre-placed part, where the weights are... μ represents the temperature adaptability T of the part. diff Part motion space adaptability s i The importance of.

[0031] Secondly, this disclosure provides an automatic autoclave discharge device, the device comprising:

[0032] The data collection module is used to collect component temperature data transmitted by each sensor and correct abnormal component temperature data.

[0033] The conversion module is used to draw a temperature curve based on the corrected part temperature data, convert the temperature curve into a polynomial through polynomial fitting, and calculate the target value of the temperature curve parameter prediction model through the polynomial.

[0034] A module is established to build the temperature curve parameter prediction model based on the various feature values ​​and target values ​​in the temperature curve parameter prediction model.

[0035] The calculation module is used to predict the temperature curve parameters of the currently pre-placed parts and the parts already in the tank using the temperature curve parameter prediction model, and calculate the corresponding temperature change curves; based on the temperature change curves, it calculates the temperature adaptability and motion space adaptability of the currently pre-placed parts, and obtains the overall adaptability of the currently pre-placed parts.

[0036] The selection module is used to select the part with the highest overall adaptability to be added to the tank arrangement scheme, and update the list of parts to be arranged until the list of parts to be arranged is empty. Then the automatic tank arrangement ends and the final tank arrangement scheme is output.

[0037] Thirdly, this disclosure provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the automatic autoclave discharge method described in any one of the first aspects.

[0038] Fourthly, this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the automatic autoclave discharge method described in any one of the first aspects.

[0039] The automatic autoclave discharge method provided in this application collects component temperature data transmitted by various sensors and corrects abnormal component temperature data; plots temperature curves based on the corrected component temperature data; converts the temperature curves into polynomials through polynomial fitting; calculates the target value of the temperature curve parameter prediction model using the polynomials; establishes the temperature curve parameter prediction model based on each feature value and the target value in the temperature curve parameter prediction model; predicts the temperature curve parameters of the currently pre-placed components and components already in the autoclave using the temperature curve parameter prediction model, and calculates the corresponding temperature change curves; calculates the temperature adaptability and motion space adaptability of the currently pre-placed components based on the temperature change curves, obtaining the overall adaptability of the currently pre-placed components; selects the component with the highest overall adaptability to add to the discharge scheme, and updates the list of components to be discharged until the list of components to be discharged is empty, at which point the automatic discharge ends and the final discharge scheme is output. This improves the prediction accuracy of the temperature change of the pre-placed components, verifies the rationality of component combination, and improves the efficiency of automatic discharge.

[0040] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the various drawings, similar components are numbered similarly.

[0042] Figure 1 A flowchart of an automatic tank discharge method for an autoclave provided in an embodiment of this application is shown;

[0043] Figure 2 A schematic diagram of an automatic autoclave discharge device provided in an embodiment of this application is shown. Detailed Implementation

[0044] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0045] It should be noted that when an element is said to be "fixed" to another element, it can be directly on the other element or there may be an intervening element. When an element is said to be "connected" to another element, it can be directly connected to the other element or there may be an intervening element. Conversely, when an element is said to be "directly" on another element, there is no intervening element. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0046] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0047] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the template description is for the purpose of describing particular embodiments only and is not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0049] Example 1

[0050] like Figure 1 The diagram shown is a flowchart of an automatic autoclave discharge method according to an embodiment of this application. The automatic autoclave discharge method provided in this application includes the following steps:

[0051] Step S101: Collect component temperature data transmitted by each sensor and correct any abnormal component temperature data.

[0052] Specifically, due to the many influencing factors in the production environment, the data collected by the sensors cannot be guaranteed to be normal at every moment. Therefore, it is usually necessary to correct the abnormal data collected by the sensors in order to ensure the accuracy of the data and improve the accuracy of the prediction.

[0053] The step of collecting component temperature data transmitted by each sensor and correcting abnormal component temperature data includes:

[0054] Collect part temperature data transmitted by each sensor, the part temperature data including tooling placement information;

[0055] The average value correction method is used to correct the part temperature data that is less than the abnormal time interval threshold, and the part temperature data that is greater than or equal to the abnormal time interval threshold is discarded.

