Activation processing control method and system based on image processing

Abstract

The invention discloses an activation processing control method and system based on image processing. The method comprises the following steps: when a target activation processing system executes high-temperature activation processing, obtaining a region image of an activation processing region; based on an image segmentation algorithm, determining a processed image and a processed image of the grenade shell in the regional image; according to the image during processing and the image after processing, determining an activation processing problem corresponding to the target activation processing system based on an image analysis algorithm; according to the activation processing problem, based on a preset instruction generation rule, generating an equipment control instruction corresponding to high-temperature processing equipment of the target activation processing system; the equipment control instruction is used for controlling the activation operation of the high-temperature treatment equipment on the lightning shell so as to solve the activation treatment problem. According to the invention, the working abnormity of the activation treatment system can be monitored and corrected more accurately and timely, the accuracy and efficiency of activation treatment are improved, and the product yield is improved.

Description

Technical Field

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

[0002] In the manufacturing process of grenades, the assembly process between the grenade shell and the fuze device is an important link, which affects the safety performance of the grenade product finally produced. Specifically, before the grenade shell and the fuze device are assembled, the plastic thread on the grenade shell needs to be subjected to high-temperature activation treatment to make it full of activation energy, so that it can be more firmly combined with the metal thread of the fuze device through double-tube adhesive in the future to avoid separation. Therefore, the processing temperature, processing position and processing time 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 is inefficient and has a high defect rate, and is prone to accidents. The activation treatment system implemented by some existing technologies through automation technology is generally based on mechanical automation control to achieve activation treatment, without further combining the analysis of the activation treatment image and data control technology, so its activation treatment efficiency is not high, the accuracy is insufficient, and there are safety hazards. It can be seen that the existing technology has defects and needs to be solved urgently. 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 monitor and correct the working abnormalities of the activation processing system more accurately and timely, improve the accuracy and efficiency of the activation processing, and improve the product yield.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses an activation process control method based on image processing, the method comprising: Acquiring a regional image of an activation treatment area while the target activation treatment system performs a high temperature activation treatment; Based on the image segmentation algorithm, determine the image during processing and the image after processing of the grenade shell in the regional image; Determine the activation processing problem corresponding to the target activation processing system based on the image during processing and the image after processing and an image analysis algorithm; According to the activation processing problem, based on the preset instruction generation rules, the 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.

[0005] As an optional implementation, in the first aspect of the present invention, determining the processed image and the processed image of the grenade shell in the regional image based on the image segmentation algorithm includes: Based on the trained image segmentation neural network, a plurality of grenade shell regions and a device image region of a high temperature processing device in the regional image are determined; the image segmentation neural network is trained by a training data set including a plurality of training regional images and corresponding grenade shell region annotations and high temperature processing device region annotations; 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; According to the processing probability and the first area distance, the processing image and the processed image of the grenade shell are screened out from the multiple grenade shell areas.

[0006] As an optional implementation, in the first aspect of the present invention, the step of selecting the processing image and the processed image of the grenade shell from the plurality of grenade shell regions according to the processing probability and the first region distance comprises: 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; The processed shell region with the smallest distance to the second region is determined as the processed image of the grenade shell.

[0007] As an optional embodiment, 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 parameter is an activation temperature deviation parameter, an activation timing deviation parameter, an activation position deviation parameter or an activation angle deviation parameter.

[0008] As an optional embodiment, in the first aspect of the present invention, determining 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 includes: 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; According to the predicted processing problem result, the activation timing problem, the activation position problem or the activation angle problem in the activation processing problem and the corresponding problem parameters are determined.

[0009] As an optional embodiment, in the first aspect of the present invention, 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 the cross entropy loss function and the gradient descent algorithm until convergence; the processing problem annotation includes processing timing problem annotation, processing timing deviation value annotation, processing position problem annotation, processing position deviation value annotation, processing angle problem annotation and processing angle deviation value annotation.

[0010] As an optional embodiment, in the first aspect of the present invention, generating, according to the activation treatment problem and based on a preset instruction generation rule, an equipment control instruction corresponding to the high temperature treatment equipment of the target activation treatment system comprises: 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 or the activation angle problem exists 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.

[0011] As an optional embodiment, in the first aspect of the present invention, generating a fire port control instruction according to the activation position deviation parameter and / or the activation angle deviation parameter includes: 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.

