Activation Processing Control Method and System Based on Image Processing

Abstract

The present invention discloses an activation processing control method and system based on image processing. The method includes: acquiring a regional image of an activation processing area while a target activation processing system performs high-temperature activation processing; determining a pre-treatment image and a post-treatment image of a grenade shell in the regional image based on an image segmentation algorithm; determining an activation processing problem corresponding to the target activation processing system based on the pre-treatment image and the post-treatment image by means of an image analysis algorithm; generating an equipment control instruction corresponding to a high-temperature processing device of the target activation processing system based on a preset instruction generation rule according to the activation processing problem; and using the equipment control instruction to control the high-temperature processing device to perform an activation operation on the grenade shell so as to solve the activation processing problem. The present invention can more accurately and timely monitor and correct the working anomalies of the activation processing system, improve the accuracy and efficiency of activation processing, and improve the product yield.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to an activation processing control method and system based on image processing. Background Art

[0002] During the manufacturing process of a grenade, the assembly and processing between the grenade shell and the fuse device is an important link, which affects the safety performance of the finally produced grenade product. Specifically, before the assembly of the grenade shell and the fuse device, it is necessary to perform high-temperature activation treatment on the plastic threads on the grenade shell to make them full of activation energy, so that they can be more firmly combined with the metal threads of the fuse device through a two-component adhesive in the subsequent process, and avoid separation. Therefore, the processing temperature, processing position, and processing duration of the thread activation treatment of the grenade shell need to be strictly controlled. The existing thread activation treatment operation of the grenade shell is generally achieved through manual operation, which has low efficiency and high defect rate, and is prone to accidents. And for the activation treatment systems implemented by some existing technologies through automation technology, they generally only achieve activation treatment based on mechanical automation control, without further combining the analysis of activation treatment images and data control technology. Therefore, the efficiency of their activation treatment is not high, the accuracy is insufficient, and there are potential safety hazards. It can be seen that there are defects in the existing technology and urgent solutions are needed. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide an activation processing control method and system based on image processing, which can more accurately and timely monitor and correct the working abnormalities of the activation processing system, improve the accuracy and efficiency of activation processing, and improve the product yield.

[0004] To solve the above technical problem, the first aspect of the present invention discloses an activation processing control method based on image processing, and the method includes:

[0005] Acquire the regional image of the activation processing area while the target activation processing system performs high-temperature activation processing;

[0006] Based on the image segmentation algorithm, determine the image during processing and the image after processing of the grenade shell in the regional image;

[0007] Based on the image analysis algorithm, determine the activation processing problem corresponding to the target activation processing system according to the image during processing and the image after processing;

[0008] According to the activation processing problem, generate a device control instruction for the high-temperature processing device of the target activation processing system based on a preset instruction generation rule; the device control instruction is used to control the activation operation of the high-temperature processing device on the grenade shell to solve the activation processing problem.

[0009] As an optional implementation manner, in the first aspect of the present invention, the processing of determining the grenade casing in the regional image and the post-processing image based on the image segmentation algorithm includes:

[0010] Based on the trained image segmentation neural network, determine multiple grenade casing regions and equipment image regions of the high-temperature processing equipment in the regional image; the image segmentation neural network is trained by a training data set including multiple training regional images and corresponding grenade casing region annotations and high-temperature processing equipment region annotations;

[0011] Input each of the grenade casing regions into the trained processing prediction neural network model to obtain the processing probability of each grenade casing region that has been subjected to high-temperature processing; the processing prediction neural network model is trained by a training data set including multiple training grenade casing images and corresponding annotations on whether they have been subjected to high-temperature processing;

[0012] Calculate the first regional distance between each of the grenade casing regions and the equipment image region;

[0013] According to the processing probability and the first regional distance, screen out the processing image and the post-processing image of the grenade casing from the multiple grenade casing regions.

[0014] As an optional implementation manner, in the first aspect of the present invention, the activation processing problem includes at least one processing problem and corresponding problem parameters; the processing problem is an activation temperature problem, an activation timing problem, an activation position problem, or an activation angle problem; the problem parameters are an activation temperature deviation parameter, an activation timing deviation parameter, an activation position deviation parameter, or an activation angle deviation parameter.

[0015] Screen out all the grenade casing regions with the processing probability greater than a preset probability threshold to obtain multiple processed casing regions;

[0016] Screen out the grenade casing region with the smallest first regional distance to obtain the processing image of the grenade casing;

[0017] Calculate the second regional distance between each of the processed casing regions and the processing image;

[0018] Determine the processed casing region with the smallest second regional distance as the post-processing image of the grenade casing.

[0019] As an optional implementation manner, in the first aspect of the present invention, the activation processing problem includes at least one processing problem and corresponding problem parameters; the processing problem is an activation temperature problem, an activation timing problem, an activation position problem, or an activation angle problem; the problem parameters are an activation temperature deviation parameter, an activation timing deviation parameter, an activation position deviation parameter, or an activation angle deviation parameter.

[0020] As an optional implementation, in the first aspect of the present invention, determining the activation processing problem corresponding to the target activation processing system based on the image analysis algorithm according to the image during processing and the image after processing includes:

[0021] Input the image during processing into an image temperature prediction algorithm model to obtain the predicted temperature information corresponding to the image during processing;

[0022] Calculate the temperature difference between the predicted temperature information and a preset temperature threshold;

[0023] Judge whether the temperature difference is negative and its absolute value is greater than a preset difference threshold to obtain a judgment result;

[0024] If the judgment result is yes, determine that there is an activation temperature problem, and determine the activation temperature deviation parameter of the activation temperature problem as the temperature difference;

[0025] Input the image during processing and the image after processing into a processing problem prediction multi-classifier to obtain the predicted processing problem result corresponding to the image after processing;

[0026] According to the predicted processing problem result, determine the activation timing problem, activation position problem or activation angle problem in the activation processing problem and the corresponding problem parameters.