[0056] Specifically, the part temperature data may also include tooling material information, relevant process parameters, and part parameters. The tooling material information includes the tooling's heat melt properties, length, width, height, mass, density, heating time, and the temperature of each part at each moment. The relevant process parameters include the autoclave's heating rate, cooling rate, and isothermal time. The part parameters include the part's length, width, height, heat melt properties, mass, and density. The tooling placement information includes the distance between the tooling and the autoclave door, and the distance between the tooling and the autoclave's central axis. The average value correction method refers to calculating the average value of various data points and adjusting the part temperature data for time intervals greater than or equal to the abnormal time interval based on the average value. For a large number of consecutive abnormal data points, the sensor can be considered damaged, and the current sensor data can be discarded.

[0057] Step S102: Plot a temperature curve based on the corrected part temperature data, convert the temperature curve into a polynomial through polynomial fitting, and calculate the target value of the temperature curve parameter prediction model using the polynomial.

[0058] In practical applications, by analyzing the thermocouple data on the parts, the temperature change of the parts is often a smooth curve. Therefore, a polynomial fitting method can be used to convert the temperature change curve of the parts into a polynomial formula y = a0t. n +a1t n-1 +…+a n-1 x, where a is the coefficient of each term, t is the heating time, and n is the highest order of the polynomial. The highest order of the polynomial is determined to be 7 using an exhaustive method, and the parameters of each term are determined by least squares fitting. Finally, the target value y of the temperature curve parameter prediction model is obtained.

[0059] Step S103: Establish the temperature curve parameter prediction model based on the various feature values ​​and target values ​​in the temperature curve parameter prediction model.

[0060] Specifically, the sample data collected by the sensor contains feature redundancy. Some features have a small impact on the prediction results of temperature curve parameters. Therefore, it is necessary to select preset feature values ​​by calculating the correlation coefficient between each feature and the target value.

[0061] The step of establishing the temperature curve parameter prediction model based on each feature value and the target value in the temperature curve parameter prediction model includes:

[0062] Calculate the correlation coefficient between each feature in the temperature curve parameter prediction model and the target value, and select preset feature values ​​based on the correlation coefficient;

[0063] The preset feature value is used as the number of nodes in the input layer of the neural network, and the number of coefficients of the polynomial is used as the number of nodes in the output layer of the neural network.

[0064] The number of nodes and layers in the hidden layer are randomly combined by random search, and the model error value is calculated using cross-validation. The number of nodes and layers in the hidden layer with the smallest model error value is selected to establish the temperature curve parameter prediction model.

[0065] Understandably, this is achieved through the formula for calculating the eigenvalue correlation coefficient. Calculate the correlation coefficients between each feature in the temperature curve parameter prediction model and the target value, where X i Let i be the feature value of sample i. Y represents the feature mean, n is the number of samples, and Y represents the feature mean. i Let i be the target value. This is the characteristic average.

[0066] The selected preset feature values ​​are used as the number of nodes in the input layer of the neural network. The number of nodes and the number of layers in the hidden layers are set by a random search method, and the range of values ​​for the set number of nodes and the number of layers are randomly combined. This ensures that the number of nodes in each layer of the hidden layers remains consistent, thus improving the search speed of the neural network.

[0067] Step S104: The temperature curve parameters of the currently pre-placed parts and the parts already in the tank are predicted using the temperature curve parameter prediction model, and the corresponding temperature change curves are calculated.

[0068] Specifically, before the step of predicting the temperature curve parameters of the currently pre-placed parts and the parts already in the tank using the temperature curve parameter prediction model, and calculating the corresponding temperature change curves, the method further includes:

[0069] The heat melting of the pre-placed part, the heat melting of the tooling, the distance of the tooling from the tank door, the distance from the central axis of the autoclave, the heating rate of the autoclave, the cooling rate, and the isothermal time are input into the temperature curve parameter prediction model to obtain the coefficients of the temperature polynomial at each moment of the pre-placed part.

