A high-precision detection method and device based on photoelectric sensor

Abstract

The present invention discloses a high-precision detection method and device based on a photoelectric sensor, which relates to the field of signal detection technology. The method includes: collecting basic information of a target object, a target detection response window, and a target detection accuracy to configure photoelectric sensor parameters, and obtain target configuration parameters; obtaining a target photoelectric sensor with parameter adjustment completed; emitting light of a preset wavelength to a target carrier through a blue light source transmitter, and the light passes through the target photoelectric sensor to form a strip light spot on the target carrier; when the target object passes through the strip light spot at a preset moving speed, a reflected light signal is obtained, and the reflected light signal is sent to the target photoelectric sensor to generate an electrical signal to be processed; a control unit is used to process the electrical signal to be processed to obtain a detection result. The present invention solves the technical problems of insufficient accuracy and susceptibility to noise interference in the prior art when detecting objects, and achieves the technical effect of improving object detection accuracy and response efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of signal detection, and in particular to a high-precision detection method and device based on a photoelectric sensor. Background Art

[0002] In modern industry and scientific research, photoelectric sensors are widely used in various high-precision detection tasks. However, existing photoelectric sensor technology faces many challenges in practical applications. First, the detection accuracy is often limited by the changes in the surface characteristics of the object, resulting in inaccurate detection results. Second, the signal acquisition and processing process is easily affected by noise interference, which affects the signal quality. Summary of the invention

[0003] The present application provides a high-precision detection method and device based on a photoelectric sensor, which is used to solve the technical problems of insufficient accuracy and susceptibility to noise interference in the prior art when detecting objects.

[0004] In view of the above problems, the present application provides a high-precision detection method and device based on a photoelectric sensor.

[0005] In a first aspect of the present application, a high-precision detection method based on a photoelectric sensor is provided, the method comprising:

[0006] Collect basic information of the target object, target detection response window and target detection accuracy to configure photoelectric sensor parameters and obtain target configuration parameters;

[0007] Adjust the photoelectric sensor according to the target configuration parameters to obtain a target photoelectric sensor in which the adjustment is completed;

[0008] The blue light source emitter emits light of a preset wavelength to the target stage, and the light passes through the target photoelectric sensor to form a strip light spot on the target stage, wherein the strip light spot has a first side length and a second side length, and the first side length is less than or equal to the second side length;

[0009] When the target object passes through the strip-shaped light spot at a preset moving speed, a reflected light signal is obtained, and the reflected light signal is sent to the target photoelectric sensor to generate an electrical signal to be processed;

[0010] The control unit is used to process the electrical signal to be processed to obtain a detection result.

[0011] The second aspect of the present application provides a high-precision detection device based on a photoelectric sensor, the device comprising:

[0012] A target configuration parameter acquisition module is used to collect basic information of the target object, the target detection response window and the target detection accuracy to configure the photoelectric sensor parameters and obtain the target configuration parameters;

[0013] A target photoelectric sensor acquisition module is used to adjust the photoelectric sensor according to the target configuration parameters to obtain the target photoelectric sensor after the adjustment;

[0014] A strip light spot forming module, used for emitting light of a preset wavelength to a target stage through a blue light source emitter, and the light passes through the target photoelectric sensor to form a strip light spot on the target stage, wherein the strip light spot has a first side length and a second side length, and the first side length is less than or equal to the second side length;

[0015] A module for generating an electrical signal to be processed, used for obtaining a reflected light signal when the target object passes through the strip-shaped light spot at a preset moving speed, sending the reflected light signal to the target photoelectric sensor, and generating an electrical signal to be processed;

[0016] The detection result acquisition module is used to use the control unit to process the electrical signal to be processed to obtain the detection result.

[0017] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0018] This application configures the photoelectric sensor parameters by collecting the basic information of the target object, the target detection response window and the target detection accuracy, obtains the target configuration parameters, and then adjusts the photoelectric sensor according to the target configuration parameters to obtain the target photoelectric sensor with the parameters adjusted, and then emits light of a preset wavelength to the target carrier through a blue light source transmitter, and the light passes through the target photoelectric sensor to form a strip light spot on the target carrier, wherein the strip light spot has a first side length and a second side length, and the first side length is less than or equal to the second side length, and when the target object passes through the strip light spot at a preset moving speed, a reflected light signal is obtained, and the reflected light signal is sent to the target photoelectric sensor to generate an electrical signal to be processed, and then the control unit is used to process the electrical signal to be processed to obtain the detection result. The technical effect of high-precision and high-reliability object detection is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1A schematic diagram of a high-precision detection method based on a photoelectric sensor provided in an embodiment of the present application;

[0021] Figure 2 A schematic diagram of the structure of a high-precision detection device based on a photoelectric sensor provided in an embodiment of the present application.