[0027] As an optional implementation, in the first aspect of the present invention, the processing problem prediction multi-classifier is an RNN neural network, which is trained through a training data set including multiple training images during processing, corresponding training images after processing, and processing problem annotations, and the parameters are optimized based on the cross-entropy loss function and the gradient descent algorithm until convergence; the processing problem annotations include processing timing problem annotations, processing timing deviation value annotations, processing position problem annotations, processing position deviation value annotations, processing angle problem annotations and processing angle deviation value annotations.

[0028] As an optional implementation, in the first aspect of the present invention, generating the device control instruction corresponding to the high-temperature processing device of the target activation processing system based on the activation processing problem includes:

[0029] When there is an activation temperature problem in the activation processing problem, generate a temperature control instruction; the temperature control instruction includes a temperature control value, and the magnitude of the temperature control value is proportional to the activation temperature deviation parameter;

[0030] When there is an activation timing problem in the activation processing problem, an ignition control instruction is generated; the ignition control instruction includes an ignition time interval value, and the magnitude of the ignition time interval value is proportional to the activation timing deviation parameter;

[0031] When there is an activation position problem or an activation angle problem in the activation processing problem, a fire outlet control instruction is generated according to the activation position deviation parameter and / or the activation angle deviation parameter;

[0032] The generated temperature control instruction, ignition control instruction, and / or fire outlet control instruction are sent to the high-temperature processing equipment of the target activation processing system for execution.

[0033] As an optional implementation manner, in the first aspect of the present invention, the generating a fire outlet control instruction according to the activation position deviation parameter and / or the activation angle deviation parameter includes:

[0034] Determine the current attitude information of the fire outlet of the high-temperature processing equipment;

[0035] Use the activation position deviation parameter and / or the activation angle deviation parameter as ignition processing change parameters and input them into the attitude simulation three-dimensional model corresponding to the high-temperature processing equipment of the target activation processing system to obtain the target attitude information corresponding to the fire outlet; the attitude simulation three-dimensional model is obtained by performing structural modeling on the high-temperature processing equipment and building an ignition simulation environment;

[0036] Calculate the attitude difference information between the target attitude information and the current attitude information;

[0037] Generate an attitude adjustment instruction corresponding to the attitude difference information to obtain a fire outlet control instruction.

[0038] A second aspect of the embodiments of the present invention discloses an activation processing control system based on image processing, and the system includes:

[0039] An acquisition module, configured to acquire a regional image of an activation processing area while the target activation processing system performs high-temperature activation processing;

[0040] A segmentation module, configured to determine the image during processing and the image after processing of the grenade shell in the regional image based on an image segmentation algorithm;

[0041] An analysis module, configured to determine the activation processing problem corresponding to the target activation processing system based on the image during processing and the image after processing and based on an image analysis algorithm;

[0042] A generation module, configured to generate a device control instruction corresponding to the high-temperature treatment device of the target activation treatment system based on a preset instruction generation rule according to the activation treatment problem; the device control instruction is used to control the high-temperature treatment device to perform an activation operation on the grenade shell to solve the activation treatment problem.

[0043] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the segmentation module determines the processing-time image and the post-processing image of the grenade shell in the regional image based on an image segmentation algorithm includes:

[0044] Based on a trained image segmentation neural network, determine multiple grenade shell regions and device image regions of the high-temperature treatment device in the regional image; the image segmentation neural network is trained by a training data set including multiple training regional images and corresponding grenade shell region annotations and high-temperature treatment device region annotations.

[0045] Input each of the grenade shell regions into a trained processing prediction neural network model to obtain the processing probability of each grenade shell region that has been subjected to high-temperature treatment; the processing prediction neural network model is trained by a training data set including multiple training grenade shell images and corresponding annotations of whether they have been subjected to high-temperature treatment.

[0046] Calculate the first regional distance between each of the grenade shell regions and the device image region.

[0047] According to the processing probability and the first regional distance, screen out the processing-time image and the post-processing image of the grenade shell from the multiple grenade shell regions.

[0048] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the segmentation module screens out the processing-time image and the post-processing image of the grenade shell from the multiple grenade shell regions according to the processing probability and the first regional distance includes:

[0049] Screen out all the grenade shell regions whose processing probability is greater than a preset probability threshold to obtain multiple processed shell regions.

[0050] Screen out the grenade shell region with the smallest first regional distance to obtain the processing-time image of the grenade shell.

[0051] Calculate the second regional distance between each of the processed shell regions and the processing-time image.

[0052] Determine the processed shell region with the smallest second regional distance as the post-processing image of the grenade shell.

[0053] As an alternative embodiment, in the second aspect of the present invention, the activation processing problem includes at least one processing problem and corresponding problem parameters; the processing problem is an activation temperature problem, an activation timing problem, an activation position problem, or an activation angle problem; the problem parameters are an activation temperature deviation parameter, an activation timing deviation parameter, an activation position deviation parameter, or an activation angle deviation parameter.

[0054] As an alternative embodiment, in the second aspect of the present invention, the analysis module determines the specific manner of the activation processing problem corresponding to the target activation processing system based on the processing-time image and the post-processing image and an image analysis algorithm, including:

[0055] Input the processing-time image into an image temperature prediction algorithm model to obtain predicted temperature information corresponding to the processing-time image;

[0056] Calculate the temperature difference between the predicted temperature information and a preset temperature threshold;

[0057] Judge whether the temperature difference is negative and the absolute value is greater than a preset difference threshold to obtain a judgment result;

[0058] If the judgment result is yes, determine that there is the activation temperature problem, and determine the activation temperature deviation parameter of the activation temperature problem as the temperature difference;

[0059] Input the processing-time image and the post-processing image into a processing problem prediction multi-classifier to obtain a predicted processing problem result corresponding to the post-processing image;

[0060] According to the predicted processing problem result, determine the activation timing problem, activation position problem, or activation angle problem in the activation processing problem and the corresponding problem parameters.

