Microwave leakage detection method, apparatus and system

Abstract

This invention provides a microwave leakage detection method, apparatus, electronic device, and storage medium, belonging to the field of intelligent detection technology. The method includes: acquiring microwave detection data, including the leak locations of multiple leak detection points on the object under test and the microwave leakage detection quantity detected at each leak detection point; determining the location of the microwave leakage source based on the microwave detection data; and determining the microwave leakage intensity of the object under test based on the microwave leakage detection quantity detected at each leak detection point and the leakage quantity weight of each leak detection point. This invention utilizes microwave leakage-related data obtained from multiple leak detection points to objectively determine the specific location of the microwave leakage source, and generates leakage quantity weights based on the distance relationship between the microwave leakage source location and each leak detection point. Then, the microwave leakage intensity of the object under test is determined by a weighted summation method. This effectively achieves rapid and accurate location of the microwave leakage source, improving the automation level, efficiency, and accuracy of microwave leakage detection.

Description

Technical Field

[0001] This invention relates to the field of intelligent detection technology, and in particular to a microwave leakage detection method, device, electronic equipment, and storage medium. Background Technology

[0002] Currently, traditional microwave leakage detection mainly relies on inspectors holding handheld testing instruments to inspect each preset testing point on the equipment on the production line, and then roughly judging the location of the leakage source based on the inspectors' personal experience and the test results of each part.

[0003] However, this manual inspection method has the following shortcomings: Firstly, manual inspection is slow, and its results are greatly affected by the operator's skill level and fatigue, making it difficult to guarantee inspection efficiency and accuracy. Secondly, because the operator's personal experience makes it difficult to accurately locate the leak source, subsequent troubleshooting is time-consuming and laborious, and in actual production, it is prone to problems such as missed detections and false detections. Summary of the Invention

[0004] This invention provides a microwave leakage detection method, device, electronic device, and storage medium to solve the defects of existing technologies, such as slow detection speed, accuracy being easily affected by human factors, and difficulty in accurately locating the leakage source, which are caused by reliance on manual detection.

[0005] This invention provides a microwave leakage detection method, comprising the following steps:

[0006] Acquire microwave detection data, which includes the leak detection location of each leak detection point on the object under test and the microwave leakage detection amount detected at each leak detection point, wherein there are multiple leak detection points;

[0007] Based on the microwave detection data, the location of the microwave leakage source is determined;

[0008] The microwave leakage intensity of the object under test is determined based on the microwave leakage detection quantity detected at each of the leak detection points and the leakage quantity weight of each of the leak detection points.

[0009] The leakage weight of each leak detection point is related to the distance between the leak detection point and the location of the microwave leakage source.

[0010] According to a microwave leakage detection method provided by the present invention, determining the location of the microwave leakage source based on the microwave detection data includes:

[0011] Construct a correlation model between the microwave leakage detection quantity and the microwave detection distance for each leak detection point, wherein the microwave detection distance is the distance between the leak detection location of the leak detection point and the location of the microwave leakage source to be predicted;

[0012] Using the aforementioned correlation model, the microwave detection distance is mapped to the predicted microwave leakage detection quantity;

[0013] Construct an error function, which is the sum of squared errors between the predicted microwave leakage detection amount and the actual microwave leakage detection amount at all the leak detection points;

[0014] Based on the gradient descent method, with the goal of minimizing the error function, the location of the microwave leakage source to be predicted is iteratively updated until the optimal location of the microwave leakage source is determined.

[0015] According to a microwave leakage detection method provided by the present invention, the step of iteratively updating the location of the microwave leakage source to be predicted based on the gradient descent method with the objective of minimizing the error function until the optimal location of the microwave leakage source is determined includes:

[0016] For the k-th iteration, determine the initial location of the microwave leakage source to be predicted;

[0017] Calculate the microwave detection distance between each leak detection point and the initial position;

[0018] Based on the aforementioned correlation model, the predicted microwave leakage detection quantity corresponding to the microwave detection distance is calculated;

[0019] The predicted microwave leakage detection value is input into the error function to determine the error loss;

[0020] Determine the gradient of the error loss with respect to the location of the microwave leakage source;

[0021] The initial position of the microwave leakage source location to be predicted is updated based on the gradient and the learning rate.

[0022] Let k = k + 1, and repeat the iteration process until the error loss converges or the preset maximum number of iterations is reached, so as to determine the optimal microwave leakage source location.

[0023] According to a microwave leakage detection method provided by the present invention, determining the location of the microwave leakage source based on the microwave detection data includes:

[0024] The microwave leakage detection value and leakage location of each of the leak detection points are input into the leakage source location prediction model to obtain the microwave leakage source location output by the leakage source location prediction model;

[0025] The leak source location prediction model is obtained by training an initial network model using training data from multiple historical leak detection data samples. The historical leak detection data samples include leak detection location samples and microwave leak detection quantity samples for each leak detection point, as well as microwave leak source location labels.

[0026] According to a microwave leakage detection method provided by the present invention, determining the microwave leakage intensity of the object under test based on the microwave leakage detection quantity detected at each leak detection point and the leakage quantity weight of each leak detection point includes:

[0027] Determine the distance between the leak detection location and the microwave leakage source location at each leak detection point, as well as the propagation loss index;

[0028] Based on the distance and the propagation loss index, determine the initial weight of the leakage amount at each leak detection point;

[0029] The initial weights of the leakage amounts at all the leak detection points are normalized to obtain the leakage amount weight for each of the leak detection points.

[0030] By using the leakage amount weight of each of the leak detection points, the microwave leakage detection amount detected at each of the leak detection points is weighted and summed to obtain the microwave leakage intensity of the object under test.

[0031] According to a microwave leakage detection method provided by the present invention, the leak detection point on the object under test is determined based on the following steps:

[0032] Acquire the three-dimensional geometric model data of the object under test, wherein the three-dimensional geometric model data includes spatial information of the shell structure and internal key components;

[0033] Based on the three-dimensional geometric model data, a candidate set of leak detection points is generated, and the candidate leak detection points in the candidate set of leak detection points cover the microwave leakage sensitive area and the microwave uniform distribution area of ​​the object to be tested.

[0034] Based on the microwave propagation simulation method, the propagation path and electromagnetic field distribution of microwaves around the object under test are simulated, and the signal reception quality and coverage contribution of each candidate leak point are calculated.

[0035] A comprehensive evaluation function is constructed, which evaluates the detection effectiveness of each candidate leak point based on the occlusion degree, signal-to-noise ratio, and sensitivity of each candidate leak point.

[0036] Based on the comprehensive evaluation function and the spatial distribution uniformity of the leak detection points, an optimization objective function is constructed with the goal of maximizing the overall detection effectiveness and the distribution uniformity of the leak detection points. The maximization of the overall detection effectiveness is determined based on the detection effectiveness of each candidate leak detection point, and the distribution uniformity of the leak detection points is determined based on the distance between two adjacent candidate leak detection points.

[0037] Based on a heuristic optimization algorithm, a subset of leak detection points that meet the constraints are selected from the candidate set of leak detection points. The constraints include an upper limit on the number of leak detection points and a positioning accuracy requirement. The subset of leak detection points includes all the leak detection points.

[0038] According to a microwave leakage detection method provided by the present invention, the object to be tested is a microwave oven, and the leakage detection points are distributed in at least one of the following areas: the door seal, the perimeter of the outer casing, and the center of the bottom of the object to be tested.

[0039] According to a microwave leakage detection method provided by the present invention, after determining the microwave leakage intensity of the object under test, the method further includes:

[0040] Based on the comparison results between the microwave leakage intensity and the leakage intensity threshold, it is determined whether microwave leakage exceeds the standard.