[0012] A second aspect of an embodiment of the present invention discloses an activation process control system based on image processing, the system comprising: 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 for determining the image during processing and the image after processing of the grenade shell in the regional image based on an image segmentation algorithm; An analysis module, for determining the activation processing problem corresponding to the target activation processing system based on the image during processing and the image after processing and an image analysis algorithm; 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.

[0013] As an optional implementation, in the second aspect of the present invention, the segmentation module determines the specific manner of the processed image and the processed image of the grenade shell in the regional image based on the image segmentation algorithm, including: Based on the trained image segmentation neural network, a plurality of grenade shell regions and a device image region of a high temperature processing device in the regional image are determined; the image segmentation neural network is trained by a training data set including a plurality of training regional images and corresponding grenade shell region annotations and high temperature processing device region annotations; 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; According to the processing probability and the first area distance, the processing image and the processed image of the grenade shell are screened out from the multiple grenade shell areas.

[0014] As an optional implementation, in the second aspect of the present invention, the specific manner in which the segmentation module selects the processing image and the processed image of the grenade shell from the multiple grenade shell regions according to the processing probability and the first region distance includes: 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; The processed shell region with the smallest distance to the second region is determined as the processed image of the grenade shell.

[0015] As an optional 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 parameter is an activation temperature deviation parameter, an activation timing deviation parameter, an activation position deviation parameter or an activation angle deviation parameter.

[0016] As an optional embodiment, in the second aspect of the present invention, the analysis module determines the specific method of the activation processing problem corresponding to the target activation processing system based on the image during processing and the image after processing and the image analysis algorithm, 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; According to the predicted processing problem result, the activation timing problem, the activation position problem or the activation angle problem in the activation processing problem and the corresponding problem parameters are determined.

[0017] As an optional embodiment, in the second aspect of the present invention, 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 the cross entropy loss function and the gradient descent algorithm until convergence; the processing problem annotation includes processing timing problem annotation, processing timing deviation value annotation, processing position problem annotation, processing position deviation value annotation, processing angle problem annotation and processing angle deviation value annotation.

[0018] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the generation module generates 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 or the activation angle problem exists 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.

[0019] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the generation module generates the firing port control instruction according to the activation position deviation parameter and / or the activation angle deviation parameter includes: 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.

[0020] The third aspect of the present invention discloses another activation process control system based on image processing, the system comprising: 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 part or all of the steps in the activation processing control method based on image processing disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they 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.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention can determine the processing image and the 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 processing image and the post-processing image, so as to determine the precise equipment control instructions to improve the activation processing problem, so as to monitor and correct the working abnormalities of the activation processing system more accurately and timely, improve the accuracy and efficiency of the activation processing, and improve the product yield. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a flow chart of an activation processing control method based on image processing disclosed in an embodiment of the present invention.

[0025] Figure 2 It is a structural schematic diagram of an activation processing control system based on image processing disclosed in an embodiment of the present invention.

[0026] Figure 3 It is a structural schematic diagram of another activation processing control system based on image processing disclosed in an embodiment of the present invention.

[0027] Figure 4 It is a schematic top view of the structure of an activation treatment system disclosed in an embodiment of the present invention.

[0028] Figure 5 It is a schematic side view of the partial structure of an activation treatment system disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] 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 "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. 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 may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0031] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0032] The present invention discloses an activation processing control method and system based on image processing, which can determine the processing image and the processed 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 processing image and the processed image, so as to determine the accurate equipment control instructions to improve the activation processing problem, so as to monitor and correct the abnormal operation of the activation processing system more accurately and timely, improve the accuracy and efficiency of the activation processing, and improve the product yield. The following are detailed descriptions.

[0033] Embodiment 1 See also Figure 1 , Figure 1 : is a flow chart of an activation process control method based on image processing disclosed in an embodiment of the present invention. Figure 1 The described activation process 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). Figure 1 As shown, the activation process control method based on image processing may include the following operations: 101. Acquire a regional image of an activation treatment area while a target activation treatment system performs a high-temperature activation treatment.

[0034] 102. Based on the image segmentation algorithm, determine the processing image and the processed image of the grenade shell in the regional image. 103. According to the images during processing and the images after processing, based on the image analysis algorithm, the activation processing problem corresponding to the target activation processing system is determined. 104. According to the activation processing problem, based on the preset instruction generation rules, generate equipment control instructions corresponding to the high temperature processing equipment of the target activation processing system.

[0035] Optionally, 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.