[0061] As an alternative embodiment, in the second aspect of the present invention, the processing problem prediction multi-classifier is an RNN neural network, which is trained through a training data set including a plurality of training processing-time images and corresponding training post-processing images and processing problem annotations, and the parameters are optimized based on a cross-entropy loss function and a gradient descent algorithm until convergence; the processing problem annotations include processing timing problem annotations, processing timing deviation value annotations, processing position problem annotations, processing position deviation value annotations, processing angle problem annotations, and processing angle deviation value annotations.

[0062] As an alternative embodiment, in the second aspect of the present invention, the generation module generates the specific manner of the device control instruction corresponding to the high-temperature processing device of the target activation processing system based on the activation processing problem and a preset instruction generation rule, including:

[0063] When there is an activation temperature problem in the activation treatment problem, a temperature control instruction is generated; the temperature control instruction includes a temperature control value, and the magnitude of the temperature control value is proportional to the activation temperature deviation parameter;

[0064] When there is an activation timing problem in the activation treatment problem, an ignition control instruction is generated; the ignition control instruction includes an ignition time interval value, and the magnitude of the ignition time interval value is proportional to the activation timing deviation parameter;

[0065] When there is an activation position problem or an activation angle problem in the activation treatment problem, an outlet control instruction is generated according to the activation position deviation parameter and / or the activation angle deviation parameter;

[0066] The generated temperature control instruction, ignition control instruction and / or outlet control instruction are sent to the high-temperature treatment equipment of the target activation treatment system for execution.

[0067] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the generation module generates an outlet control instruction according to the activation position deviation parameter and / or the activation angle deviation parameter includes:

[0068] Determine the current attitude information of the outlet of the high-temperature treatment equipment;

[0069] Take the activation position deviation parameter and / or the activation angle deviation parameter as ignition treatment change parameters and input them into the attitude simulation three-dimensional model corresponding to the high-temperature treatment equipment of the target activation treatment system to obtain the target attitude information corresponding to the outlet; the attitude simulation three-dimensional model is obtained by performing structural modeling on the high-temperature treatment equipment and building an ignition simulation environment;

[0070] Calculate the attitude difference information between the target attitude information and the current attitude information;

[0071] Generate an attitude adjustment instruction corresponding to the attitude difference information to obtain an outlet control instruction.

[0072] The third aspect of the present invention discloses another activation treatment control system based on image processing, and the system includes:

[0073] A memory storing executable program code;

[0074] A processor coupled to the memory;

[0075] The processor calls the executable program code stored in the memory and executes some or all of the steps in the activation processing control method based on image processing disclosed in the first aspect of the present invention.

[0076] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which are used to execute some or all of the steps in the activation processing control method based on image processing disclosed in the first aspect of the present invention when the computer instructions are called.

[0077] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0078] The present invention can determine the images of the grenade shell during and after processing in the image of the activation processing area based on the image segmentation algorithm, and then accurately analyze the activation processing problems corresponding to the activation processing system according to the images during and after processing, so as to determine accurate device control instructions to improve the activation processing problems, thereby being able to monitor and correct the working abnormalities of the activation processing system more accurately and timely, improving the accuracy and efficiency of the activation processing, and improving the product yield. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0080] Figure 1 It is a schematic flowchart of an activation processing control method based on image processing disclosed in an embodiment of the present invention.

[0081] Figure 2 It is a schematic structural diagram of an activation processing control system based on image processing disclosed in an embodiment of the present invention.

[0082] Figure 3 It is a schematic structural diagram of another activation processing control system based on image processing disclosed in an embodiment of the present invention.

[0083] Figure 4 It is a schematic top view of the structure of an activation processing system disclosed in an embodiment of the present invention.

[0084] Figure 5 It is a schematic side view of a partial structure of an activation processing system disclosed in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0085] To enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0086] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or equipment.

[0087] Referring to "embodiment" herein means that a specific feature, structure or characteristic described in connection with the embodiment may be included in at least one embodiment of the present invention. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0088] The present invention discloses an activation processing control method and system based on image processing, which can determine the images of the grenade shell during and after processing in the image of the activation processing area based on an image segmentation algorithm, and then accurately analyze the activation processing problems corresponding to the activation processing system according to the images during and after processing, so as to determine accurate device control instructions to improve the activation processing problems, thereby being able to more accurately and timely monitor and correct the working anomalies of the activation processing system, improve the accuracy and efficiency of the activation processing, and improve the product yield. The following will be described in detail respectively.

[0089] Embodiment 1

[0090] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of an activation processing control method based on image processing disclosed in the embodiments of the present invention. Among them, Figure 1 the described activation processing control method based on image processing can be applied to a data processing system / data processing device / data processing server (wherein, the server includes a local processing server or a cloud processing server). As Figure 1 shown, the activation processing control method based on image processing may include the following operations:

[0091] 101. Obtain the regional image of the activation treatment area while the target activation treatment system performs high-temperature activation treatment.

[0092] 102. Based on the image segmentation algorithm, determine the image of the grenade shell during treatment and the image after treatment in the regional image.

[0093] 103. According to the image during treatment and the image after treatment, based on the image analysis algorithm, determine the activation treatment problem corresponding to the target activation treatment system.

[0094] 104. According to the activation treatment problem, based on the preset instruction generation rule, generate the device control instruction corresponding to the high-temperature treatment device of the target activation treatment system.

[0095] Optionally, the device control instruction is used to control the activation operation of the high-temperature treatment device on the grenade shell to solve the activation treatment problem.