[0041] If this occurs, the alarm device will output an alarm notification.

[0042] According to a microwave leakage detection method provided by the present invention, after determining the microwave leakage intensity of the object under test, the method further includes:

[0043] The location of the microwave leakage source and the intensity of the microwave leakage are displayed in the virtual model of the object under test.

[0044] The present invention also provides a microwave leakage detection device, comprising:

[0045] A data receiving unit is used to acquire microwave detection data, which includes the leak detection location of each leak detection point of the object under test and the microwave leakage detection amount detected at each leak detection point, wherein there are multiple leak detection points.

[0046] A leakage source location unit is used to determine the location of the microwave leakage source based on the microwave detection data.

[0047] An intensity determination unit is used to determine the microwave leakage intensity of the object under test based on the microwave leakage detection quantity detected at each of the leak detection points and the leakage quantity weight of each of the leak detection points.

[0048] The leakage weight of each leak detection point is related to the distance between the leak detection point and the location of the microwave leakage source.

[0049] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the microwave leakage detection method as described above.

[0050] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the microwave leakage detection method as described above.

[0051] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the microwave leakage detection method as described above.

[0052] The microwave leakage detection method, apparatus, electronic device, and storage medium provided by this invention objectively determine the specific location of the microwave leakage source by using microwave leakage-related data obtained from multiple leakage detection points. The leakage weight is generated based on the distance relationship between the microwave leakage source location and each leakage detection point, and then the microwave leakage intensity of the object under test is determined by weighted summation. This can effectively achieve rapid and accurate location of the microwave leakage source, and improve the automation level, efficiency, and accuracy of microwave leakage detection. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a flowchart illustrating the microwave leakage detection method provided by the present invention.

[0055] Figure 2 This is a schematic diagram of the process for determining the location of a microwave leakage source based on microwave detection data, provided by the present invention.

[0056] Figure 3 This is a schematic diagram of the fitting process of the correlation model between microwave leakage detection quantity and microwave detection distance provided by the present invention.

[0057] Figure 4 This is a schematic diagram of the process for determining microwave leakage intensity based on microwave leakage detection quantity and leakage quantity weight provided by the present invention.

[0058] Figure 5 This is a flowchart illustrating the leak detection point determination method provided by the present invention.

[0059] Figure 6 This is a schematic diagram showing the distribution of leak detection points in a microwave oven provided by the present invention.

[0060] Figure 7 This is a schematic diagram of the test process for the microwave leakage detection method provided by the present invention.

[0061] Figure 8 This is a schematic diagram of the structure of the microwave leakage detection device provided by the present invention.

[0062] Figure 9 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0064] It should be noted that, in the description of this invention, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. Those skilled in the art will understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0065] The following is combined Figures 1-9 This invention describes the microwave leakage detection method, apparatus, electronic device, and storage medium provided by the present invention.

[0066] Figure 1 This is a flowchart illustrating the microwave leakage detection method provided by the present invention, as shown below. Figure 1 As shown, including but not limited to the following steps:

[0067] Step 110: Obtain microwave detection data. The microwave detection data includes the leak detection location of each leak detection point on the object under test and the microwave leakage detection amount detected at each leak detection point. There are multiple leak detection points.

[0068] Specifically, this microwave leakage detection method can be applied to the automated production line testing process of equipment such as microwave ovens. The object under test can be a microwave oven, or other equipment that may generate microwave leakage, such as microwave communication equipment, industrial microwave heating equipment, etc. In the following embodiments, the microwave oven will be used as the object under test for illustration, which is not considered as a specific limitation on the scope of protection of this invention.

[0069] Optionally, multiple leak detection points can be pre-arranged around the microwave oven. The number of these points can be set according to the required detection accuracy and the structural complexity of the object being tested; for example, six leak detection points can be set. These points can be distributed in key locations of the microwave oven, such as the door seal, the perimeter of the outer casing, and the center of the bottom, to comprehensively collect microwave leakage information from the surrounding space.

[0070] The microwave detection data includes the leak detection locations of each leak detection point in the microwave oven and the detected microwave leakage amount at each leak detection point. The leak detection location is the spatial coordinate of each leak detection point within the detection environment of the object under test. For example, in a preset three-dimensional coordinate system, the leak detection location of each leak detection point can be represented by its three-dimensional coordinates (…). x, y, z The microwave leakage detection quantity is represented by the actual microwave energy intensity value measured by the microwave sensor at each leak detection point. This microwave energy intensity value can be expressed as power density (e.g., mW / cm²) or other energy units.

[0071] Specifically, the process of obtaining this microwave leakage detection quantity can be achieved through the following steps: A high-precision microwave sensor is configured at each leak detection point. This microwave sensor can employ a radially arranged antenna array design (e.g., an eight-antenna array) to receive microwave signals from multiple directions. The detected microwave electric field signal can be converted into an electrical signal proportional to the microwave intensity using conversion elements such as hot-carrier diodes.

[0072] Optionally, the execution entity of the microwave leakage detection method provided by the present invention can be a central processing unit, and the microwave sensor at each leak detection point is connected to the central processing unit through a signal transmission line. After the detection begins, the microwave sensor at each leak detection point sends the converted electrical signal to the central processing unit in real time or at a preset time interval (e.g., 0.1 seconds). After receiving the leak detection location information of all leak detection points (which can be pre-stored in the central processing unit) and the detected microwave leakage amount, the central processing unit forms complete microwave detection data for subsequent calculations and can store it in the data storage module.

[0073] Step 120: Determine the location of the microwave leakage source based on the microwave detection data.

[0074] Specifically, after acquiring microwave detection data containing the locations of multiple leak detection points and the microwave leakage detection quantity of each leak detection point, the specific spatial location of the source of the microwave leakage is determined by calculation using this microwave detection data, that is, the three-dimensional coordinates of the microwave leakage source location are output, for example ( x 0 ，y 0 ，z0 ).

[0075] Next, we will give a brief introduction to two methods for determining the location of microwave leakage sources.

[0076] Implementation method 1: The location of the microwave leakage source can be determined by constructing a mathematical model and solving it.

[0077] Considering that the energy of microwaves attenuates with increasing distance from the source as they propagate through space, a correlation model can be established to describe the mathematical relationship between the detected microwave leakage and the distance from the leak point to the microwave leakage source. Subsequently, an error function can be constructed, comprising the sum of squared errors between the predicted and actual detected microwave leakage at all leak points. Finally, aiming to minimize this error function, an optimization algorithm, such as gradient descent, is used to iteratively update the predicted microwave leakage source location until the error function converges or reaches a preset number of iterations. The obtained microwave leakage source location is then the optimal determination.

[0078] In Implementation Method 2, the location of the microwave leakage source can also be determined by a pre-trained leakage source location prediction model.

[0079] The leak source location prediction model can be a deep learning network model. During the model training phase, it learns a large number of historical leak detection data samples, where each sample contains the leak detection location of multiple leak detection points, the microwave leak detection quantity, and the corresponding known real microwave leak source location label.

[0080] After completing the model training, during the detection phase, simply input the newly acquired microwave detection data (i.e., the location and leakage amount of all leak detection points) into the leak source location prediction model after model training, and you can obtain the microwave leak source location output by the leak source location prediction model.

[0081] Step 130: Determine the microwave leakage intensity of the object under test based on the microwave leakage detection quantity detected at each leak detection point and the leakage quantity weight of each leak detection point; the leakage quantity weight of each leak detection point is related to the distance between the leak detection point and the microwave leakage source location.