[0036] It can be seen that the above-mentioned embodiment of the invention can determine the processing image and the 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 processing image and the post-processing image, so as to determine the precise equipment control instructions to improve the activation processing problem, so as to more accurately and timely monitor and correct the working abnormalities of the activation processing system, improve the accuracy and efficiency of the activation processing, and improve the product yield.

[0037] As an optional embodiment, in the above step, determining the processing image and the processed image of the grenade shell in the regional image based on the image segmentation algorithm includes: Based on the trained image segmentation neural network, multiple grenade shell regions and equipment image regions of the high temperature processing equipment in the regional image are determined; 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 equipment region annotations; Input each grenade shell region into a trained processing prediction neural network model to obtain a processing probability corresponding to 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 a plurality of training grenade shell images and corresponding labels of whether or not they have been subjected to high temperature processing; Calculate the first area distance between each grenade shell area and the device image area; According to the processing probability and the first area distance, the processing image and the processed image of the grenade shell are screened out from the plurality of grenade shell areas.

[0038] It can be seen that through the above-mentioned optional embodiments, the grenade shell area and the equipment image area can be determined through the trained image segmentation neural network, and the processing image and the processed image of the grenade shell can be accurately screened through the predicted processing probability and area distance, which can be used to accurately analyze the activation processing problems corresponding to the activation processing system, and assist in more accurate and timely monitoring and correction of the working abnormalities of the activation processing system, thereby improving the accuracy and efficiency of the activation processing and improving the product yield.

[0039] As an optional embodiment, in the above step, selecting the processing image and the processed image of the grenade shell from the plurality of grenade shell regions according to the processing probability and the first area distance includes: Screening out all grenade shell regions whose processing probability is greater than a preset probability threshold, and obtaining a plurality of processed shell regions; Filter out the grenade shell area with the smallest distance to the first area, and obtain the processed image of the grenade shell; calculating a second region distance between each processed shell region and the image during processing; The processed shell region with the smallest second region distance is determined as the processed image of the grenade shell.

[0040] It can be seen that through the above-mentioned optional embodiments, the image of the grenade shell during processing and the image just after being processed can be determined based on the screening and calculation of the processing probability and the area distance respectively, which can be used to accurately analyze the activation processing problems corresponding to the activation processing system, and assist in more accurate and timely monitoring and correction of the working abnormalities of the activation processing system, thereby improving the accuracy and efficiency of the activation processing and improving product yield.

[0041] As an optional embodiment, in the above steps, 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 parameter is an activation temperature deviation parameter, an activation timing deviation parameter, an activation position deviation parameter or an activation angle deviation parameter.

[0042] It can be seen that through the above-mentioned optional embodiments, the content of the activation processing problem is limited, which can fully characterize the characteristics of the problems that may arise in the activation processing of the grenade shell thread, and is subsequently used to determine precise equipment control instructions to improve the activation processing problem, and assist in achieving more accurate and timely monitoring and correction of working abnormalities of the activation processing system, thereby improving the accuracy and efficiency of the activation processing and improving product yield.

[0043] As an optional embodiment, in the above steps, determining 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 the image analysis algorithm includes: Inputting the processed image into the image temperature prediction algorithm model to obtain the predicted temperature information corresponding to the processed image; Calculating the 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 there is an activation temperature problem, and the activation temperature deviation parameter of the activation temperature problem is determined as the temperature difference; 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; According to the prediction processing problem result, the activation timing problem, activation position problem or activation angle problem and the corresponding problem parameters in the activation processing problem are determined.

[0044] Specifically, the predicted processing problem results will include the processing problem predicted by the processing problem prediction multi-classifier on the image during processing and the image after processing, which may include one or more of activation timing problems, activation position problems or activation angle problems and corresponding problem parameters.

[0045] It can be seen that through the above-mentioned optional embodiments, it is possible to comprehensively determine the activation processing problems and problem parameters corresponding to the target activation processing system based on image temperature prediction and processing problem prediction multi-classifier algorithms, which can be used to subsequently determine precise equipment control instructions to improve the activation processing problems, assist in achieving more accurate and timely monitoring and correction of working abnormalities of the activation processing system, improve the accuracy and efficiency of the activation processing, and improve product yield.

[0046] As an optional embodiment, in the above steps, 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 the cross entropy loss function and the gradient descent algorithm until convergence; the processing problem annotation includes processing timing problem annotation, processing timing deviation value annotation, processing position problem annotation, processing position deviation value annotation, processing angle problem annotation and processing angle deviation value annotation.