[0070] The heating time of the pre-placed part is used as the independent variable of the temperature polynomial of the pre-placed part at each time moment, and the temperature of the pre-placed part at each time moment is calculated.

[0071] The step of predicting the temperature curve parameters of the currently pre-placed parts and the parts already in the tank using the temperature curve parameter prediction model, and calculating the corresponding temperature change curves, includes:

[0072] The temperature curve parameter prediction model is used to predict the temperature curve parameters of the pre-placed parts and the parts already in the tank, so as to obtain the temperature change curves of the pre-placed parts and the parts already in the tank.

[0073] Using temperature constraint formula Determine whether the temperature change of the pre-placed part is acceptable, where T is the predicted temperature of the pre-placed part. 标 The target temperature for pre-inserting parts. To determine the time required for pre-insertion of parts to reach the specified temperature, t 标 The required temperature to reach the standard for qualified pre-placed parts.

[0074] Step S105: Based on the temperature change curve, calculate the temperature adaptability and motion space adaptability of the currently pre-placed part to obtain the overall adaptability of the currently pre-placed part.

[0075] The step of calculating the temperature adaptability and motion space adaptability of the currently pre-placed part based on the temperature change curve, and obtaining the overall adaptability of the currently pre-placed part, includes:

[0076] Using the temperature adaptation formula Calculate the temperature difference between the pre-placed parts and the already placed parts, where n is the number of already placed parts, and t... total T represents the total processing time. t Let T be the temperature at which the part is pre-placed at time t. (i)t Let t be the temperature of part i that has been placed in the tank at time t.

[0077] Specifically, by calculating the proportion of cases where the temperature difference between pre-placed parts and already-placed parts is less than 10°C, when T diff The larger the value, the higher the temperature adaptability of the pre-placed parts.

[0078] The step of calculating the temperature adaptability and motion space adaptability of the currently pre-placed part based on the temperature change curve, and obtaining the overall adaptability of the currently pre-placed part, includes:

[0079] Using the spatial fitness formula Calculate the motion space adaptability of the pre-placed part, where ni s represents the number of motion spaces after the pre-placed parts are inserted. i To determine the flatness of the remaining space after the pre-placed parts are inserted;

[0080] Using the comprehensive fitness formula Calculate the overall fitness of the pre-placed part, where the weights are... μ represents the temperature adaptability T of the part. diff Part motion space adaptability s i The importance of.

[0081] Understandably, the more movement space there is after the pre-placed parts are put in, the less even the remaining space will be.

[0082] Step S106: Select the part with the highest overall adaptability and add it to the tank arrangement scheme, and update the list of parts to be arranged until the list of parts to be arranged is empty. The automatic tank arrangement ends and the final tank arrangement scheme is output.

[0083] The automatic autoclave discharge method provided in this application collects part temperature data transmitted by various sensors and corrects abnormal part temperature data; plots temperature curves based on the corrected part temperature data; converts the temperature curves into polynomials through polynomial fitting; calculates the target value of the temperature curve parameter prediction model using the polynomials; establishes the temperature curve parameter prediction model based on each feature value and the target value in the temperature curve parameter prediction model; predicts the temperature curve parameters of the currently pre-placed parts and the parts already in the autoclave using the temperature curve parameter prediction model, and calculates the corresponding temperature change curves; calculates the temperature adaptability and motion space adaptability of the currently pre-placed parts based on the temperature change curves, and obtains the comprehensive adaptability of the currently pre-placed parts; selects the part with the highest comprehensive adaptability to add to the discharge scheme, and updates the list of parts to be discharged until the list of parts to be discharged is empty, at which point the automatic discharge ends and the final discharge scheme is output. This improves the prediction accuracy of the temperature change of the pre-placed parts, verifies the rationality of the part combination, and improves the efficiency of automatic discharge.

[0084] Example 2

[0085] like Figure 2 The diagram shown is a structural schematic of an automatic autoclave discharge device 200 according to an embodiment of this application. The device includes:

[0086] The collection module 201 is used to collect the part temperature data transmitted by each sensor and correct abnormal part temperature data.