[0096] It can be seen that the above-mentioned invention embodiments can, based on the image segmentation algorithm, determine the image of the grenade shell during treatment and the image after treatment in the image of the activation treatment area, and then accurately analyze the activation treatment problem corresponding to the activation treatment system according to the image during treatment and the image after treatment, so as to determine accurate device control instructions to improve the activation treatment problem, thereby being able to more accurately and timely monitor and correct the working abnormality of the activation treatment system, improve the accuracy and efficiency of the activation treatment, and improve the product yield.

[0097] As an optional embodiment, in the above steps, based on the image segmentation algorithm, determining the image of the grenade shell during treatment and the image after treatment in the regional image includes:

[0098] Based on the trained image segmentation neural network, determine multiple grenade shell areas and the device image area of the high-temperature treatment device in the regional image; optionally, the image segmentation neural network is trained through a training data set including multiple training regional images and corresponding grenade shell area annotations and high-temperature treatment device area annotations;

[0099] Input each grenade shell area into the trained processing prediction neural network model to obtain the processing probability of each grenade shell area that has been subjected to high-temperature treatment; optionally, the processing prediction neural network model is trained through a training data set including multiple training grenade shell images and corresponding annotations of whether they have been subjected to high-temperature treatment;

[0100] Calculate the first regional distance between each grenade shell area and the device image area;

[0101] According to the processing probability and the first regional distance, screen out the image of the grenade shell during treatment and the image after treatment from multiple grenade shell areas.

[0102] It can be seen that through the above optional embodiments, the grenade shell area and the device image area can be determined by the trained image segmentation neural network, and further, the images of the grenade shell during and after processing can be accurately selected through the predicted processing probability and the area distance. Subsequently, they are used for precise analysis of the activation processing problems corresponding to the activation processing system, assisting in more precise and timely monitoring and correction of the working abnormalities of the activation processing system, improving the accuracy and efficiency of the activation processing, and increasing the product yield.

[0103] As an optional embodiment, in the above steps, screening the images of the grenade shell during and after processing from multiple grenade shell areas according to the processing probability and the first area distance includes:

[0104] Screening out all grenade shell areas with a processing probability greater than a preset probability threshold to obtain multiple processed shell areas;

[0105] Screening out the grenade shell area with the smallest first area distance to obtain the image of the grenade shell during processing;

[0106] Calculating the second area distance between each processed shell area and the image during processing;

[0107] Determining the processed shell area with the smallest second area distance as the image of the grenade shell after processing.

[0108] It can be seen that through the above optional embodiments, based on the screening and calculation of the processing probability and the area distance respectively, the image of the grenade shell during processing and the image just after processing can be determined. Subsequently, they are used for precise analysis of the activation processing problems corresponding to the activation processing system, assisting in more precise and timely monitoring and correction of the working abnormalities of the activation processing system, improving the accuracy and efficiency of the activation processing, and increasing the product yield.

[0109] As an optional embodiment, in the above steps, the activation processing problems include at least one processing problem and corresponding problem parameters; the processing problems are activation temperature problems, activation timing problems, activation position problems, or activation angle problems; the problem parameters are activation temperature deviation parameters, activation timing deviation parameters, activation position deviation parameters, or activation angle deviation parameters.

[0110] It can be seen that through the above optional embodiments, the content of the activation processing problems is defined, which can fully characterize the problem characteristics that may occur in the activation processing of the grenade shell thread. Subsequently, they are used to determine precise equipment control instructions to improve the activation processing problems, assisting in more precise and timely monitoring and correction of the working abnormalities of the activation processing system, improving the accuracy and efficiency of the activation processing, and increasing the product yield.

[0111] As an alternative embodiment, in the above steps, based on the image analysis algorithm according to the image during processing and the image after processing, determining the activation processing problem corresponding to the target activation processing system includes:

[0112] Input the image during processing into the image temperature prediction algorithm model to obtain the predicted temperature information corresponding to the image during processing;

[0113] Calculate the temperature difference between the predicted temperature information and the preset temperature threshold;

[0114] Determine whether the temperature difference is negative and its absolute value is greater than the preset difference threshold to obtain a judgment result;

[0115] If the judgment result is yes, determine that there is an activation temperature problem, and determine the activation temperature deviation parameter of the activation temperature problem as the temperature difference;

[0116] Input the image during processing and the image after processing into the processing problem prediction multi-classifier to obtain the predicted processing problem result corresponding to the image after processing;

[0117] According to the predicted processing problem result, determine the activation timing problem, activation position problem or activation angle problem in the activation processing problem and the corresponding problem parameters.

[0118] Specifically, the predicted processing problem result will include the processing problems predicted by the processing problem prediction multi-classifier for the image during processing and the image after processing, which may include one or more of the activation timing problem, activation position problem or activation angle problem and the corresponding problem parameters.

[0119] It can be seen that through the above alternative embodiment, it is possible to comprehensively determine the activation processing problem and problem parameters corresponding to the target activation processing system based on the image temperature prediction and the processing problem prediction multi-classifier algorithm, and then use them to determine accurate equipment control instructions to improve the activation processing problem, assist in more accurately and timely monitoring and correcting the working abnormalities of the activation processing system, improve the accuracy and efficiency of the activation processing, and improve the product yield.

[0120] As an alternative embodiment, in the above steps, the processing problem prediction multi-classifier is an RNN neural network, which is trained through a training data set including multiple training images during processing and the corresponding training images after processing as well as processing problem annotations, and the parameters are optimized based on the cross-entropy loss function and the gradient descent algorithm until convergence; the processing problem annotations include processing timing problem annotations, processing timing deviation value annotations, processing position problem annotations, processing position deviation value annotations, processing angle problem annotations and processing angle deviation value annotations.

[0121] It can be seen that through the above optional embodiments, the model details and training details of the problem prediction multi-classifier are defined, which can be used to accurately predict activation timing problems, activation position problems, or activation angle problems and corresponding problem parameters, and subsequently used to determine accurate equipment control instructions to improve activation processing problems, assisting in more accurate and timely monitoring and correction of work abnormalities in the activation processing system, improving the accuracy and efficiency of activation processing, and increasing the product yield.