[0047] It can be seen that through the above-mentioned optional embodiments, the model details and training details of the processing 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 equipment control instructions to improve activation processing problems, and assist in achieving more accurate and timely monitoring and correction of working abnormalities of the activation processing system, thereby improving the accuracy and efficiency of the activation process and improving product yield.

[0048] As an optional embodiment, in the above steps, according to the activation processing problem, based on the preset instruction generation rules, generating the equipment control instructions corresponding to the high temperature processing equipment of the target activation processing system includes: When there is an activation temperature problem in the activation treatment problem, a temperature control instruction is generated; optionally, 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 there is an activation timing problem in the activation process, an ignition control instruction is generated; optionally, 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 there is an activation position problem or an activation angle problem 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 instructions, ignition control instructions and / or fire outlet control instructions are sent to the high temperature processing equipment of the target activation processing system for execution.

[0049] It can be seen that through the above-mentioned optional embodiments, corresponding control instructions can be determined according to existing activation processing problems to improve activation processing problems, so as to achieve more accurate and timely monitoring and correction of working abnormalities of the activation processing system, improve the accuracy and efficiency of the activation processing, and improve product yield.

[0050] As an optional embodiment, in the above steps, generating a fire port control instruction according to the activation position deviation parameter and / or the activation angle deviation parameter includes: Determine the current posture information of the fire outlet of the high temperature processing equipment; The activation position deviation parameter and / or the activation angle deviation parameter are input as ignition processing variation parameters 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; optionally, the attitude simulation three-dimensional model is obtained by structural modeling of the high temperature processing equipment and building an ignition simulation environment; Calculate the posture difference information between the target posture information and the current posture information; Generate attitude adjustment instructions corresponding to the attitude difference information and obtain the fire outlet control instructions.

[0051] 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 the posture parameters and the corresponding ignition effect is built based on historical ignition data to achieve posture simulation. Specifically, after the activation position deviation parameter and / or the activation angle deviation parameter are input as ignition treatment change parameters into the posture simulation three-dimensional model, the current ignition effect can be determined based on the activation position deviation parameter and / or the activation angle deviation parameter, and then the target posture of the fire outlet of the high-temperature treatment equipment is simulated and inferred based on the ignition effect and the built simulation environment, and then the corresponding control instructions are generated based on the difference between the current posture and the target posture.

[0052] It can be seen that through the above-mentioned optional embodiments, the activation position deviation parameters and / or activation angle deviation parameters can be calculated and simulated through the posture simulation three-dimensional model to obtain the target posture information of the fire outlet, so as to accurately generate control instructions for the fire outlet based on the posture differences, thereby achieving more accurate and timely monitoring and correction of working abnormalities of the activation processing system, improving the accuracy and efficiency of the activation processing, and improving product yield.

[0053] In a specific embodiment, the structure of the activation treatment system referred to in the present invention can be seen in Figure 4 and Figure 5 It has a conveying mechanism for rotating and conveying the grenade shell to be activated and an ignition device for high-temperature activation. The ignition device is controlled by a control mechanism with multiple degrees of freedom, which can realize control transformation of different postures. The activation image is acquired by a camera device arranged in the activation area, and the image processing technology disclosed in the above embodiment is combined to analyze the problem and control the ignition device, so as to improve the activation efficiency and stability of the activation system.

[0054] Embodiment 2 See also Figure 2 , Figure 2 Schematic diagram of an activation control system based on image processing disclosed in an embodiment of the present invention. Figure 2The described activation process 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). Figure 2 As shown, the activation process control system based on image processing may include: The acquisition module 201 is used to acquire a regional image of the activation treatment area while the target activation treatment system performs high-temperature activation treatment.

[0055] The segmentation module 202 is used to determine the processing image and the processed image of the grenade shell in the regional image based on the image segmentation algorithm. The analysis module 203 is used 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 the image analysis algorithm. The generation module 204 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.

[0056] Optionally, 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.

[0057] It can be seen that the above-mentioned embodiment of the invention can determine the processing image and the 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 processing image and the post-processing image, so as to determine the precise equipment control instructions to improve the activation processing problem, so as to more accurately and timely monitor and correct the working abnormalities of the activation processing system, improve the accuracy and efficiency of the activation processing, and improve the product yield.

