Intelligent control configuration method and system of magnetic memory detector

Abstract

The invention relates to an intelligent control configuration method and system for a magnetic memory detector, and belongs to the technical field of magnetic memory detector control. Index data of the magnetic memory detector during detection are obtained, and finally the index data threshold range of the magnetic memory detector during detection is set; and identifying the target constraint condition feature data according to the index data of the magnetic memory detectors during detection to obtain an identification result, and performing multi-parameter configuration optimization on the real-time detection parameter combination feature data of each magnetic memory detector based on the identification result. Through fusion of the generative adversarial network and the genetic algorithm, detection index data corresponding to multiple target parameters can be detected, the coupling condition among the detection parameters of the magnetic memory detector is fully utilized, the detection parameters of the magnetic memory detector under condition parameters of different material types and the like can be further optimized, and the detection accuracy of the magnetic memory detector is improved. Therefore, the detection precision of the magnetic memory detector under different material types and other condition parameters can be improved.

Description

Technical Field

[0001] The present invention relates to the field of magnetic memory detector control technology, and in particular to an intelligent control configuration method and system for a magnetic memory detector. Background Art

[0002] Metal magnetic memory detection technology is a rapid nondestructive testing method that uses the metal magnetic memory effect to detect the stress concentration area of ​​the component. It overcomes the shortcomings of traditional nondestructive testing and can diagnose the stress concentration area inside the ferromagnetic metal component, that is, microscopic defects and early failure and damage, etc., to prevent sudden fatigue damage. It is a new detection method in the field of nondestructive testing. The main principle is that when ferromagnetic metal parts are processed and operated, due to the combined action of load and geomagnetic field, magnetic domain organization orientation and irreversible reorientation with magnetostrictive properties will occur in the stress and deformation concentration area. This irreversible change of magnetic state will not only be retained after the working load is eliminated, but also related to the maximum applied stress. This magnetic state on the surface of the metal component "memorizes" the location of microscopic defects or stress concentrations, which is the so-called magnetic memory effect. When the ferromagnetic component in the geomagnetic field environment is subjected to external loads, magnetic domain organization orientation and irreversible reorientation with magnetostrictive properties will occur in the stress concentration area, and fixed nodes of magnetic domains will appear in this part, generating magnetic poles and forming a demagnetization field, so that the magnetic permeability of the ferromagnetic metal here is minimized, and a leakage magnetic field is formed on the metal surface. The tangential component Hpx of the leakage magnetic field intensity has a maximum value, while the normal component Hpy changes its sign and has a zero value. This irreversible change in the magnetic state still retains the "memory" of the location of stress concentration after the working load is eliminated. However, the magnetic memory detector involves multiple parameters in the process of operation, and there may be certain coupling interference problems between the parameters. In the prior art, the possibility of certain coupling interference problems between the parameters is not considered, resulting in low detection accuracy during the detection process. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides an intelligent control configuration method and system for a magnetic memory detector.

[0004] To achieve the above object, the technical solution adopted by the present invention is:

[0005] The first aspect of the present invention provides an intelligent control configuration method for a magnetic memory detector, comprising the following steps:

[0006] Acquire multi-source characteristic data of the component to be detected currently, and initialize the detection parameter characteristic data of the magnetic memory detector according to the multi-source characteristic data of the component to be detected currently;

[0007] Constructing a parameter optimization objective constraint function based on the detection parameter characteristic data of the magnetic memory detector, and constructing a detection parameter index data prediction model according to the parameter optimization objective constraint function;

[0008] Acquire the real-time detection parameter combination characteristic data of each magnetic memory detector, predict the real-time detection parameter combination characteristic data of each magnetic memory detector according to the detection parameter index data prediction model, and acquire the index data of the magnetic memory detector during detection;

[0009] A threshold range of index data of the magnetic memory detector during detection is set, and the target constraint condition characteristic data is identified according to the index data of the magnetic memory detector during detection to obtain an identification result, and multi-parameter configuration optimization is performed on the real-time detection parameter combination characteristic data of each magnetic memory detector based on the identification result.