[0082] Existing manual detection methods cannot fully reflect the overall microwave leakage situation of equipment. This invention, after determining the location of the microwave leakage source, can further provide a quantitative and comprehensive assessment of the overall leakage situation of the object under test. Specifically, this invention generates a more scientific and comprehensive assessment result by weighting the detection data from all leakage points.

[0083] Considering that the closer the leak detection point is to the microwave leakage source, the more critical the data detected is for assessing the severity of the leakage, this invention introduces a distance-related leakage amount weight, so that the data from the leak detection point closer to the leakage source has a greater weight in the final assessment. This is more in line with the physical laws of microwave energy propagation and attenuation in space.

[0084] For example, the leakage weight of each leak detection point can be determined based on the distance between the leak detection point and the microwave leakage source location, and then the microwave leakage intensity can be calculated by weighted summation of the microwave leakage detection values ​​of all leak detection points.

[0085] The microwave leakage detection method provided in this embodiment uses microwave leakage-related data obtained from multiple leak detection points to objectively determine the specific location of the microwave leakage source. It generates leakage weights based on the distance relationship between the microwave leakage source location and each leak detection point, and then determines the microwave leakage intensity of the object under test by weighted summation. This method can effectively achieve rapid and accurate location of the microwave leakage source, and improve the automation level, efficiency and accuracy of microwave leakage detection.

[0086] Figure 2 This is a schematic diagram of the process for determining the location of a microwave leakage source based on microwave detection data provided by the present invention, as shown below. Figure 2 As shown, in one optional embodiment, determining the location of the microwave leakage source based on microwave detection data specifically includes the following steps:

[0087] Step 210: Construct a correlation model between the microwave leakage detection quantity and the microwave detection distance at each leak detection point.

[0088] Among them, microwave detection distance refers to the distance between the leak detection point and the location of the microwave leakage source to be predicted.

[0089] Considering that the intensity of microwave energy attenuates with increasing distance from the leakage source as it propagates through space, the detected microwave leakage at any leak detection point has a specific functional relationship with the distance between it and the microwave leakage source (i.e., the microwave detection distance). Therefore, this invention establishes a mathematical function as a correlation model that can quantitatively describe the relationship between the detected microwave leakage and the propagation distance.

[0090] Furthermore, this correlation model can be constructed as a quadratic polynomial function, the specific form of which is shown in formula (1):

[0091] (1)

[0092] in, Represents the first calculated based on this association model Predicted microwave leakage detection quantity for each leak detection point Representing the The distance between the side leak location of each leak detection point and the location of the microwave leakage source to be predicted. , and These are the fitting coefficients that have been pre-calibrated using experimental data.

[0093] Optionally, the fitting coefficients , and This can be determined through pre-fitting. For example, microwave leaks at known locations can be created at different points on the object under test, and the corresponding microwave leak detection values ​​at all leak detection points can be measured to obtain a set of training data containing known distances and measured leak values. Subsequently, using this set of training data, the optimal fitting coefficients can be solved using a curve fitting algorithm (such as the least squares method). , and The value of .

[0094] Step 220: Using the above correlation model, the determined microwave detection distance is mapped to the predicted microwave leakage detection quantity.

[0095] The above-mentioned process for determining the predicted microwave leakage detection quantity is a forward calculation process after establishing the correlation model. Its purpose is to determine the location of a microwave leakage source to be predicted. x 0 ，y 0 ，z 0 Using this correlation model, the forward derivation of the leakage point at the microwave leakage source location is obtained for each leak detection point. x i ，y i ，z i The theoretically detectable microwave leakage amount, i.e., the predicted microwave leakage amount. The predicted microwave leakage detection amount will be used in subsequent steps to compare with the actual detected microwave leakage amount in order to assess the accuracy of the current predicted microwave leakage source location.

[0096] Step 230: Construct an error function, which is the sum of squared errors between the predicted microwave leakage detection amount and the actual microwave leakage detection amount at all leak detection points.

[0097] This invention establishes a quantitative index to evaluate the location of the microwave leakage source to be predicted in step 220. x 0 ，y 0，z 0 The error function calculates the deviation between the predicted microwave leakage detection value and the actual microwave leakage detection value obtained in step 110. The smaller the output value of this error function, the closer the predicted microwave leakage source location is to the actual microwave leakage source location.

[0098] Alternatively, the error function is specifically shown in formula (2):

[0099] (2)

[0100] Furthermore, Substituting the expression into the formula yields formula (3):

[0101] (3)

[0102] in, This represents the error function. Representing the i The actual amount of microwave leakage detected at each leak detection point Represents the number calculated based on the current position to be predicted. i Predicted microwave leakage detection quantity for each leak detection point Representing the The distance between the side leak location of each leak detection point and the location of the microwave leakage source to be predicted. , and These are the fitting coefficients that have been pre-calibrated using experimental data.

[0103] Step 240: Based on the gradient descent method, with the goal of minimizing the error function, iteratively update the location of the microwave leakage source to be predicted until the optimal microwave leakage source location is determined.

[0104] This invention utilizes an iterative optimization algorithm based on gradient descent to gradually adjust the location of the microwave leakage source to be predicted. x 0 ，y 0 ，z 0 ), to find the error function The solution that reaches the minimum value. The basic principle of gradient descent is to move along the direction of the steepest descent of the function's gradient, thereby efficiently approximating the function's minimum point.

[0105] Next, we will briefly introduce the implementation process for iteratively updating the predicted microwave leakage source location, which mainly includes the following steps:

[0106] (1) Initialization: The location of the microwave leakage source to be predicted ( x0 ，y 0 ，z 0 Set an initial position The initial position can be a preset point at the geometric center of the object under test, or a point randomly selected based on experience.

[0107] (2) Iterative calculation: For the th k The next iteration ( k Starting from 0), first, calculate the error function. At the current location gradient grad[ The gradient is a vector that indicates the direction in which the error function value increases most rapidly at the current position. Then, based on this gradient and the preset learning rate... The location of the microwave leakage source to be predicted is updated.

[0108] Optionally, the update rule for the location of the microwave leakage source is as shown in formula (4):

[0109] (4)

[0110] in, This is the location of the microwave leakage source after the update. It is the first k Location of microwave leakage source in the next iteration. It is the learning rate, used to control the step size of each iteration update. [This is the error function at the current position] The gradient.

[0111] (3) Termination of judgment: Let k=k +1, repeat the above iterative calculation process until the preset convergence condition is met. The convergence condition may be: the change in the value of the error function is less than a very small preset threshold; or, the number of iterations reaches the preset maximum number of iterations.

[0112] It should be noted that when the iteration process terminates, the location of the microwave leakage source to be predicted obtained from the last iteration ( x 0 ，y 0 ，z 0 This means that the location of the microwave leakage source is determined to be the optimal location.

[0113] The microwave leakage detection method provided by this invention transforms the problem of locating the leakage source into a mathematical optimization problem based on the microwave propagation characteristics, and uses the gradient descent method to iteratively solve the problem, thereby achieving accurate, efficient and reliable localization of the microwave leakage source.

[0114] Figure 3 This is a schematic diagram of the fitting process of the correlation model between microwave leakage detection quantity and microwave detection distance provided by the present invention, as shown in the figure. Figure 3 As shown, the fitting process first inputs the coordinates of multiple (e.g., 6) leak detection points and the microwave leakage detection data detected at each point. Next, the microwave leakage detection data is preprocessed. This preprocessing may include operations such as filtering, noise reduction, and normalization.