[0022] Description of reference numerals: target configuration parameter acquisition module 11 , target photoelectric sensor acquisition module 12 , strip light spot forming module 13 , to-be-processed electrical signal generating module 14 , detection result acquisition module 15 . DETAILED DESCRIPTION

[0023] The present application provides a high-precision detection method and device based on a photoelectric sensor, which is used to solve the technical problems of insufficient accuracy and susceptibility to noise interference in the prior art when detecting objects.

[0024] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0025] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0026] Embodiment 1

[0027] like Figure 1 As shown, the present application provides a high-precision detection method based on a photoelectric sensor, wherein the method comprises:

[0028] S100: collecting basic information of the target object, the target detection response window, and the target detection accuracy to configure photoelectric sensor parameters and obtain target configuration parameters;

[0029] Furthermore, the basic information of the object includes object material, color type, surface characteristics, size, and object shape.

[0030] Furthermore, the target configuration parameters include spot size, detection distance, output type, power supply voltage and installation method.

[0031] In a possible embodiment, in order to improve the detection accuracy, it is necessary to set the parameters of the photoelectric sensor according to the actual detection object and the detection requirements. The target object is any object that needs to be detected, which can be a chip, a hollow object, etc. The target detection response window is the response time of the photoelectric sensor to the detection object. The target detection accuracy is the minimum deviation when the photoelectric sensor detects the object.

[0032] Optionally, the basic information of the object includes the material, color type, surface characteristics, size, and shape of the object. The target configuration parameters include the spot size, detection distance, output type, power supply voltage, and installation method. The installation method is the fixed angle and fixed bracket type of the photoelectric sensor.

[0033] By obtaining the basic information of the object, the target detection response window and the target detection accuracy, reliable basic analysis data is provided for configuring a photoelectric sensor that meets actual needs.

[0034] Further, basic object information, target detection response window, and target detection accuracy of the target object are collected to configure photoelectric sensor parameters and obtain target configuration parameters. Step S100 of the embodiment of the present application further includes:

[0035] Constructing a parameter configuration space, wherein the parameter configuration space is constructed based on a three-dimensional coordinate system, the x-axis is basic information of the object, the y-axis is the detection response window, and the z-axis is the detection accuracy;

[0036] Collecting multiple sample object basic information, multiple target detection response windows, and multiple target detection precisions and inputting them into the parameter configuration space to obtain multiple sample points, and using multiple sample configuration parameters to identify the multiple sample points;

[0037] Inputting the basic object information, target detection response window and target detection accuracy of the target object into the parameter configuration space to obtain target coordinate points;

[0038] The target configuration parameters are obtained by optimizing the multiple sample points based on the target coordinate point.

[0039] In a possible embodiment, the parameter configuration space is a simulation device for optimizing parameters of a photoelectric sensor, and is constructed based on a three-dimensional coordinate system having three coordinate axes, the x-axis being basic information of the object, the y-axis being the detection response window, and the z-axis being the detection accuracy.

[0040] The multiple sample object basic information, multiple target detection response windows, and multiple target detection accuracies are respectively input into the parameter configuration space according to corresponding coordinates to obtain the multiple sample points. The multiple sample points are identified using multiple sample configuration parameters corresponding to the multiple sample object basic information, multiple target detection response windows, and multiple target detection accuracies.

[0041] Then, the basic object information, target detection response window and target detection accuracy of the target object are input into the parameter configuration space to obtain the corresponding target coordinate point. According to the distribution position of the target coordinate point in the parameter configuration space, the multiple sample points are optimized to obtain the target configuration parameters. The target configuration parameters are used to configure the photoelectric sensor to meet the current detection requirements.