[0010] Furthermore, in the intelligent control configuration method of the magnetic memory detector, the multi-source characteristic data of the current component to be detected is obtained, and the detection parameter characteristic data of the magnetic memory detector is initialized according to the multi-source characteristic data of the current component to be detected, specifically:

[0011] Acquire multi-source feature data of the current component to be inspected, wherein the multi-source feature data includes material type features, thickness features of the component to be inspected, and spatial features of the component to be inspected;

[0012] Constructing a search tag based on the multi-source feature data, and performing a search through big data based on the search tag to obtain a detection parameter feature data range of the magnetic memory detector under each multi-source feature data;

[0013] Building a database according to the detection parameter characteristic data range of the magnetic memory detector under each multi-source characteristic data, and inputting the multi-source characteristic data of the current component to be detected into the database for data matching;

[0014] Through data matching, the detection parameter characteristic data range of the magnetic memory detector under the multi-source characteristic data of the current component to be detected is obtained, and the detection parameter characteristic data of the magnetic memory detector is initialized according to the detection parameter characteristic data range of the magnetic memory detector under the multi-source characteristic data of the current component to be detected.

[0015] Furthermore, in the intelligent control configuration method of the magnetic memory detector, a parameter optimization objective constraint function is constructed based on the detection parameter characteristic data of the magnetic memory detector, and a detection parameter index data prediction model is constructed according to the parameter optimization objective constraint function, specifically:

[0016] Setting a number of index data of magnetic memory detectors during detection, taking the index data of the magnetic memory detectors during detection as dependent variables, taking the detection parameter characteristic data of the magnetic memory detectors as independent variables to construct a parameter optimization objective constraint function, constructing a detection parameter index data prediction model based on a generative adversarial network, and at the same time, introducing a genetic algorithm;

[0017] Setting a genetic algebra based on the genetic algorithm, taking each independent variable of the parameter optimization objective constraint function as a constraint condition, and inputting the parameter optimization objective constraint function into the generative adversarial network;

[0018] The generator predicts the index data of the magnetic memory detector during detection according to the constraint conditions to generate an initial prediction result;

[0019] Inputting the initial prediction result into the discriminator for judgment, judging whether to accept the initial prediction result in combination with the constraint condition, and outputting the initial prediction result if accepted;

[0020] If not accepted, genetic iteration is performed based on the genetic algebra, and the generator continues to generate the next prediction result until the constraint conditions are met and the final prediction result is output.

[0021] Furthermore, in the intelligent control configuration method of the magnetic memory detector, the real-time detection parameter combination characteristic data of each magnetic memory detector is obtained, and the real-time detection parameter combination characteristic data of each magnetic memory detector is predicted according to the detection parameter index data prediction model to obtain the index data of the magnetic memory detector during detection, specifically:

[0022] Acquire real-time detection parameter combination characteristic data of each magnetic memory detector, and input the real-time detection parameter combination characteristic data of each magnetic memory detector into the detection parameter index data prediction model for prediction;

[0023] Through prediction, the index data of the magnetic memory detector during detection under the real-time detection parameter combination characteristic data of each magnetic memory detector is obtained, and the index data of the magnetic memory detector during detection is output.

[0024] Furthermore, in the intelligent control configuration method of the magnetic memory detector, a threshold range of the index data of the magnetic memory detector during detection is set, and the target constraint condition feature data is identified according to the index data of the magnetic memory detector during detection to obtain an identification result, specifically:

[0025] Setting a threshold range of index data of the magnetic memory detector during detection, and determining whether the index data of the magnetic memory detector during detection is within the threshold range of the index data of the magnetic memory detector during detection;

[0026] When the index data of the magnetic memory detector during detection is within the threshold range of the index data of the magnetic memory detector during detection, normal real-time detection parameter combination characteristic data is generated;

[0027] When the index data of the magnetic memory detector during detection is not within the index data threshold range of the magnetic memory detector during detection, generating abnormal real-time detection parameter combination feature data;

[0028] A recognition result is generated according to the normal real-time detection parameter combination feature data and the abnormal real-time detection parameter combination feature data, and the recognition result is output.