[0058] As an optional embodiment, the segmentation module determines the specific manner of the processed image and the processed image of the grenade shell in the regional image based on the image segmentation algorithm, including: Based on the trained image segmentation neural network, multiple grenade shell regions and equipment image regions of the high temperature processing equipment in the regional image are determined; 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 equipment region annotations; Input each grenade shell region into a trained processing prediction neural network model to obtain a processing probability corresponding to 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 a plurality of training grenade shell images and corresponding labels of whether or not they have been subjected to high temperature processing; Calculate the first area distance between each grenade shell area and the device image area; According to the processing probability and the first area distance, the processing image and the processed image of the grenade shell are screened out from the plurality of grenade shell areas.

[0059] It can be seen that through the above-mentioned optional embodiments, the grenade shell area and the equipment image area can be determined through the trained image segmentation neural network, and the processing image and the processed image of the grenade shell can be accurately screened through the predicted processing probability and area distance, which can be used to accurately analyze the activation processing problems corresponding to the activation processing system, and assist in more accurate and timely monitoring and correction of the working abnormalities of the activation processing system, thereby improving the accuracy and efficiency of the activation processing and improving the product yield.

[0060] As an optional embodiment, the segmentation module selects the processing image and the processed image of the grenade shell from the multiple grenade shell regions according to the processing probability and the first area distance, including: Screening out all grenade shell regions whose processing probability is greater than a preset probability threshold, and obtaining a plurality of processed shell regions; Filter out the grenade shell area with the smallest distance to the first area, and obtain the processed image of the grenade shell; calculating a second region distance between each processed shell region and the image during processing; The processed shell region with the smallest second region distance is determined as the processed image of the grenade shell.

[0061] It can be seen that through the above-mentioned optional embodiments, the image of the grenade shell during processing and the image just after being processed can be determined based on the screening and calculation of the processing probability and the area distance respectively, which can be used to accurately analyze the activation processing problems corresponding to the activation processing system, and assist in more accurate and timely monitoring and correction of the working abnormalities of the activation processing system, thereby improving the accuracy and efficiency of the activation processing and improving product yield.

[0062] As an optional embodiment, 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 parameter is an activation temperature deviation parameter, an activation timing deviation parameter, an activation position deviation parameter or an activation angle deviation parameter.

[0063] It can be seen that through the above-mentioned optional embodiments, the content of the activation processing problem is limited, which can fully characterize the characteristics of the problems that may arise in the activation processing of the grenade shell thread, and is subsequently used to determine precise equipment control instructions to improve the activation processing problem, and assist in achieving more accurate and timely monitoring and correction of working abnormalities of the activation processing system, thereby improving the accuracy and efficiency of the activation processing and improving product yield.

[0064] As an optional embodiment, the analysis module determines the specific method of the activation treatment problem corresponding to the target activation treatment system based on the image during processing and the image after processing and the image analysis algorithm, including: Inputting the processed image into the image temperature prediction algorithm model to obtain the predicted temperature information corresponding to the processed image; Calculating the temperature difference between the predicted temperature information and a preset temperature threshold; Determine whether the temperature difference is negative 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 there is an activation temperature problem, and the activation temperature deviation parameter of the activation temperature problem is determined as the temperature difference; 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; According to the prediction processing problem result, the activation timing problem, activation position problem or activation angle problem and the corresponding problem parameters in the activation processing problem are determined.

[0065] It can be seen that through the above-mentioned optional embodiments, it is possible to comprehensively determine the activation processing problems and problem parameters corresponding to the target activation processing system based on image temperature prediction and processing problem prediction multi-classifier algorithms, which can be used to subsequently determine precise equipment control instructions to improve the activation processing problems, assist in achieving more accurate and timely monitoring and correction of working abnormalities of the activation processing system, improve the accuracy and efficiency of the activation processing, and improve product yield.

[0066] As an optional embodiment, 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 the cross entropy loss function and the gradient descent algorithm until convergence; the processing problem annotation includes processing timing problem annotation, processing timing deviation value annotation, processing position problem annotation, processing position deviation value annotation, processing angle problem annotation and processing angle deviation value annotation.

[0067] It can be seen that through the above-mentioned optional embodiments, the model details and training details of the processing 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 equipment control instructions to improve activation processing problems, and assist in achieving more accurate and timely monitoring and correction of working abnormalities of the activation processing system, thereby improving the accuracy and efficiency of the activation process and improving product yield.

[0068] As an optional embodiment, the generation module generates a specific method of generating the equipment control instruction 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, including: When there is an activation temperature problem in the activation treatment problem, a temperature control instruction is generated; optionally, 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 there is an activation timing problem in the activation process, an ignition control instruction is generated; optionally, 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 there is an activation position problem or an activation angle problem 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 instructions, ignition control instructions and / or fire outlet control instructions are sent to the high temperature processing equipment of the target activation processing system for execution.