[0122] As an optional embodiment, in the above steps, according to the activation processing problem, based on a preset instruction generation rule, generating an equipment control instruction corresponding to the high-temperature treatment equipment of the target activation processing system includes:

[0123] When there is an activation temperature problem in the activation processing problem, generating a temperature control instruction; optionally, the temperature control instruction includes a temperature control value, and the magnitude of the temperature control value is proportional to the activation temperature deviation parameter;

[0124] When there is an activation timing problem in the activation processing problem, generating an ignition control instruction; optionally, the ignition control instruction includes an ignition time interval value, and the magnitude of the ignition time interval value is proportional to the activation timing deviation parameter;

[0125] When there is an activation position problem or an activation angle problem in the activation processing problem, generating a fire outlet control instruction according to the activation position deviation parameter and / or the activation angle deviation parameter;

[0126] Sending the generated temperature control instruction, ignition control instruction, and / or fire outlet control instruction to the high-temperature treatment equipment of the target activation processing system for execution.

[0127] It can be seen that through the above optional embodiments, it is possible to determine corresponding control instructions based on the existing activation processing problems to improve the activation processing problems, achieve more accurate and timely monitoring and correction of work abnormalities in the activation processing system, improve the accuracy and efficiency of activation processing, and increase the product yield.

[0128] As an optional embodiment, in the above steps, generating a fire outlet control instruction according to the activation position deviation parameter and / or the activation angle deviation parameter includes:

[0129] Determining the current attitude information of the fire outlet of the high-temperature treatment equipment;

[0130] Taking the activation position deviation parameter and / or the activation angle deviation parameter as ignition processing change parameters and inputting them into the attitude simulation three-dimensional model corresponding to the high-temperature treatment equipment of the target activation processing system to obtain the target attitude information corresponding to the fire outlet; optionally, the attitude simulation three-dimensional model is obtained by performing structural modeling on the high-temperature treatment equipment and building an ignition simulation environment;

[0131] Calculate the pose difference information between the target pose information and the current pose information;

[0132] Generate a pose adjustment instruction corresponding to the pose difference information to obtain a control instruction for the flame outlet.

[0133] Specifically, a three-dimensional simulation environment for the overall structure of the high-temperature treatment equipment is built through simulation software, and a simulation environment for pose parameters and corresponding ignition effects is built based on historical ignition data to achieve pose simulation. Specifically, after inputting the activation position deviation parameter and / or activation angle deviation parameter as ignition treatment change parameters into the three-dimensional pose simulation model, the current ignition effect can be determined first based on the activation position deviation parameter and / or activation angle deviation parameter, and then based on the ignition effect, the target pose of the flame outlet of the high-temperature treatment equipment can be simulated and calculated based on the built simulation environment. Then, according to the difference between the current pose and the target pose, the corresponding control instruction is generated.

[0134] It can be seen that through the above optional embodiments, the target pose information of the flame outlet can be calculated and simulated for the activation position deviation parameter and / or activation angle deviation parameter through the three-dimensional pose simulation model, so as to accurately generate the control instruction for the flame outlet based on the pose difference, realize more accurate and timely monitoring and correction of the working abnormality of the activation treatment system, improve the accuracy and efficiency of the activation treatment, and improve the product yield.

[0135] In a specific implementation, the structure of the activation treatment system referred to in the present invention can be referred to Figure 4 and Figure 5 , which has a conveying mechanism for rotating and conveying the grenade shell to be activated and an ignition device for high-temperature activation treatment. The ignition device is controlled by a control mechanism with multiple degrees of freedom, and different pose control transformations can be realized. The images of the activation treatment are obtained through a camera device arranged in the activation treatment area, and the problems are analyzed and the ignition device is controlled by combining the image processing technology disclosed in the above embodiments, which can improve the activation treatment efficiency and stability of the activation treatment system.

[0136] Embodiment 2

[0137] Please refer to Figure 2 , Figure 2 which is a schematic structural diagram of an activation treatment control system based on image processing disclosed in an embodiment of the present invention. Among them, Figure 2 The described activation treatment control system based on image processing can be applied to a data processing system / data processing device / data processing server (wherein, the server includes a local processing server or a cloud processing server). As Figure 2As shown in the figure, the activation processing control system based on image processing may include:

[0138] An acquisition module 201, configured to acquire the regional image of the activation processing area while the target activation processing system performs high-temperature activation processing.

[0139] A segmentation module 202, configured to determine the pre-processing image and post-processing image of the grenade shell in the regional image based on an image segmentation algorithm.

[0140] An analysis module 203, configured to determine the activation processing problem corresponding to the target activation processing system based on the pre-processing image and post-processing image and an image analysis algorithm.

[0141] A generation module 204, configured to generate a device control instruction corresponding to the high-temperature processing device of the target activation processing system based on the activation processing problem and a preset instruction generation rule.

[0142] Optionally, the device control instruction is used to control the activation operation of the high-temperature processing device on the grenade shell to solve the activation processing problem.

[0143] It can be seen that the above-mentioned invention embodiments can determine the pre-processing image and post-processing image of the grenade shell in the image of the activation processing area based on the image segmentation algorithm, and then accurately analyze the activation processing problem corresponding to the activation processing system according to the pre-processing image and post-processing image, so as to determine accurate device control instructions to improve the activation processing problem, thereby being able to more accurately and timely monitor and correct the working abnormality of the activation processing system, improve the accuracy and efficiency of activation processing, and improve the product yield.