[0029] Furthermore, in the intelligent control configuration method of the magnetic memory detector, multi-parameter configuration optimization is performed on the real-time detection parameter combination characteristic data of each magnetic memory detector based on the recognition result, specifically:

[0030] When there is abnormal real-time detection parameter combination feature data in the recognition result, the magnetic memory detector corresponding to the abnormal real-time detection parameter combination feature data is obtained, and the detection parameter combination feature data of the abnormal magnetic memory detector is reconfigured;

[0031] Predict the index data of the reconfigured magnetic memory detector during detection by using the detection parameter index data prediction model, and if the index data of the reconfigured magnetic memory detector during detection is within the index data threshold range of the magnetic memory detector during detection, output the current detection parameter combination feature data;

[0032] If the index data of the reconfigured magnetic memory detector during detection is not within the threshold range of the index data of the magnetic memory detector during detection, reconfigure the current detection parameter combination feature data until it is within the threshold range of the index data of the magnetic memory detector during detection.

[0033] A second aspect of the present invention provides an intelligent control configuration system for a magnetic memory detector, comprising a memory and a processor, wherein the memory comprises an intelligent control configuration method program for a magnetic memory detector, and when the intelligent control configuration method program for a magnetic memory detector is executed by the processor, the steps of any one of the intelligent control configuration methods for a magnetic memory detector are implemented.

[0034] The third aspect of the present invention provides a computer-readable storage medium, including a program for the intelligent control configuration method of a magnetic memory detector. When the program for the intelligent control configuration method of a magnetic memory detector is executed by a processor, the steps of any one of the intelligent control configuration methods of a magnetic memory detector are implemented.

[0035] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0036] The present invention obtains multi-source feature data of the current component to be detected, and initializes the detection parameter feature data of the magnetic memory detector according to the multi-source feature data of the current component to be detected, and then constructs a parameter optimization target constraint function based on the detection parameter feature data of the magnetic memory detector, and constructs a detection parameter index data prediction model according to the parameter optimization target constraint function, so as to obtain the real-time detection parameter combination feature data of each magnetic memory detector, predict the real-time detection parameter combination feature data of each magnetic memory detector according to the detection parameter index data prediction model, obtain the index data of the magnetic memory detector during detection, finally set the index data threshold range of the magnetic memory detector during detection, and identify the target constraint condition feature data according to the index data of the magnetic memory detector during detection, obtain the identification result, and perform multi-parameter configuration optimization on the real-time detection parameter combination feature data of each magnetic memory detector based on the identification result. The present invention can detect the detection index data corresponding to multiple target parameters by integrating the generative adversarial network and the genetic algorithm, fully utilize the coupling between the detection parameters of the magnetic memory detector, and further optimize the detection parameters of the magnetic memory detector under different material types and other situation parameters, thereby improving the detection accuracy of the magnetic memory detector under different material types and other situation parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0038] Figure 1 An overall flow chart of an intelligent control configuration method for a magnetic memory detector is shown;

[0039] Figure 2 The invention shows a system block diagram of an intelligent control configuration system of a magnetic memory detector. DETAILED DESCRIPTION

[0040] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0041] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0042] like Figure 1 As shown, the first aspect of the present invention provides an intelligent control configuration method for a magnetic memory detector, comprising the following steps:

[0043] S102: Acquire multi-source characteristic data of the component to be detected, and initialize the detection parameter characteristic data of the magnetic memory detector according to the multi-source characteristic data of the component to be detected;

[0044] S104: constructing a parameter optimization objective constraint function based on the detection parameter characteristic data of the magnetic memory detector, and constructing a detection parameter index data prediction model according to the parameter optimization objective constraint function;

[0045] S106: obtaining real-time detection parameter combination characteristic data of each magnetic memory detector, predicting the real-time detection parameter combination characteristic data of each magnetic memory detector according to the detection parameter index data prediction model, and obtaining the index data of the magnetic memory detector during detection;

[0046] S108: Set the threshold range of the index data of the magnetic memory detector during detection, and identify the target constraint condition feature data according to the index data of the magnetic memory detector during detection, obtain the identification result, and perform multi-parameter configuration optimization on the real-time detection parameter combination feature data of each magnetic memory detector based on the identification result.