[0069] It can be seen that through the above-mentioned optional embodiments, corresponding control instructions can be determined according to existing activation processing problems to improve activation processing problems, so as to achieve more accurate and timely monitoring and correction of working abnormalities of the activation processing system, improve the accuracy and efficiency of the activation processing, and improve product yield.

[0070] As an optional embodiment, the specific manner in which the generation module generates the ignition control instruction according to the activation position deviation parameter and / or the activation angle deviation parameter includes: Determine the current posture information of the fire outlet of the high temperature processing equipment; The activation position deviation parameter and / or the activation angle deviation parameter are input as ignition processing variation parameters 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; optionally, the attitude simulation three-dimensional model is obtained by structural modeling of the high temperature processing equipment and building an ignition simulation environment; Calculate the posture difference information between the target posture information and the current posture information; Generate attitude adjustment instructions corresponding to the attitude difference information and obtain the fire outlet control instructions.

[0071] It can be seen that through the above-mentioned optional embodiments, the activation position deviation parameters and / or activation angle deviation parameters can be calculated and simulated through the posture simulation three-dimensional model to obtain the target posture information of the fire outlet, so as to accurately generate control instructions for the fire outlet based on the posture differences, thereby achieving more accurate and timely monitoring and correction of working abnormalities of the activation processing system, improving the accuracy and efficiency of the activation processing, and improving product yield.

[0072] Embodiment 3 See also Figure 3 , Figure 3 It is another activation processing control system based on image processing disclosed in an embodiment of the present invention. Figure 3 The described activation process 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). Figure 3 As shown, the activation process control system based on image processing may include: A memory 301 storing executable program codes; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the activation control method based on image processing described in the first embodiment.

[0073] Embodiment 4 An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the activation process control method based on image processing described in the first embodiment.

[0074] Embodiment 5 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 enable a computer to execute the steps of the activation processing control method based on image processing described in the first embodiment.

[0075] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0076] The systems, devices, modules or units described in the above embodiments may be 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 a combination of any of these devices.

[0077] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0078] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0079] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0080] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0081] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0082] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0083] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0084] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be 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 technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0085] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0086] This specification may 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 may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0087] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0088] Finally, it should be noted that the activation processing control method and system based on image processing disclosed in the embodiment of the present invention only discloses the preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various 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; Based on the image segmentation algorithm, determine the image during processing and the image after processing of the grenade shell in the regional image; Determine the activation processing problem corresponding to the target activation processing system based on the image during processing and the image after processing and an image analysis algorithm; 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 method of determining the image during processing and the image after processing of the grenade shell in the regional image based on the image segmentation algorithm includes: Based on the trained image segmentation neural network, a plurality of grenade shell regions and a device image region of a high temperature processing device in the regional image are determined; the image segmentation neural network is trained by a training data set including a plurality of training regional images and corresponding grenade shell region annotations and high temperature processing device region annotations; 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; According to the processing probability and the first area distance, the processing image and the processed image of the grenade shell are screened out from the multiple grenade shell areas.

3. The activation process control method based on image processing according to claim 2, characterized in that: The step of selecting the processing image and the processed image of the grenade shell from the plurality of grenade shell regions according to the processing probability and the first area distance includes: 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; The processed shell region with the smallest distance to the second region is determined as the processed image of the grenade shell.

4. The activation process control method based on image processing according to claim 1, characterized in that: 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 parameter is an activation temperature deviation parameter, an activation timing deviation parameter, an activation position deviation parameter or an activation angle deviation parameter.

5. The activation process control method based on image processing according to claim 4, characterized in that: The step of determining the activation processing problem corresponding to the target activation processing system based on the image during processing and the image after processing and an image analysis algorithm includes: 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; According to the predicted processing problem result, the activation timing problem, the activation position problem or the activation angle problem in the activation processing problem and the corresponding problem parameters are determined.

6. The activation process control method based on image processing according to claim 5, 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.

7. The activation process control method based on image processing according to claim 4, 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 or the activation angle problem exists 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.

8. The activation process control method based on image processing according to claim 7, 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.

9. 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 for determining the image during processing and the image after processing of the grenade shell in the regional image based on an image segmentation algorithm; An analysis module, for determining the activation processing problem corresponding to the target activation processing system based on the image during processing and the image after processing and an image analysis algorithm; 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.

10. 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 1-8.

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