[0144] As an optional embodiment, the specific manner in which the segmentation module determines the pre-processing image and post-processing image of the grenade shell in the regional image based on the image segmentation algorithm includes:

[0145] Based on the trained image segmentation neural network, determine multiple grenade shell regions and the device image region of the high-temperature processing device in the regional image; optionally, the image segmentation neural network is trained by a training data set including multiple training regional images and corresponding grenade shell region annotations and high-temperature processing device region annotations;

[0146] Input each grenade shell region into the trained processing prediction neural network model to obtain the processing probability of each grenade shell region that has been subjected to high-temperature processing; optionally, the processing prediction neural network model is trained by a training data set including multiple training grenade shell images and corresponding annotations of whether they have been subjected to high-temperature processing;

[0147] Calculate the first regional distance between each grenade shell region and the device image region;

[0148] Based on the processing probability and the first regional distance, the images during processing and after processing of the grenade casing are screened out from multiple grenade casing regions.

[0149] It can be seen that through the above optional embodiments, the grenade casing region and the device image region can be determined by the trained image segmentation neural network, and further, the images during processing and after processing of the grenade casing can be accurately screened out through the predicted processing probability and regional distance, which are subsequently used for precise analysis of the activation processing problems corresponding to the activation processing system, assisting in more precise and timely monitoring and correction of the working anomalies of the activation processing system, improving the accuracy and efficiency of activation processing, and improving the product yield.

[0150] As an optional embodiment, the specific manner in which the segmentation module screens out the images during processing and after processing of the grenade casing from multiple grenade casing regions according to the processing probability and the first regional distance includes:

[0151] Screen out all grenade casing regions with a processing probability greater than a preset probability threshold to obtain multiple processed casing regions;

[0152] Screen out the grenade casing region with the smallest first regional distance to obtain the image of the grenade casing during processing;

[0153] Calculate the second regional distance between each processed casing region and the image during processing;

[0154] Determine the processed casing region with the smallest second regional distance as the image of the grenade casing after processing.

[0155] It can be seen that through the above optional embodiments, the image of the grenade casing during processing and the image just after processing can be determined respectively based on the screening and calculation of the processing probability and regional distance, which are subsequently used for precise analysis of the activation processing problems corresponding to the activation processing system, assisting in more precise and timely monitoring and correction of the working anomalies of the activation processing system, improving the accuracy and efficiency of activation processing, and improving the product yield.

[0156] As an optional embodiment, the activation processing problems include at least one processing problem and corresponding problem parameters; the processing problems are activation temperature problems, activation timing problems, activation position problems or activation angle problems; the problem parameters are activation temperature deviation parameters, activation timing deviation parameters, activation position deviation parameters or activation angle deviation parameters.

[0157] It can be seen that through the above optional embodiments, the content of the activation treatment problem is defined, which can fully characterize the problem characteristics that may occur in the activation treatment of the grenade shell thread. Subsequently, it is used to determine accurate equipment control instructions to improve the activation treatment problem, assist in more accurately and timely monitoring and correcting the working abnormalities of the activation treatment system, improve the accuracy and efficiency of the activation treatment, and improve the product yield.

[0158] As an optional embodiment, the analysis module determines the specific manner of the activation treatment problem corresponding to the target activation treatment system based on the image during processing and the image after processing, and based on the image analysis algorithm, including:

[0159] Input the image during processing into the image temperature prediction algorithm model to obtain the predicted temperature information corresponding to the image during processing;

[0160] Calculate the temperature difference between the predicted temperature information and the preset temperature threshold;

[0161] Judge whether the temperature difference is negative and the absolute value is greater than the preset difference threshold to obtain a judgment result;

[0162] If the judgment result is yes, it is determined that there is an activation temperature problem, and the activation temperature deviation parameter of the activation temperature problem is determined as the temperature difference;

[0163] Input the image during processing and the image after processing into the processing problem prediction multi-classifier to obtain the predicted processing problem result corresponding to the image after processing;

[0164] According to the predicted processing problem result, determine the activation timing problem, activation position problem or activation angle problem in the activation treatment problem and the corresponding problem parameters.

[0165] It can be seen that through the above optional embodiments, based on the image temperature prediction and the processing problem prediction multi-classifier algorithm, the activation treatment problem and problem parameters corresponding to the target activation treatment system can be comprehensively determined. Subsequently, it is used to determine accurate equipment control instructions to improve the activation treatment problem, assist in more accurately and timely monitoring and correcting the working abnormalities of the activation treatment system, improve the accuracy and efficiency of the activation treatment, and improve the product yield.

[0166] As an optional embodiment, the processing problem prediction multi-classifier is an RNN neural network, which is trained through a training data set including multiple training images during processing and corresponding training images after processing and processing problem annotations, and the parameters are optimized until convergence based on the cross-entropy loss function and the gradient descent algorithm; the processing problem annotations include processing timing problem annotations, processing timing deviation value annotations, processing position problem annotations, processing position deviation value annotations, processing angle problem annotations and processing angle deviation value annotations.

[0167] As can be seen, through the above optional embodiments, the model details and training details of the problem prediction multi-classifier are defined, which can be used to accurately predict activation timing problems, activation position problems, or activation angle problems and corresponding problem parameters, and subsequently used to determine precise device control instructions to improve activation processing problems, assisting in more accurate and timely monitoring and correction of work anomalies in the activation processing system, improving the accuracy and efficiency of activation processing, and increasing the product yield.

[0168] As an optional embodiment, the specific manner in which the generation module generates a device control instruction corresponding to the high-temperature treatment device of the target activation processing system according to the activation processing problem based on a preset instruction generation rule includes:

[0169] When there is an activation temperature problem in the activation processing problem, a temperature control instruction is generated; optionally, the temperature control instruction includes a temperature control value, and the magnitude of the temperature control value is proportional to the activation temperature deviation parameter;

[0170] When there is an activation timing problem in the activation processing problem, an ignition control instruction is generated; optionally, the ignition control instruction includes an ignition time interval value, and the magnitude of the ignition time interval value is proportional to the activation timing deviation parameter;

[0171] When there is an activation position problem or an activation angle problem in the activation processing problem, a nozzle control instruction is generated according to the activation position deviation parameter and / or the activation angle deviation parameter;

[0172] The generated temperature control instruction, ignition control instruction, and / or nozzle control instruction are sent to the high-temperature treatment device of the target activation processing system for execution.