[0087] The conversion module 202 is used to draw a temperature curve based on the corrected part temperature data, convert the temperature curve into a polynomial through polynomial fitting, and calculate the target value of the temperature curve parameter prediction model through the polynomial.

[0088] Module 203 is used to establish the temperature curve parameter prediction model based on each feature value and target value in the temperature curve parameter prediction model.

[0089] The calculation module 204 is used to predict the temperature curve parameters of the currently pre-placed parts and the parts already in the tank using the temperature curve parameter prediction model, and calculate the corresponding temperature change curves; based on the temperature change curves, it calculates the temperature adaptability and motion space adaptability of the currently pre-placed parts, and obtains the comprehensive adaptability of the currently pre-placed parts.

[0090] The selection module 205 is used to select the part with the highest overall adaptability to be added to the tank arrangement scheme, and update the list of parts to be arranged until the list of parts to be arranged is empty. Then the automatic tank arrangement ends and the final tank arrangement scheme is output.

[0091] The automatic autoclave discharge device provided in this application collects part temperature data transmitted by various sensors and corrects abnormal part temperature data; it plots temperature curves based on the corrected part temperature data, converts the temperature curves into polynomials through polynomial fitting, and calculates the target value of the temperature curve parameter prediction model using the polynomials; it establishes the temperature curve parameter prediction model based on each feature value and the target value in the temperature curve parameter prediction model; it predicts the temperature curve parameters of the currently pre-placed parts and the parts already in the autoclave using the temperature curve parameter prediction model, and calculates the corresponding temperature change curves; it calculates the temperature adaptability and motion space adaptability of the currently pre-placed parts based on the temperature change curves, and obtains the comprehensive adaptability of the currently pre-placed parts; it selects the part with the highest comprehensive adaptability to add to the discharge scheme, and updates the list of parts to be discharged until the list of parts to be discharged is empty, at which point the automatic discharge ends and the final discharge scheme is output. This improves the prediction accuracy of the temperature change of the pre-placed parts, verifies the rationality of the part combination, and improves the efficiency of automatic discharge.

[0092] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, as an alternative implementation, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0093] In addition, the functional modules or units in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0094] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0095] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. An automatic tank discharge method for autoclaves, characterized in that, The method includes: Collect component temperature data transmitted by each sensor and correct any abnormal component temperature data; A temperature curve is plotted based on the corrected part temperature data. The temperature curve is then converted into a polynomial through polynomial fitting. The target value of the temperature curve parameter prediction model is then calculated using the polynomial. The temperature curve parameter prediction model is established based on the characteristic values ​​and target values ​​in the temperature curve parameter prediction model. The temperature curve parameter prediction model is used to predict the temperature curve parameters of the currently pre-placed parts and the parts already in the tank, and the corresponding temperature change curves are calculated. Based on the temperature change curve, calculate the temperature adaptability and motion space adaptability of the currently pre-placed part to obtain the overall adaptability of the currently pre-placed part; Select the part with the highest overall adaptability and add it to the can arrangement scheme, and update the list of parts to be arranged until the list of parts to be arranged is empty. Then the automatic can arrangement ends and the final can arrangement scheme is output. The step of establishing the temperature curve parameter prediction model based on each feature value and the target value in the temperature curve parameter prediction model includes: Calculate the correlation coefficient between each feature in the temperature curve parameter prediction model and the target value, and select preset feature values ​​based on the correlation coefficient; The preset feature value is used as the number of nodes in the input layer of the neural network, and the number of coefficients of the polynomial is used as the number of nodes in the output layer of the neural network. The number of nodes and layers in the hidden layer are randomly combined by random search, and the model error value is calculated using cross-validation. The number of nodes and layers in the hidden layer with the smallest model error value is selected to establish the temperature curve parameter prediction model.

2. The automatic tank discharge method for autoclaves according to claim 1, characterized in that, The step of collecting component temperature data transmitted by each sensor and correcting abnormal component temperature data includes: Collect part temperature data transmitted by each sensor, the part temperature data including tooling placement information; The average value correction method is used to correct the part temperature data that is less than the abnormal time interval threshold, and the part temperature data that is greater than or equal to the abnormal time interval threshold is discarded.