[0115] Filtering and denoising can remove signal noise interference that may be introduced during sensor acquisition, while normalization can process data of different orders of magnitude into the same interval. The preprocessed microwave detection data will be used to perform fitting calculations to determine the location of the microwave leakage source. This fitting calculation can employ a multiple linear regression fitting method, specifically using the quadratic polynomial function in step 210. Finally, the fitting algorithm is executed, and the location coordinates of the microwave leakage source are determined through calculation using an error function, and these coordinates are output.

[0116] In another embodiment of the present invention, based on the gradient descent method, the location of the microwave leakage source to be predicted is iteratively updated with the goal of minimizing the error function until the optimal microwave leakage source location is determined, including:

[0117] For the k In the next iteration, the initial position of the microwave leakage source to be predicted is determined; the microwave detection distance between each leak detection point and the initial position is calculated; based on the obtained correlation model, the predicted microwave leakage detection quantity corresponding to the microwave detection distance is calculated; the predicted microwave leakage detection quantity is input into the error function to determine the error loss; the gradient of the error loss with respect to the microwave leakage source position is determined; the initial position of the microwave leakage source to be predicted is updated according to the gradient and the learning rate; let... k = k +1, repeat the above iterative process until the error loss converges or the preset maximum number of iterations is reached, in order to determine the optimal microwave leakage source location.

[0118] Specifically, this iterative update process based on gradient descent with the objective of minimizing the error function includes, but is not limited to, the following steps:

[0119] First, before the iteration begins, an initial location needs to be determined for the microwave leakage source to be predicted. .

[0120] Then, the iterative process begins. For the... k The next iteration ( k (where the integer is non-negative), and the specific operations are as follows:

[0121] (1) Calculate the location of the microwave leakage source for each leak detection point in the current iteration. The microwave detection distance between them.

[0122] (2) Based on the correlation model, the calculated microwave detection distance is mapped to the predicted microwave leakage detection amount corresponding to each leak detection point.

[0123] (3) Input the predicted microwave leakage detection quantity into the error function and compare it with the actual detected microwave leakage quantity to determine the error loss under the current iteration.

[0124] (4) Determine the error loss relative to the current microwave leakage source location. The gradient.

[0125] (5) Based on the calculated gradient and the preset learning rate, update the location of the microwave leakage source to be predicted to obtain the location for the next iteration. .

[0126] Next, order k = k The algorithm increments the value by 1 and checks if the termination condition is met. If not, the iteration process is repeated until the error loss converges (e.g., the difference between the error losses of two consecutive iterations is less than a preset minimum value) or the number of iterations reaches a preset maximum number of iterations, at which point the iteration terminates. The final location obtained at this point is determined as the optimal microwave leakage source location.

[0127] The microwave leakage detection method provided by this invention decomposes the iterative process of the gradient descent method into a series of specific and orderly operation steps, such as initialization, distance calculation, leakage prediction, error determination, gradient calculation, and position update. This method can further enhance the operability, process controllability, and reproducibility of the localization algorithm while providing a clear and efficient localization path.

[0128] In another embodiment of the present invention, the location of the microwave leakage source is determined based on microwave detection data, and the specific steps include, but are not limited to:

[0129] The microwave leakage detection quantity and leakage location of each leak detection point are input into the leakage source location prediction model to obtain the microwave leakage source location output by the leakage source location prediction model. The leakage source location prediction model is obtained by training the initial network model with training data from multiple historical leak detection data samples. The historical leak detection data samples include the leakage location sample and microwave leakage detection quantity sample of each leak detection point, as well as the microwave leakage source location label.

[0130] Specifically, the steps for determining the location of the microwave leakage source may include: inputting the microwave leakage detection quantity and leakage location of each leakage detection point into a pre-trained leakage source location prediction model, and directly obtaining the microwave leakage source location output by the leakage source location prediction model.

[0131] Optionally, the leak source location prediction model is a deep learning network model, such as a multi-layer perceptron (MLP) or other neural network model.

[0132] The leak source location prediction model is obtained by training an initial network model. The training process utilizes a large number of historical leak detection data samples as training data. Each historical leak detection data sample includes a training sample and a training label. The training sample includes leak location samples and microwave leak detection quantity samples of all leak detection points in a single historical measurement. The training label includes the known real microwave leak source location label corresponding to the training sample.

[0133] Specifically, the training process of the leak source location prediction model may include, but is not limited to, the following steps:

[0134] (1) Prepare a certain number of historical leak test data samples. These historical leak test data samples can be obtained by creating microwave leakage at multiple different known locations on the object under test in the experimental environment and collecting corresponding multi-point sensor data.

[0135] (2) Select a historical leak detection data sample from the training data, and input the training sample (i.e. the location and leakage amount of all leak detection points) into the initial network model to obtain a predicted microwave leak source location.

[0136] (3) Compare the predicted location output by the initial network model with the training label (i.e. the actual microwave leakage source location) in the historical leak detection data sample, and calculate the error between the two, such as the mean squared error (MSE).

[0137] (4) Based on the calculated error, the backpropagation algorithm and optimizer (e.g., Adam optimizer) are used to adjust the network parameters (e.g., weights and biases) inside the initial network model so that the model’s next prediction result is closer to the true label.

[0138] (5) Repeat steps (2) to (4) above until all training data have been traversed or the performance of the model on the validation set reaches the preset standard, and the final leak source location prediction model is obtained.

[0139] The microwave leakage detection method provided by this invention learns a large amount of historical data through a leakage source location prediction model, which can discover the complex nonlinear relationship between the microwave leakage detection quantity and the leakage source location, thereby improving the positioning accuracy and robustness.

[0140] Figure 4 This is a schematic diagram of the process for determining microwave leakage intensity based on microwave leakage detection quantity and leakage quantity weight, as provided by the present invention. Figure 4 As shown, as an optional embodiment, the microwave leakage intensity of the object under test is determined based on the microwave leakage detection quantity detected at each leak detection point and the leakage quantity weight of each leak detection point, specifically including:

[0141] Step 410: Determine the distance between the leak detection location of each leak detection point and the microwave leakage source location, as well as the propagation loss index.

[0142] Specifically, the method for calculating the distance between the leak detection location of each leak detection point and the microwave leakage source location is shown in formula (5):

[0143] (5)

[0144] in, Representing the The distance between the side leak location of each leak detection point and the location of the microwave leakage source to be predicted, ( x i ，y i ，z i ) indicates the first i Leak detection locations of each leak detection point, ( x 0 ，y 0 ，z 0 () indicates the location of the microwave leakage source.

[0145] Furthermore, the propagation loss index is a parameter characterizing the rate attenuation of a microwave signal with increasing distance in a specific detection environment.

[0146] Optionally, the propagation loss index can be an empirical value preset based on the electromagnetic characteristics of the detection environment, or a fixed value obtained through prior experimental calibration. For example, an optimal propagation loss index can be determined through multiple experimental measurements for a specific model of the object under test and the layout of the production line, and stored as a fixed parameter in the microwave leakage detection device for subsequent calculations.

[0147] Step 420: Determine the initial weight of the leakage amount at each leak detection point based on the distance and propagation loss index.

[0148] The closer the leak detection point is to the microwave leakage source, the greater the contribution of the leak detection point to the assessment of the overall leakage intensity. Therefore, the initial weight of the leakage amount corresponding to the leak detection point should also be greater. Thus, this invention assigns an unnormalized original weight value, i.e., the initial weight of the leakage amount, to each leak detection point to reflect the importance of each leak detection point relative to the determined microwave leakage source location.