[0173] As can be seen, through the above optional embodiments, corresponding control instructions can be determined based on the existing activation processing problems to improve the activation processing problems, enabling more accurate and timely monitoring and correction of work anomalies in the activation processing system, improving the accuracy and efficiency of activation processing, and increasing the product yield.

[0174] As an optional embodiment, the specific manner in which the generation module generates a nozzle control instruction according to the activation position deviation parameter and / or the activation angle deviation parameter includes:

[0175] Determine the current attitude information of the nozzle of the high-temperature treatment device;

[0176] Taking the activation position deviation parameter and / or the activation angle deviation parameter as the ignition treatment variation parameter, input it into the attitude simulation three-dimensional model corresponding to the high-temperature treatment equipment of the target activation treatment system to obtain the target attitude information corresponding to the fire outlet; optionally, the attitude simulation three-dimensional model is obtained by building a structural model of the high-temperature treatment equipment and building an ignition simulation environment;

[0177] Calculate the attitude difference information between the target attitude information and the current attitude information;

[0178] Generate an attitude adjustment instruction corresponding to the attitude difference information to obtain a fire outlet control instruction.

[0179] It can be seen that through the above optional embodiments, the target attitude information of the fire outlet can be calculated and simulated by the attitude simulation three-dimensional model for the activation position deviation parameter and / or the activation angle deviation parameter, so as to accurately generate the control instruction of the fire outlet based on the attitude difference, realize more accurate and timely monitoring and correction of the working abnormality of the activation treatment system, improve the accuracy and efficiency of the activation treatment, and improve the product yield.

[0180] Embodiment III

[0181] Please refer to Figure 3 , Figure 3 which is another activation treatment control system based on image processing disclosed in the embodiments of the present invention. Figure 3 The described activation treatment control system based on image processing is applied to a data processing system / data processing device / data processing server (wherein, the server includes a local processing server or a cloud processing server). As Figure 3 shown, the activation treatment control system based on image processing may include:

[0182] A memory 301 storing executable program code;

[0183] A processor 302 coupled to the memory 301;

[0184] Wherein, the processor 302 calls the executable program code stored in the memory 301 to execute the steps of the activation treatment control method described in Embodiment I.

[0185] Embodiment IV

[0186] The embodiments of the present invention disclose a computer-readable storage medium, which stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the activation treatment control method described in Embodiment I.

[0187] Embodiment V

[0188] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute the steps of the activation processing control method based on image processing described in the first embodiment.

[0189] The above describes specific embodiments of this specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily have to be performed in the specific order or continuous order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0190] The systems, devices, modules, or units illustrated in the above embodiments may be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0191] For convenience of description, when describing the above devices, they are described separately as various units according to their functions. Of course, when implementing this specification, the functions of each unit may be implemented in one or more software and / or hardware.

[0192] Those skilled in the art should understand that the embodiments of this specification may be provided as a method, a system, or a computer program product. Therefore, the embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0193] This specification is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of this specification. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices generate for implementation in the processFigure 1 means for a process or processes and / or blocks Figure 1 specified in one block or blocks.

[0194] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the functions specified in the process Figure 1 for a process or processes and / or blocks Figure 1 specified in one block or blocks.

[0195] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in the process Figure 1 for a process or processes and / or blocks Figure 1 specified in one block or blocks.

[0196] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0197] Memory may include non-permanent memory in computer-readable media, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

[0198] Computer-readable media includes both permanent and non-permanent, removable and non-removable media implemented by any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0199] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising said element.

[0200] This specification can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0201] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for system embodiments, since they are basically similar to method embodiments, they are described relatively simply, and reference can be made to the corresponding parts of the method embodiments for the relevant content.

[0202] Finally, it should be noted that the disclosed method and system for activation processing control based on image processing according to the embodiments of the present invention are only preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent substitution on some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An activation process control method based on image processing, characterized in that: The method comprises: Acquiring a regional image of an activation treatment area while the target activation treatment system performs a high temperature activation treatment; Determining the image during processing and the image after processing of the grenade shell in the regional image based on an image segmentation algorithm includes: Based on the trained image segmentation neural network, determining a plurality of grenade shell regions and a device image region of the high temperature processing device in the regional image; Inputting each of the grenade shell regions into a trained processing prediction neural network model to obtain a processing probability of each of the grenade shell regions having been subjected to high temperature processing; the processing prediction neural network model is trained using a training data set including a plurality of training grenade shell images and corresponding labels of whether or not the grenade shells have been subjected to high temperature processing; Calculating a first area distance between each of the grenade shell areas and the device image area; Screening out all the grenade shell regions whose processing probability is greater than a preset probability threshold, to obtain a plurality of processed shell regions; Filter out the grenade shell region with the smallest distance to the first region to obtain a processed image of the grenade shell; Calculating a second area distance between each of the processed shell areas and the processed image; Determine the processed shell region with the smallest distance to the second region as the processed image of the grenade shell; According to the image during processing and the image after processing, based on the image analysis algorithm, the activation processing problem corresponding to the target activation processing system is determined; the activation processing problem includes at least one processing problem and a corresponding problem parameter; the processing problem is an activation temperature problem, an activation timing problem, an activation position problem or an activation angle problem; the problem parameter is an activation temperature deviation parameter, an activation timing deviation parameter, an activation position deviation parameter or an activation angle deviation parameter; specifically including: Inputting the processed image into an image temperature prediction algorithm model to obtain predicted temperature information corresponding to the processed image; Calculating a temperature difference between the predicted temperature information and a preset temperature threshold; Determine whether the temperature difference is a negative number and the absolute value is greater than a preset difference threshold, and obtain a determination result; If the judgment result is yes, it is determined that the activation temperature problem exists, and the activation temperature deviation parameter of the activation temperature problem is determined as the temperature difference; Inputting the image during processing and the image after processing into a processing problem prediction multi-classifier to obtain a predicted processing problem result corresponding to the image after processing; Determine the activation timing problem, activation position problem or activation angle problem in the activation treatment problem and the corresponding problem parameters according to the prediction processing problem result; According to the activation processing problem, based on preset instruction generation rules, equipment control instructions corresponding to the high-temperature processing equipment of the target activation processing system are generated; the equipment control instructions are used to control the activation operation of the high-temperature processing equipment on the grenade shell to solve the activation processing problem.