3. The automatic tank discharge method for autoclaves according to claim 1, characterized in that, Before the step of predicting the temperature curve parameters of the currently pre-placed parts and the parts already in the tank using the temperature curve parameter prediction model, and calculating the corresponding temperature change curves, the method further includes: The heat melting of the pre-placed part, the heat melting of the tooling, the distance of the tooling from the tank door, the distance from the central axis of the autoclave, the heating rate of the autoclave, the cooling rate, and the isothermal time are input into the temperature curve parameter prediction model to obtain the coefficients of the temperature polynomial at each moment of the pre-placed part. The heating time of the pre-placed part is used as the independent variable of the temperature polynomial of the pre-placed part at each time moment, and the temperature of the pre-placed part at each time moment is calculated.

4. The automatic tank discharge method for autoclaves according to claim 3, characterized in that, The step of predicting the temperature curve parameters of the currently pre-placed parts and the parts already in the tank using the temperature curve parameter prediction model, and calculating the corresponding temperature change curves, includes: The temperature curve parameter prediction model is used to predict the temperature curve parameters of the pre-placed parts and the parts already in the tank, so as to obtain the temperature change curves of the pre-placed parts and the parts already in the tank. Using temperature constraint formula , Determine whether the temperature change of the pre-placed part is acceptable, where T is the predicted temperature of the pre-placed part. The target temperature for pre-inserting parts. To allow for the time required for the parts to reach the specified temperature before placement. The required temperature to reach the standard for qualified pre-placed parts.

5. The automatic tank discharge method for autoclaves according to claim 1, characterized in that, The step of calculating the temperature adaptability and motion space adaptability of the currently pre-placed part based on the temperature change curve, and obtaining the overall adaptability of the currently pre-placed part, includes: Using the temperature adaptation formula , Calculate the temperature difference between the pre-placed part and the already placed parts in the tank, where n is the number of already placed parts in the tank. Total processing time Let t be the time to pre-place the part at the desired temperature. Let t be the temperature of part i that has been placed in the tank at time t.

6. The automatic tank discharge method for autoclaves according to claim 1, characterized in that, The step of calculating the temperature adaptability and motion space adaptability of the currently pre-placed part based on the temperature change curve, and obtaining the overall adaptability of the currently pre-placed part, includes: Using the spatial fitness formula Calculate the motion space adaptability of the pre-placed part, where This refers to the amount of space required for the movement of the pre-placed parts. To determine the flatness of the remaining space after the pre-placed parts are inserted; Using the comprehensive fitness formula Calculate the overall fitness of the pre-placed part, where the weights are... These represent the temperature adaptability of the parts respectively. Part movement space adaptability The importance of.

7. An automatic tank discharge device for an autoclave, characterized in that, The apparatus for performing the automatic tank discharge method of claim 1, the device comprising: The data collection module is used to collect component temperature data transmitted by each sensor and correct abnormal component temperature data. The conversion module is used to draw a temperature curve based on the corrected part temperature data, convert the temperature curve into a polynomial through polynomial fitting, and calculate the target value of the temperature curve parameter prediction model through the polynomial. A module is established to build the temperature curve parameter prediction model based on the various feature values ​​and target values ​​in the temperature curve parameter prediction model. The calculation module is used to predict the temperature curve parameters of the currently pre-placed parts and the parts already in the tank using the temperature curve parameter prediction model, and calculate the corresponding temperature change curves; based on the temperature change curves, it calculates the temperature adaptability and motion space adaptability of the currently pre-placed parts, and obtains the overall adaptability of the currently pre-placed parts. The selection module is used to select the part with the highest overall adaptability to be added to the tank arrangement scheme, and update the list of parts to be arranged until the list of parts to be arranged is empty. Then the automatic tank arrangement ends and the final tank arrangement scheme is output.

8. A computer device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the automatic tank discharge method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the automatic autoclave discharge method according to any one of claims 1-6.

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