[0047] It should be noted that the present invention, by integrating generative adversarial networks and genetic algorithms, can detect detection index data corresponding to multiple target parameters, fully utilize the coupling between the detection parameters of the magnetic memory detector, and can further optimize the detection parameters of the magnetic memory detector under different material types and other situation parameters, thereby improving the detection accuracy of the magnetic memory detector under different material types and other situation parameters.

[0048] Furthermore, in the intelligent control configuration method of the magnetic memory detector, the multi-source characteristic data of the current component to be detected is obtained, and the detection parameter characteristic data of the magnetic memory detector is initialized according to the multi-source characteristic data of the current component to be detected, specifically:

[0049] Acquire multi-source feature data of the current component to be inspected, wherein the multi-source feature data includes material type features, thickness features of the component to be inspected, and spatial features of the component to be inspected;

[0050] Constructing a search tag based on multi-source feature data, and performing a search through big data based on the search tag to obtain the detection parameter feature data range of the magnetic memory detector under each multi-source feature data;

[0051] Building a database according to the detection parameter characteristic data range of the magnetic memory detector under each multi-source characteristic data, and inputting the multi-source characteristic data of the current component to be detected into the database for data matching;

[0052] Through data matching, the detection parameter characteristic data range of the magnetic memory detector under the multi-source characteristic data of the current component to be detected is obtained, and the detection parameter characteristic data of the magnetic memory detector is initialized according to the detection parameter characteristic data range of the magnetic memory detector under the multi-source characteristic data of the current component to be detected.

[0053] It should be noted that the detection parameter characteristic data of the magnetic memory detector include working parameters such as excitation current, sampling frequency, and filter cutoff frequency. Since different multi-source characteristic data have different detection parameter characteristic data, non-destructive testing is performed through this detection parameter characteristic data. Through this method, the detection parameter characteristic data range of the magnetic memory detector can be determined according to data such as material type characteristics, thickness characteristics of the parts to be detected, and spatial characteristics of the parts to be detected, so as to initially select the detection parameter characteristic data of the magnetic memory detector.

[0054] Furthermore, in the intelligent control configuration method of the magnetic memory detector, a parameter optimization objective constraint function is constructed based on the detection parameter characteristic data of the magnetic memory detector, and a detection parameter index data prediction model is constructed according to the parameter optimization objective constraint function, specifically:

[0055] Set several index data of magnetic memory detectors during detection, and use the index data of magnetic memory detectors during detection as dependent variables, use the detection parameter characteristic data of magnetic memory detectors as independent variables to construct parameter optimization objective constraint functions, and build a detection parameter index data prediction model based on generative adversarial networks. At the same time, introduce genetic algorithms;

[0056] The genetic algebra is set based on the genetic algorithm, each independent variable of the parameter optimization objective constraint function is used as a constraint condition, and the parameter optimization objective constraint function is input into the generative adversarial network;

[0057] The generator predicts the index data of the magnetic memory detector during detection according to the constraint conditions to generate an initial prediction result;

[0058] The initial prediction result is input into the discriminator for judgment, and the constraint conditions are combined to determine whether to accept the initial prediction result. If accepted, the initial prediction result is output;

[0059] If not accepted, genetic iteration is performed based on genetic algebra, and the generator continues to generate the next prediction result until the constraints are met and the final prediction result is output.

[0060] It should be noted that due to the possible coupling interference between parameters such as excitation current, sampling frequency, and filter cutoff frequency, the parameter optimization objective constraint function is predicted, learned, and analyzed through the generative adversarial network. The dependent variables of the parameter optimization objective constraint function (the index data of the magnetic memory detector during detection) include maximizing the defect recognition rate and minimizing the energy consumption, which can satisfy the following relationship:

[0061]

[0062] Among them, Q j The dependent variables of the objective constraint function for the jth parameter optimization include maximizing the defect recognition rate, minimizing energy consumption, etc., x i is the independent variable of the objective constraint function for the i-th parameter optimization, including the excitation current, sampling frequency, filter cutoff frequency and other parameters, where Q is the total amount of the dependent variable.