2. The activation process control method based on image processing according to claim 1, characterized in that: The image segmentation neural network is trained by using a training data set including a plurality of training area images and corresponding grenade shell area annotations and high temperature processing equipment area annotations.

3. The activation process control method based on image processing according to claim 1, characterized in that: The processing problem prediction multi-classifier is an RNN neural network, which is trained by a training data set including multiple training processing images and corresponding training processed images and processing problem annotations, and performs parameter optimization based on a cross entropy loss function and a gradient descent algorithm until convergence; the processing problem annotations include processing timing problem annotations, processing timing deviation value annotations, processing position problem annotations, processing position deviation value annotations, processing angle problem annotations and processing angle deviation value annotations.

4. The activation process control method based on image processing according to claim 1, characterized in that: The generating of the equipment control instructions corresponding to the high temperature processing equipment of the target activation processing system according to the activation processing problem and based on the preset instruction generation rules includes: When the activation temperature problem exists in the activation treatment problem, a temperature control instruction is generated; the temperature control instruction includes a temperature control value, and the size of the temperature control value is proportional to the activation temperature deviation parameter; When the activation timing problem exists in the activation processing problem, an ignition control instruction is generated; the ignition control instruction includes an ignition time interval value, and the size of the ignition time interval value is proportional to the activation timing deviation parameter; When the activation position problem and / or the activation angle problem exist in the activation treatment problem, a fire port control instruction is generated according to the activation position deviation parameter and / or the activation angle deviation parameter; The generated temperature control instruction, the ignition control instruction and / or the fire outlet control instruction are sent to the high temperature processing equipment of the target activation processing system for execution.

5. The activation process control method based on image processing according to claim 4, characterized in that: The step of generating a fire port control instruction according to the activation position deviation parameter and / or the activation angle deviation parameter comprises: Determining current posture information of a fire outlet of the high temperature processing equipment; The activation position deviation parameter and / or the activation angle deviation parameter are used as ignition processing variation parameters and input into a posture simulation three-dimensional model corresponding to a high temperature processing device of a target activation processing system to obtain target posture information corresponding to the fire outlet; the posture simulation three-dimensional model is obtained by structural modeling of the high temperature processing device and building an ignition simulation environment; Calculating posture difference information between the target posture information and the current posture information; Generate a posture adjustment instruction corresponding to the posture difference information to obtain a fire outlet control instruction.

6. An activation process control system based on image processing, characterized in that: The system comprises: An acquisition module, used for acquiring a regional image of an activation treatment area while the target activation treatment system performs a high-temperature activation treatment; A segmentation module, used to determine the image during processing and the image after processing of the grenade shell in the regional image based on an image segmentation algorithm, comprising: Based on the trained image segmentation neural network, determining a plurality of grenade shell regions and a device image region of the high temperature processing device in the regional image; Inputting each of the grenade shell regions into a trained processing prediction neural network model to obtain a processing probability of each of the grenade shell regions having been subjected to high temperature processing; the processing prediction neural network model is trained using a training data set including a plurality of training grenade shell images and corresponding labels of whether or not the grenade shells have been subjected to high temperature processing; Calculating a first area distance between each of the grenade shell areas and the device image area; Screening out all the grenade shell regions whose processing probability is greater than a preset probability threshold, to obtain a plurality of processed shell regions; Filter out the grenade shell region with the smallest distance to the first region to obtain a processed image of the grenade shell; Calculating a second area distance between each of the processed shell areas and the processed image; Determine the processed shell region with the smallest distance to the second region as the processed image of the grenade shell; The analysis module is used to determine the activation treatment problem corresponding to the target activation treatment system based on the image during treatment and the image after treatment and based on the image analysis algorithm; the activation treatment problem includes at least one treatment problem and a corresponding problem parameter; the treatment problem is an activation temperature problem, an activation timing problem, an activation position problem or an activation angle problem; the problem parameter is an activation temperature deviation parameter, an activation timing deviation parameter, an activation position deviation parameter or an activation angle deviation parameter; specifically including: Inputting the processed image into an image temperature prediction algorithm model to obtain predicted temperature information corresponding to the processed image; Calculating a temperature difference between the predicted temperature information and a preset temperature threshold; Determine whether the temperature difference is a negative number and the absolute value is greater than a preset difference threshold, and obtain a determination result; If the judgment result is yes, it is determined that the activation temperature problem exists, and the activation temperature deviation parameter of the activation temperature problem is determined as the temperature difference; Inputting the image during processing and the image after processing into a processing problem prediction multi-classifier to obtain a predicted processing problem result corresponding to the image after processing; Determine the activation timing problem, activation position problem or activation angle problem in the activation treatment problem and the corresponding problem parameters according to the prediction processing problem result; A generation module is used to generate equipment control instructions corresponding to the high-temperature processing equipment of the target activation processing system according to the activation processing problem and based on preset instruction generation rules; the equipment control instructions are used to control the activation operation of the high-temperature processing equipment on the grenade shell to solve the activation processing problem.

7. An activation process control system based on image processing, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the activation processing control method based on image processing as described in any one of claims 1-5.

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