[0149] Optionally, the initial weight of the leakage amount can be calculated as shown in formula (6):

[0150] (6)

[0151] in, Representing the i Initial weights of leakage at each leak detection point Representing the i The distance between each leak detection point and the location of the microwave leak source This represents the propagation loss index.

[0152] Step 430: Normalize the initial weights of the leakage amounts at all leak detection points to obtain the leakage amount weight for each leak detection point.

[0153] Specifically, this step transforms the initial leakage weights into a set of standardized final leakage weights that sum to 1. This normalization process ensures that the contribution of each leak detection point is included in the final microwave leakage intensity calculation in a relatively proportional manner, making the final evaluation results consistent and comparable.

[0154] Alternatively, this normalization process can be performed as shown in formula (7):

[0155] (7)

[0156] in, After normalization, the first i The final leakage weight of each leak detection point Representing the i The distance between each leak detection point and the location of the microwave leak source Represents the propagation loss index. This represents all leak detection points (e.g., j The sum is obtained by summing the initial weights of the leakage amount (with values ​​ranging from 1 to 6).

[0157] Step 440: Using the leakage weight of each leak detection point, the microwave leakage detection quantity detected at each leak detection point is weighted and summed to obtain the microwave leakage intensity of the object under test.

[0158] Specifically, this step involves multiplying the actual microwave leakage detected at each leak point by its corresponding leakage weight, and then summing the results of all leak points to obtain a single index that can comprehensively and quantitatively evaluate the overall microwave shielding performance of the object under test, namely the microwave leakage intensity.

[0159] Optionally, the microwave leakage intensity can be calculated using formula (8):

[0160] (8)

[0161] in, Represents microwave leakage intensity. Representative at the The amount of microwave leakage detected at each leak detection point. Representing the The leakage weight at each leak detection point. This leakage weight... The determination of the leakage rate is directly related to the distance between the leak detection point and the microwave leakage source; the closer the distance, the higher the leakage rate weight. Furthermore, the sum of the leakage rate weights for all leak detection points can be set to 1, i.e. .

[0162] The microwave leakage detection method provided by this invention determines the microwave leakage intensity of the object under test based on the microwave leakage detection quantity detected at each leak detection point and the leakage quantity weight of each leak detection point. It scientifically considers the distance relationship between each leak detection point and the leakage source, thus reflecting the overall leakage level of the object under test more comprehensively and accurately than a single detection point.

[0163] Figure 5 This is a flowchart illustrating the leak detection point determination method provided by the present invention, as shown below. Figure 5 As shown, in another embodiment of the present invention, the leak detection point on the object to be tested can be determined based on the following steps:

[0164] Step 510: Obtain the three-dimensional geometric model data of the object to be tested. The three-dimensional geometric model data includes the spatial information of the shell structure and internal key components.

[0165] Specifically, the three-dimensional geometric model data can be a digital model file created by computer-aided design (CAD) software (such as AutoCAD, SolidWorks, etc.), which accurately describes the complete spatial structure of the object under test (such as a microwave oven).

[0166] Optionally, the three-dimensional geometric model data should include not only the shell structure information of the object under test (e.g., oven door, door seal, shell seams, observation window, etc.), but also the spatial information of its internal key components. These internal key components mainly refer to microwave generating or conducting components, such as the magnetron and waveguide in a microwave oven. Obtaining the spatial information of these internal components is crucial because these components are potential sources of microwave leakage, and their location directly affects the possible path of microwave leakage and the distribution of the external field.

[0167] Specifically, the acquisition process can be achieved by importing the design drawing file of the object under test into a software platform for electromagnetic simulation or a specialized layout optimization program.

[0168] Step 520: Based on the three-dimensional geometric model data, generate a candidate set of leak detection points. The candidate leak detection points in the candidate set cover the microwave leakage sensitive area and the microwave uniform distribution area of ​​the object under test.

[0169] Optionally, the process of generating the candidate set of leak points may involve generating one or more virtual meshes around the outer surface of the object to be tested in the virtual space represented by the three-dimensional geometric model data, and the nodes on the mesh constitute the candidate set of leak points.

[0170] Specifically, the generation of candidate leak points can be adaptively adjusted based on the following different regions:

[0171] (1) For microwave leakage sensitive areas, a higher density of candidate leak detection points will be generated. These microwave leakage sensitive areas refer to the locations most likely to experience microwave leakage based on physical structure and electromagnetic principles. For example, for a microwave oven, these sensitive areas include the door seal seam between the oven door and the oven body, the edge of the viewing window, the seams between the various panels of the outer casing, and the area on the outer casing closest to the internal magnetron. By generating a denser number of candidate leak detection points in these areas, it can be ensured that no critical leakage paths are overlooked in the assessment.

[0172] (2) For microwave uniform distribution areas, candidate leak detection points with relatively low density can be generated. This microwave uniform distribution area usually refers to a large area with a simple structure and flat surface on the object under test, such as the top, side, and bottom center areas of a microwave oven. The purpose of arranging candidate leak detection points in these areas is to ensure that the final selected leak detection point layout has good spatial coverage, can capture unexpected leaks, and provide more comprehensive spatial data for the leak source localization algorithm, thereby improving the robustness and accuracy of the localization algorithm.

[0173] Step 530: Based on the microwave propagation simulation method, simulate the propagation path and electromagnetic field distribution of microwaves around the object under test, and calculate the signal reception quality and coverage contribution of each candidate leak point.

[0174] Specifically, the microwave propagation simulation method can be either the Finite-Difference Time-Domain (FDTD) method or the Finite Element Method (FEM). This microwave propagation simulation is performed in a virtual environment containing the three-dimensional geometric model data of the object under test. Optionally, the simulation process involves setting up virtual microwave leakage sources one by one in multiple microwave leakage-sensitive areas of the model (e.g., different locations on a door seal) and simulating the electromagnetic field distribution formed as the leakage propagates outwards.

[0175] Furthermore, the microwave leak detection device will calculate the signal reception quality and coverage contribution for each candidate leak detection point.

[0176] Optionally, the signal reception quality can be comprehensively measured by one or more of the following metrics:

[0177] (1) Signal-to-noise ratio (SNR): For a simulated leak source, the ratio between the simulated signal strength at the candidate leak detection point and the preset ambient background noise strength. The higher the SNR of the candidate leak detection point, the stronger its ability to detect weak leak signals.

[0178] (2) Sensitivity: The degree to which the simulated signal intensity at the candidate leak detection point responds to changes in the power of the simulated leak source. The higher the sensitivity of the candidate leak detection point, the more sensitive it is to changes in leakage.

[0179] (3) Obstruction: The degree to which the propagation path from multiple different simulated leak source locations to the candidate leak detection point is obstructed by the structure of the object under test. The lower the obstruction, the wider the "field of view" of the candidate leak detection point, and the less likely it is to fail due to changes in the leak location.

[0180] Optionally, the coverage contribution assesses the role of the candidate leak detection point in improving the spatial diversity of the entire detection network. A high coverage contribution from a candidate leak detection point means that it can effectively detect areas that are difficult for other candidate leak detection points to cover, or that it has low information redundancy with neighboring candidate leak detection points.

[0181] Step 540: Construct a comprehensive evaluation function. The comprehensive evaluation function evaluates the detection effectiveness of each candidate leak point based on the occlusion degree, signal-to-noise ratio, and sensitivity of each candidate leak point.

[0182] Specifically, the comprehensive evaluation function is a mathematical expression whose inputs are performance indicators such as occlusion degree, signal-to-noise ratio, and sensitivity, and whose output is a numerical value representing the effectiveness of the detection.