[0063] It should be noted that Q j (Due to the differences in the multi-source feature data of the components to be tested, it can be understood as an empirical value, which can be obtained from previous test data or through continuous learning of the generative adversarial network) i The method can solve the problem of coupling interference between the excitation current, sampling frequency, filter cutoff frequency and other parameters, and improve the detection accuracy of the magnetic memory detector.

[0064] Furthermore, in the intelligent control configuration method of the magnetic memory detector, the real-time detection parameter combination characteristic data of each magnetic memory detector is obtained, and the real-time detection parameter combination characteristic data of each magnetic memory detector is predicted according to the detection parameter index data prediction model to obtain the index data of the magnetic memory detector during detection, specifically:

[0065] Acquire the real-time detection parameter combination characteristic data of each magnetic memory detector, and input the real-time detection parameter combination characteristic data of each magnetic memory detector into the detection parameter index data prediction model for prediction;

[0066] Through prediction, the index data of the magnetic memory detector during detection under the real-time detection parameter combination characteristic data of each magnetic memory detector is obtained, and the index data of the magnetic memory detector during detection is output.

[0067] Furthermore, in the intelligent control configuration method of the magnetic memory detector, the threshold range of the index data of the magnetic memory detector during detection is set, and the target constraint condition feature data is identified according to the index data of the magnetic memory detector during detection to obtain the identification result, which is specifically:

[0068] Set the index data threshold range of the magnetic memory detector during detection, and determine whether the index data of the magnetic memory detector during detection is within the index data threshold range of the magnetic memory detector during detection;

[0069] When the index data of the magnetic memory detector during detection is within the index data threshold range of the magnetic memory detector during detection, normal real-time detection parameter combination characteristic data is generated;

[0070] When the index data of the magnetic memory detector during detection is not within the index data threshold range of the magnetic memory detector during detection, abnormal real-time detection parameter combination feature data is generated;

[0071] A recognition result is generated according to normal real-time detection parameter combination feature data and abnormal real-time detection parameter combination feature data, and the recognition result is output.

[0072] Furthermore, in the intelligent control configuration method of the magnetic memory detector, multi-parameter configuration optimization is performed on the real-time detection parameter combination characteristic data of each magnetic memory detector based on the recognition result, specifically:

[0073] When there is abnormal real-time detection parameter combination feature data in the recognition result, the magnetic memory detector corresponding to the abnormal real-time detection parameter combination feature data is obtained, and the detection parameter combination feature data of the abnormal magnetic memory detector is reconfigured;

[0074] Predict the index data of the reconfigured magnetic memory detector during detection by using the detection parameter index data prediction model; if the index data of the reconfigured magnetic memory detector during detection is within the index data threshold range of the magnetic memory detector during detection, output the current detection parameter combination feature data;

[0075] If the index data of the reconfigured magnetic memory detector during detection is not within the threshold range of the index data of the magnetic memory detector during detection, reconfigure the current detection parameter combination feature data until it is within the threshold range of the index data of the magnetic memory detector during detection.

[0076] It should be noted that this method can further optimize the index data of the reconfigured magnetic memory detector during detection, so that the index data of the reconfigured magnetic memory detector during detection is within the threshold range of the index data of the magnetic memory detector during detection, thereby optimizing the detection parameters of the magnetic detector.

[0077] In addition, the method may further comprise the following steps:

[0078] Obtaining the sensor sensitivity of the magnetic detector under each signal-to-noise ratio, and constructing a sensor sensitivity prediction model based on a deep neural network, and inputting the sensor sensitivity of the magnetic detector under each signal-to-noise ratio into the sensor sensitivity prediction model;

[0079] When the model parameters of the sensor sensitivity prediction model are within the preset model parameter range, the sensor sensitivity prediction model training is completed and the sensor sensitivity prediction model is output;

[0080] Acquire the signal-to-noise ratio characteristics of the magnetic detector under various detection environments and the detection environment information of the current magnetic detector through big data, and acquire the signal-to-noise ratio characteristics of the current magnetic detector based on the signal-to-noise ratio characteristics of the magnetic detector under various detection environments and the detection environment information of the current magnetic detector;