[0183] Optionally, the comprehensive evaluation function can be constructed as a weighted summation. Before calculation, the simulated original values ​​of the occlusion degree, signal-to-noise ratio, and sensitivity of each candidate leak point can be normalized to unify their numerical range (e.g., mapped to the [0, 1] interval) to eliminate the influence of differences in the dimensions and numerical ranges of different indicators. Subsequently, the specific form of the comprehensive evaluation function can be as shown in formula (9):

[0184] (9)

[0185] in, Indicates the first i The detection effectiveness score of each candidate leak detection point. , and Representing the first i The signal-to-noise ratio, sensitivity, and occlusion degree of each candidate leak detection point were normalized. Considering that occlusion degree is a negative indicator (the lower the value, the better), therefore, [the following parameters were used]. It transforms this into a positive contribution in the form of [the form]. m, n, q These are preset weighting coefficients, and their sum is 1. These weighting coefficients can be preset by the designer according to actual detection needs to reflect the relative importance of different performance indicators. For example, if the primary objective is to detect extremely weak leaks, the weighting coefficient corresponding to this signal-to-noise ratio can be increased. m .

[0186] Step 550: Based on the comprehensive evaluation function and the spatial distribution uniformity of the leak detection points, an optimization objective function is constructed with the goal of maximizing the overall detection effectiveness and the distribution uniformity of the leak detection points. Maximizing the overall detection effectiveness is determined based on the detection effectiveness of each candidate leak detection point, and the distribution uniformity of the leak detection points is determined based on the distance between two adjacent candidate leak detection points.

[0187] Specifically, this step is the core decision-making step in the entire leak detection point layout optimization method. Its purpose is to construct a mathematical optimization objective function, which will guide the algorithm to select a subset of leak detection points from a large candidate set that can both ensure optimal overall detection performance and achieve a reasonable spatial layout.

[0188] Optionally, the objective function is a function that needs to be maximized, which consists of two main parts:

[0189] (1) Overall Detection Effectiveness: This section aims to ensure that the selected subset of leak detection points has the strongest overall detection capability. This maximization of overall detection effectiveness is determined based on the detection effectiveness of each candidate leak detection point. For example, it can be expressed as the sum of the detection effectiveness scores of all selected candidate leak detection points. The larger the sum, the stronger the overall detection performance of the leak detection point combination.

[0190] (2) Uniformity of Leak Detection Point Distribution: This section aims to ensure that the final selected leak detection points are not overly concentrated in space, so as to achieve more comprehensive coverage of the space around the object to be tested. The uniformity of the leak detection point distribution is determined based on the distance between two adjacent candidate leak detection points in the subset. For example, the uniformity can be quantified as the minimum Euclidean distance between any two candidate leak detection points in the subset. The larger this minimum distance is, the more uniform the leak detection point distribution is, and the less dead zone the coverage.

[0191] Alternatively, the specific form of the optimization objective function can be as shown in formula (10):

[0192] (10)

[0193] in, This represents the value of the objective function to be maximized. This represents a subset of leak detection points selected from the candidate set of leak detection points. This represents the sum of the detection effectiveness scores of all candidate leak detection points in subset S. Represents any two candidate leak detection points in subset S. and The minimum distance between them and This is a preset balancing factor, whose sum can be 1. This balancing factor is used to adjust the relative importance of overall detection effectiveness and distribution uniformity in the final evaluation. If it is desirable to prioritize detection performance, this factor can be increased. If you want to prioritize ensuring uniform coverage, you can increase the size of the area. .

[0194] Step 560: Based on a heuristic optimization algorithm, select a subset of leak detection points that meet the constraints from the candidate set of leak detection points; the constraints include an upper limit on the number of leak detection points and a positioning accuracy requirement, and the subset of leak detection points includes all leak detection points.

[0195] Specifically, the heuristic optimization algorithm can be a genetic algorithm (GA), a particle swarm optimization algorithm (PSO), or a simulated annealing algorithm (SA), etc.

[0196] For example, taking the genetic algorithm as an example, its execution process can be as follows:

[0197] (1) Initialize the population: Randomly generate multiple different subsets of leak detection points from the candidate set of leak detection points. Each subset (i.e., an individual) contains candidate leak detection points that meet the upper limit of the number of leak detection points (e.g., 6). All these subsets constitute the initial population.

[0198] (2) Fitness assessment: For each individual in the population (i.e. each subset of leak test points), the fitness score is calculated using the optimization objective function constructed in step 450.

[0199] (3) Selection, crossover and mutation: Based on fitness scores, select excellent individuals for crossover (exchanging some test points) and mutation (replacing individual test points) operations to generate a new generation of population.

[0200] (4) Iteration: Repeat steps (2) and (3) until the overall fitness of the population converges or reaches the preset maximum number of generations.

[0201] During the execution of this heuristic optimization algorithm, preset constraints must always be followed, including the upper limit of the number of leak detection points and the positioning accuracy requirements.

[0202] For example, if a microwave leak detection device is planned to use 6 leak detection points, then the algorithm will only evaluate and iterate within a subset of 6 candidate leak detection points throughout the entire search process. For the subset of leak detection points with high fitness found by the algorithm during the iteration process, a quick verification simulation can be performed to check whether the positioning error of several standard leak sources meets the preset accuracy requirements (e.g., positioning error less than 1 cm) when using this subset layout.

[0203] Furthermore, the heuristic optimization algorithm outputs a subset of leak detection points that maximizes the value of the objective function while satisfying all constraints. This subset of leak detection points, ultimately output by the algorithm, represents all the leak detection points that will be adopted and deployed around the object under test.

[0204] The microwave leakage detection method provided by this invention, by introducing a systematic leak detection point layout method based on 3D model simulation and heuristic optimization algorithms during the design phase, can fundamentally solve the problem of leak detection point layout relying on manual experience and lacking scientific basis, while achieving accurate microwave leakage location and intensity assessment. This method ensures that the finally deployed leak detection points achieve a scientifically optimal balance between detection performance and spatial coverage, thus providing a high-quality data foundation for subsequent location and assessment processes, and comprehensively improving the performance ceiling and reliability of the entire microwave leakage detection method.

[0205] In another embodiment of the present invention, the object to be tested is a microwave oven, and the leak detection points are distributed in at least one of the following areas: the door seal, the perimeter of the outer casing, and the center of the bottom of the object to be tested.

[0206] Figure 6 This is a schematic diagram of the distribution of leak detection points in a microwave oven provided by the present invention, as shown below. Figure 6 As shown in the diagram, this figure illustrates a specific layout scheme with six leak detection points for a microwave oven under test. The microwave oven diagram indicates its A-side (front), B-side (top), C-side (right side), and D-side (bottom).

[0207] Specifically, the distribution of the six leak detection points P1-P6 is as follows:

[0208] (1) Leak detection point P1 is set in the center area of ​​the A side (front) of the microwave oven, which is directly opposite the oven door, and can effectively monitor the microwave leakage that may occur at the oven door and its door seal.

[0209] (2) Leak detection point P2 is set on the upper edge of the B side (top surface) of the microwave oven to cover the leak detection of the top shell.

[0210] (3) Leak detection points P3, P4 and P5 are distributed on the C side (right side) of the microwave oven. P3 is located at the front edge of the side, while P4 and P5 are located at the top and bottom edges of the rear side, respectively. These three leak detection points constitute a three-dimensional monitoring of the entire side of the microwave oven.

[0211] (4) Leak detection point P6 is set on the D side (bottom) of the microwave oven to monitor the leakage in the bottom area.

[0212] The microwave leakage detection method provided by this invention focuses the distribution of leak detection points on the known critical leakage areas of the microwave oven, a specific object to be tested. This makes the microwave leakage detection method of this invention more targeted and practical, and improves the detection effectiveness and reliability for microwave ovens, a common application scenario, while ensuring detection accuracy.

[0213] In another embodiment of the present invention, after determining the microwave leakage intensity of the object to be tested, the method further includes: determining whether microwave leakage exceeds the standard based on the comparison result between the microwave leakage intensity and the leakage intensity threshold; if so, controlling the alarm device to output an alarm reminder.

[0214] Optionally, this leakage intensity threshold is a key parameter used to determine whether a product is qualified. This value can be set according to national or industry-issued microwave leakage safety standards, or it can be a more stringent standard value set internally by the company based on its quality control requirements. This leakage intensity threshold can be pre-stored in the microwave leakage detection device.

[0215] Specifically, when a microwave leakage exceeding the standard is detected, the central processing unit of the microwave leakage detection device sends a control signal to one or more external alarm devices. These alarm devices can be audible and visual alarms, indicator lights on the production line, buzzers, or directly a production line control computer or a programmable logic controller (PLC).

[0216] The alarm notification can take various forms. For example, it could be a continuous audible sound or a flashing warning light to alert on-site personnel. Furthermore, the alarm notification could also include displaying the excessive microwave leakage intensity value and the coordinates of the microwave leakage source on a human-machine interface screen, or triggering the production line control system to perform corresponding operations, such as stopping the movement of the object under test on the conveyor belt for manual intervention or automatic sorting.

[0217] The microwave leakage detection method provided by this invention, by adding an alarm linkage step, can directly convert the detection results into real-time, automated quality control actions, realizing a closed-loop automated process from detection to judgment to response. This not only solves the problems of low efficiency and error-proneness of manual judgment in the prior art, but also enables timely detection and isolation of defective products, greatly improving the safety and quality control level of the production line.

[0218] Figure 7 This is a schematic diagram of the test process for the microwave leakage detection method provided by the present invention, as shown below. Figure 7 As shown, the process may include:

[0219] First, turn on the microwave oven to put it into operation. Then, the microwave leakage detection device begins collecting data on the location of all leak points and the microwave leakage detection value at each point to obtain raw microwave detection data.

[0220] After acquiring the microwave detection data, it can be transformed and preprocessed. For example, filtering, denoising, and calibration can be performed on the acquired signal to ensure the accuracy and reliability of the data used in subsequent calculations. Then, based on the preprocessed microwave detection data, the location of the microwave leakage source and the microwave leakage intensity can be determined sequentially.

[0221] Subsequently, enter the judgment phase to determine whether the calculated microwave leakage intensity exceeds a preset leakage intensity threshold. If the microwave leakage intensity exceeds the leakage intensity threshold, it is determined that there is an over-standard microwave leakage phenomenon, and the alarm device is controlled to output an alarm reminder, such as emitting an audible and visual alarm. If the microwave leakage intensity does not exceed the leakage intensity threshold, it is determined that the detection is qualified. Finally, whether the detection is qualified or an over-standard alarm occurs, the operation of turning off the microwave oven and stopping the detection will be executed to complete a complete detection cycle.

[0222] In another embodiment provided by the present invention, after determining the microwave leakage intensity of the待测 object, it further includes: displaying the microwave leakage source position and the microwave leakage intensity in the virtual model of the待测 object.

[0223] Specifically, the virtual model can be a three-dimensional virtual model, and its data can be derived from the three-dimensional geometric model data of the待测 object. The virtual model can be rendered and displayed on the human-computer interaction interface of a microwave leakage detection device, such as a touch screen or an external computer monitor.

[0224] Optionally, the implementation methods of displaying in the virtual model of the待测 object may include but are not limited to the following two:

[0225] (1) The microwave leakage source position is displayed through a highly visible visualization marker (such as a flashing red dot, a three-dimensional arrow, or a crosshair). The visualization marker will be accurately rendered at the coordinate point on the virtual model corresponding to the determined microwave leakage source position.

[0226] (2) The microwave leakage intensity is directly displayed in numerical form next to the visualization marker. Alternatively, the microwave leakage intensity can also be represented by the visual attributes of the visualization marker. For example, the color of the marker can change according to different intervals of the leakage intensity (e.g., green represents safety, yellow represents warning, and red represents serious over-standard), or the size of the marker can be proportional to the value of the leakage intensity.

[0227] The microwave leakage detection method provided by the present invention greatly improves the readability and usability of the detection results by visually displaying the abstract detection data on the virtual model. This method enables maintenance personnel to quickly and accurately understand the specific location and severity of the leakage problem without performing complex coordinate correspondence, significantly shortening the troubleshooting time, further solving the problem of time-consuming and laborious troubleshooting caused by inaccurate positioning in the prior art, and improving the efficiency of human-computer interaction.

[0228] Figure 8 is a schematic structural diagram of the microwave leakage detection device provided by the present invention, as Figure 8 shown, mainly including but not limited to:

[0229] The data receiving unit 810 is used to acquire microwave detection data, which includes the leak detection location of each leak detection point of the object under test and the microwave leakage detection amount detected at each leak detection point. There are multiple leak detection points.

[0230] Leakage source location unit 820 is used to determine the location of microwave leakage source based on microwave detection data.

[0231] The intensity determination unit 830 is used to determine the microwave leakage intensity of the object under test based on the microwave leakage detection quantity detected at each leak detection point and the leakage quantity weight of each leak detection point.

[0232] The leakage weight of each leak detection point is related to the distance between the leak detection point and the microwave leakage source.

[0233] It should be noted that the microwave leakage detection device provided by the present invention can execute the microwave leakage detection method of any of the above embodiments during specific operation, which will not be elaborated in this embodiment.

[0234] The microwave leakage detection device provided by this invention uses microwave leakage-related data obtained from multiple leakage detection points to objectively determine the specific location of the microwave leakage source. It generates leakage weights based on the distance relationship between the microwave leakage source location and each leakage detection point, and then determines the microwave leakage intensity of the object under test by weighted summation. This device can effectively achieve rapid and accurate location of microwave leakage sources, and improves the automation level, efficiency and accuracy of microwave leakage detection.

[0235] Figure 9 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 9 As shown, the electronic device may include a processor 910, a communications interface 920, a memory 930, and a communication bus 940. The processor 910, communications interface 920, and memory 930 communicate with each other via the communication bus 940. The processor 910 can call logical instructions in the memory 930 to execute a microwave leakage detection method. This method includes: acquiring microwave detection data, which includes the leak location of each leak detection point on the object under test and the microwave leakage detection amount detected at each leak detection point; determining the location of the microwave leakage source based on the microwave detection data; determining the microwave leakage intensity of the object under test based on the microwave leakage detection amount detected at each leak detection point and the leakage weight of each leak detection point; the leakage weight of each leak detection point is related to the distance between the leak detection point and the microwave leakage source location.

[0236] Furthermore, the logical instructions in the aforementioned memory 930 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0237] On the other hand, the present invention also provides a computer program product, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, when the program instructions are executed by a computer, the computer is able to execute the microwave leakage detection method provided in the above embodiments, the method including: acquiring microwave detection data, the microwave detection data including the leak detection location of each leak detection point of the object under test and the microwave leakage detection amount detected at each leak detection point, the leak detection points being multiple; determining the microwave leakage source location based on the microwave detection data; determining the microwave leakage intensity of the object under test based on the microwave leakage detection amount detected at each leak detection point and the leakage amount weight of each leak detection point; the leakage amount weight of each leak detection point is related to the distance between the leak detection point and the microwave leakage source location.