[0081] Input the current signal-to-noise ratio characteristic of the magnetic detector into the sensor sensitivity prediction model for prediction, and obtain the real-time sensor sensitivity characteristic. When the real-time sensor sensitivity characteristic is lower than the preset sensor sensitivity characteristic threshold, the corresponding collected data is used as abnormal data;

[0082] When the real-time sensor sensitivity characteristic is not lower than the preset sensor sensitivity characteristic threshold, the corresponding collected data is regarded as normal data. When it is abnormal data, the detection environment of the current magnetic detector is dynamically adjusted within the preset range.

[0083] It should be noted that the detection environment will affect the signal-to-noise ratio of the sensor in the magnetic detector, and the signal-to-noise ratio will affect the sensitivity of the sensor. The sensitivity of the sensor will affect whether the collected data is normal or abnormal. This method can improve the detection rationality of the magnetic memory detector.

[0084] like Figure 2 As shown, the second aspect of the present invention provides an intelligent control configuration system 4 for a magnetic memory detector, comprising a memory 41 and a processor 42. The memory 41 comprises an intelligent control configuration method program for a magnetic memory detector. When the intelligent control configuration method program for a magnetic memory detector is executed by the processor 42, any step of the intelligent control configuration method for a magnetic memory detector is implemented.

[0085] The third aspect of the present invention provides a computer-readable storage medium, including a program for the intelligent control configuration method of a magnetic memory detector. When the program for the intelligent control configuration method of a magnetic memory detector is executed by a processor, any step of the intelligent control configuration method of a magnetic memory detector is implemented.

[0086] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0087] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0088] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0089] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical disks, and other media that can store program codes.

[0090] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0091] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An intelligent control configuration method for a magnetic memory detector, characterized in that: The following steps are involved: Acquire multi-source characteristic data of the component to be detected currently, and initialize the detection parameter characteristic data of the magnetic memory detector according to the multi-source characteristic data of the component to be detected currently; Constructing a parameter optimization objective constraint function based on the detection parameter characteristic data of the magnetic memory detector, and constructing a detection parameter index data prediction model according to the parameter optimization objective constraint function; Acquire the real-time detection parameter combination characteristic data of each magnetic memory detector, predict the real-time detection parameter combination characteristic data of each magnetic memory detector according to the detection parameter index data prediction model, and acquire the index data of the magnetic memory detector during detection; A threshold range of index data of the magnetic memory detector during detection is set, and the target constraint condition characteristic data is identified according to the index data of the magnetic memory detector during detection to obtain an identification result, and multi-parameter configuration optimization is performed on the real-time detection parameter combination characteristic data of each magnetic memory detector based on the identification result.

2. The intelligent control configuration method of a magnetic memory detector according to claim 1, characterized in that: Acquire multi-source characteristic data of the current component to be detected, and initialize the detection parameter characteristic data of the magnetic memory detector according to the multi-source characteristic data of the current component to be detected, specifically: Acquire multi-source feature data of the current component to be inspected, wherein the multi-source feature data includes material type features, thickness features of the component to be inspected, and spatial features of the component to be inspected; Constructing a search tag based on the multi-source feature data, and performing a search through big data based on the search tag to obtain a detection parameter feature data range of the magnetic memory detector under each multi-source feature data; Building a database according to the detection parameter characteristic data range of the magnetic memory detector under each multi-source characteristic data, and inputting the multi-source characteristic data of the current component to be detected into the database for data matching; Through data matching, the detection parameter characteristic data range of the magnetic memory detector under the multi-source characteristic data of the current component to be detected is obtained, and the detection parameter characteristic data of the magnetic memory detector is initialized according to the detection parameter characteristic data range of the magnetic memory detector under the multi-source characteristic data of the current component to be detected.