[0238] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the microwave leakage detection method provided in the above embodiments. The method includes: acquiring microwave detection data, the microwave detection data including the leak detection location of each leak detection point of the object under test and the microwave leakage detection amount detected at each leak detection point, wherein there are multiple leak detection points; determining the location of the microwave leakage source based on the microwave detection data; determining the microwave leakage intensity of the object under test based on the microwave leakage detection amount detected at each leak detection point and the leakage weight of each leak detection point; wherein the leakage weight of each leak detection point is related to the distance between the leak detection point and the location of the microwave leakage source.

[0239] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0240] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0241] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A microwave leakage detection method, characterized in that, include: Acquire microwave detection data, which includes the leak detection location of each leak detection point on the object under test and the microwave leakage detection amount detected at each leak detection point, wherein there are multiple leak detection points; Based on the microwave detection data, the location of the microwave leakage source is determined; The microwave leakage intensity of the object under test is determined based on the microwave leakage detection quantity detected at each of the leak detection points and the leakage quantity weight of each of the leak detection points. The leakage weight of each leak detection point is related to the distance between the leak detection point and the microwave leakage source location; The step of determining the microwave leakage intensity of the object under test based on the microwave leakage detection quantity detected at each of the leak detection points and the leakage quantity weight of each of the leak detection points includes: Determine the distance between the leak detection location and the microwave leakage source location at each leak detection point, as well as the propagation loss index; Based on the distance and the propagation loss index, determine the initial weight of the leakage amount at each leak detection point; The initial weights of the leakage amounts at all the leak detection points are normalized to obtain the leakage amount weight for each of the leak detection points. By using the leakage amount weight of each of the leak detection points, the microwave leakage detection amount detected at each of the leak detection points is weighted and summed to obtain the microwave leakage intensity of the object under test.

2. The microwave leakage detection method according to claim 1, characterized in that, Determining the location of the microwave leakage source based on the microwave detection data includes: Construct a correlation model between the microwave leakage detection quantity and the microwave detection distance for each leak detection point, wherein the microwave detection distance is the distance between the leak detection location of the leak detection point and the location of the microwave leakage source to be predicted; Using the aforementioned correlation model, the microwave detection distance is mapped to the predicted microwave leakage detection quantity; Construct an error function, which is the sum of squared errors between the predicted microwave leakage detection amount and the actual microwave leakage detection amount at all the leak detection points; Based on the gradient descent method, with the goal of minimizing the error function, the location of the microwave leakage source to be predicted is iteratively updated until the optimal location of the microwave leakage source is determined.

3. The microwave leakage detection method according to claim 2, characterized in that, The gradient descent method, with the objective of minimizing the error function, iteratively updates the location of the microwave leakage source to be predicted until the optimal location of the microwave leakage source is determined, including: For the k-th iteration, determine the initial location of the microwave leakage source to be predicted; Calculate the microwave detection distance between each leak detection point and the initial position; Based on the aforementioned correlation model, the predicted microwave leakage detection quantity corresponding to the microwave detection distance is calculated; The predicted microwave leakage detection value is input into the error function to determine the error loss; Determine the gradient of the error loss with respect to the location of the microwave leakage source; The initial position of the microwave leakage source location to be predicted is updated based on the gradient and the learning rate. Let k = k + 1, and repeat the iteration process until the error loss converges or the preset maximum number of iterations is reached, so as to determine the optimal microwave leakage source location.

4. The microwave leakage detection method according to claim 1, characterized in that, Determining the location of the microwave leakage source based on the microwave detection data includes: The microwave leakage detection value and leakage location of each of the leak detection points are input into the leakage source location prediction model to obtain the microwave leakage source location output by the leakage source location prediction model; The leak source location prediction model is obtained by training an initial network model using training data from multiple historical leak detection data samples. The historical leak detection data samples include leak detection location samples and microwave leak detection quantity samples for each leak detection point, as well as microwave leak source location labels.

5. The microwave leakage detection method according to claim 1, characterized in that, The leak detection point on the object to be tested is determined based on the following steps: Acquire the three-dimensional geometric model data of the object under test, wherein the three-dimensional geometric model data includes spatial information of the shell structure and internal key components; Based on the three-dimensional geometric model data, a candidate set of leak detection points is generated, and the candidate leak detection points in the candidate set of leak detection points cover the microwave leakage sensitive area and the microwave uniform distribution area of ​​the object to be tested. Based on the microwave propagation simulation method, the propagation path and electromagnetic field distribution of microwaves around the object under test are simulated, and the signal reception quality and coverage contribution of each candidate leak point are calculated. A comprehensive evaluation function is constructed, which evaluates the detection effectiveness of each candidate leak point based on the occlusion degree, signal-to-noise ratio, and sensitivity of each candidate leak point. Based on the comprehensive evaluation function and the spatial distribution uniformity of the leak detection points, an optimization objective function is constructed with the goal of maximizing the overall detection effectiveness and the distribution uniformity of the leak detection points. The maximization of the overall detection effectiveness is determined based on the detection effectiveness of each candidate leak detection point, and the distribution uniformity of the leak detection points is determined based on the distance between two adjacent candidate leak detection points. Based on a heuristic optimization algorithm, a subset of leak detection points that meet the constraints are selected from the candidate set of leak detection points. The constraints include an upper limit on the number of leak detection points and a positioning accuracy requirement. The subset of leak detection points includes all the leak detection points.

6. The microwave leakage detection method according to claim 5, characterized in that, The object to be tested is a microwave oven, and the leak detection points are distributed in at least one of the following areas: the door seal, the perimeter of the outer casing, and the center of the bottom of the object to be tested.

7. The microwave leakage detection method according to claim 1, characterized in that, After determining the microwave leakage intensity of the object under test, the process also includes: Based on the comparison results between the microwave leakage intensity and the leakage intensity threshold, it is determined whether microwave leakage exceeds the standard. If this occurs, the alarm device will output an alarm notification.

8. The microwave leakage detection method according to claim 1, characterized in that, After determining the microwave leakage intensity of the object under test, the process also includes: The location of the microwave leakage source and the intensity of the microwave leakage are displayed in the virtual model of the object under test.

9. A microwave leakage detection device, characterized in that, include: A data receiving unit is used to acquire microwave detection data, which includes the leak detection location of each leak detection point of the object under test and the microwave leakage detection amount detected at each leak detection point, wherein there are multiple leak detection points. A leakage source location unit is used to determine the location of the microwave leakage source based on the microwave detection data. An intensity determination unit is used to determine the microwave leakage intensity of the object under test based on the microwave leakage detection quantity detected at each of the leak detection points and the leakage quantity weight of each of the leak detection points. The leakage weight of each leak detection point is related to the distance between the leak detection point and the microwave leakage source location; The step of determining the microwave leakage intensity of the object under test based on the microwave leakage detection quantity detected at each of the leak detection points and the leakage quantity weight of each of the leak detection points includes: Determine the distance between the leak detection location and the microwave leakage source location at each leak detection point, as well as the propagation loss index; Based on the distance and the propagation loss index, determine the initial weight of the leakage amount at each leak detection point; The initial weights of the leakage amounts at all the leak detection points are normalized to obtain the leakage amount weight for each of the leak detection points. By using the leakage amount weight of each of the leak detection points, the microwave leakage detection amount detected at each of the leak detection points is weighted and summed to obtain the microwave leakage intensity of the object under test.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the microwave leakage detection method as described in any one of claims 1 to 8.

11. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the microwave leakage detection method as described in any one of claims 1 to 8.

12. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the microwave leakage detection method as described in any one of claims 1 to 8.

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