3. The intelligent control configuration method of a magnetic memory detector according to claim 1, characterized in that: Based on the detection parameter characteristic data of the magnetic memory detector, a parameter optimization objective constraint function is constructed, and according to the parameter optimization objective constraint function, a detection parameter index data prediction model is constructed, specifically: Setting a number of index data of magnetic memory detectors during detection, taking the index data of the magnetic memory detectors during detection as dependent variables, taking the detection parameter characteristic data of the magnetic memory detectors as independent variables to construct a parameter optimization objective constraint function, constructing a detection parameter index data prediction model based on a generative adversarial network, and at the same time, introducing a genetic algorithm; Setting a genetic algebra based on the genetic algorithm, taking each independent variable of the parameter optimization objective constraint function as a constraint condition, and inputting the parameter optimization objective constraint function into the generative adversarial network; The generator predicts the index data of the magnetic memory detector during detection according to the constraint conditions to generate an initial prediction result; Inputting the initial prediction result into the discriminator for judgment, judging whether to accept the initial prediction result in combination with the constraint condition, and outputting the initial prediction result if accepted; If not accepted, genetic iteration is performed based on the genetic algebra, and the generator continues to generate the next prediction result until the constraint conditions are met and the final prediction result is output.

4. The intelligent control configuration method of a magnetic memory detector according to claim 1, characterized in that: Acquire the real-time detection parameter combination characteristic data of each magnetic memory detector, predict the real-time detection parameter combination characteristic data of each magnetic memory detector according to the detection parameter index data prediction model, and obtain the index data of the magnetic memory detector during detection, specifically: Acquire real-time detection parameter combination characteristic data of each magnetic memory detector, and input the real-time detection parameter combination characteristic data of each magnetic memory detector into the detection parameter index data prediction model for prediction; Through prediction, the index data of the magnetic memory detector during detection under the real-time detection parameter combination characteristic data of each magnetic memory detector is obtained, and the index data of the magnetic memory detector during detection is output.

5. The intelligent control configuration method of a magnetic memory detector according to claim 1, characterized in that: The index data threshold range of the magnetic memory detector during detection is set, and the target constraint condition feature data is identified according to the index data of the magnetic memory detector during detection to obtain the identification result, specifically: Setting a threshold range of index data of the magnetic memory detector during detection, and determining whether the index data of the magnetic memory detector during detection is within the threshold range of the index data of the magnetic memory detector during detection; When the index data of the magnetic memory detector during detection is within the threshold range of the index data of the magnetic memory detector during detection, normal real-time detection parameter combination characteristic data is generated; When the index data of the magnetic memory detector during detection is not within the index data threshold range of the magnetic memory detector during detection, generating abnormal real-time detection parameter combination feature data; A recognition result is generated according to the normal real-time detection parameter combination feature data and the abnormal real-time detection parameter combination feature data, and the recognition result is output.

6. The intelligent control configuration method of a magnetic memory detector according to claim 1, characterized in that: Based on the recognition results, multi-parameter configuration optimization is performed on the real-time detection parameter combination characteristic data of each magnetic memory detector, specifically: When there is abnormal real-time detection parameter combination feature data in the recognition result, the magnetic memory detector corresponding to the abnormal real-time detection parameter combination feature data is obtained, and the detection parameter combination feature data of the abnormal magnetic memory detector is reconfigured; Predict the index data of the reconfigured magnetic memory detector during detection by using the detection parameter index data prediction model, and if the index data of the reconfigured magnetic memory detector during detection is within the index data threshold range of the magnetic memory detector during detection, output the current detection parameter combination feature data; If the index data of the reconfigured magnetic memory detector during detection is not within the threshold range of the index data of the magnetic memory detector during detection, reconfigure the current detection parameter combination feature data until it is within the threshold range of the index data of the magnetic memory detector during detection.

7. An intelligent control configuration system for a magnetic memory detector, characterized in that: It includes a memory and a processor, wherein the memory includes a program for the intelligent control configuration method of a magnetic memory detector, and when the program for the intelligent control configuration method of a magnetic memory detector is executed by the processor, the steps of the intelligent control configuration method of a magnetic memory detector as described in any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium, characterized in that: It comprises a program of intelligent control configuration method of a magnetic memory detector, and when the program of intelligent control configuration method of a magnetic memory detector is executed by a processor, the steps of the intelligent control configuration method of a magnetic memory detector as described in any one of claims 1-6 are implemented.

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