[0042] Furthermore, step S100 in the embodiment of the present application further includes:

[0043] Matching a plurality of matching sample points within a preset step length from the target coordinate point in the parameter configuration space;

[0044] Perform transition edge recognition based on the multiple matching sample points to obtain a first transition step length;

[0045] A plurality of target sample points whose distance from the target coordinate point is the first transition step length are counted, and a plurality of target sample configuration parameters corresponding to the plurality of target sample points are averaged to obtain the target configuration parameters.

[0046] Further, based on the multiple matching sample points, transition edge recognition is performed to obtain a first transition step length. Step S100 in the embodiment of the present application further includes:

[0047] Taking the target coordinate point as the center, determining a first transition region according to a preset edge step, wherein the first transition region has a first clustering density, wherein the preset edge step is smaller than the preset step;

[0048] Expanding the edge of the first transition region according to the preset edge step length to obtain a second transition region, wherein the second transition region has a second clustering density;

[0049] Determine whether the difference between the second clustering density and the first clustering density satisfies a preset transition difference, and if not, expand the edge of the second transition region again according to the preset edge step length to obtain a third transition region, wherein the third transition region has a third clustering density;

[0050] When the difference between the third clustering density and the second clustering density satisfies a preset transition difference, the distance from the target coordinate point to the edge of the second transition region is used as the first transition step length.

[0051] In a possible embodiment, multiple matching sample points within a preset step length from the target coordinate point are matched in the parameter configuration space. The preset step length is a distance preset by a person skilled in the art. Transition edge recognition is performed on the multiple matching sample points to determine a densely distributed edge, and the length from the edge to the target coordinate point is used as the first transition step length.

[0052] Furthermore, a plurality of target sample points whose distance from the target coordinate point is the first transition step length are counted, and a plurality of target sample configuration parameters corresponding to the plurality of target sample points are averaged to obtain the target configuration parameters.

[0053] Optionally, an area constructed with the target coordinate point as the center and a preset edge step as the radius is used as the first transition area, wherein the first transition area has a first clustering density, wherein the preset edge step is smaller than the preset step. Exemplarily, the preset edge step is one sixth of the preset step. The first clustering density reflects the distribution density of the matching sample points distributed in the first transition area.

[0054] The first clustering density is obtained by counting the number of matching sample points in the first transition region and dividing the statistical result by the area of ​​the first transition region.

[0055] Then, the edge of the first transition region is expanded according to the preset edge step, and the region obtained after the expansion is used as the second transition region, wherein the second transition region has a second clustering density. The second clustering density is determined based on the same acquisition principle as the first clustering density. The second clustering density reflects the distribution density of the matching sample points distributed in the second transition region.

[0056] It is determined whether the difference between the second clustering density and the first clustering density satisfies the preset transition difference. If not, it indicates that the distribution density of matching sample points in the first transition region and the second transition region is not much different. At this time, it is necessary to expand the edge of the second transition region again according to the preset edge step length to obtain a third transition region, wherein the third transition region has a third clustering density. The third clustering density reflects the distribution density of matching sample points distributed in the third transition region.

[0057] When the difference between the third clustering density and the second clustering density meets the preset transition difference, it indicates that the edge of the area with more concentrated distribution has been reached. At this time, the distance from the target coordinate point to the edge of the second transition area is used as the first transition step length.

[0058] S200: Adjust the photoelectric sensor according to the target configuration parameters to obtain a target photoelectric sensor after the adjustment;

[0059] In one possible embodiment, according to the installation mode in the target configuration parameters, the photoelectric sensor is installed at a predetermined position using an appropriate fixing bracket to ensure that its relative position with the target object is stable and meets the detection requirements. The photoelectric sensor is connected to a power supply voltage that meets the target configuration parameters, such as 24VDC, to ensure stable power supply. The output interface of the sensor is connected to the signal processing unit, and a suitable signal transmission mode is selected, such as an analog signal or a digital signal.

[0060] The emitting light source and lens system of the photoelectric sensor are adjusted according to the light spot size in the target configuration parameters to obtain the corresponding strip light spot. Then, according to the installation environment and the object size, the distance between the photoelectric sensor and the target object is adjusted to ensure that the detection distance is within the range of the configuration parameters, such as 100mm, so as to obtain the target photoelectric sensor with the adjustment completed.

[0061] S300: emitting light of a preset wavelength to the target stage through a blue light source emitter, and the light passes through the target photoelectric sensor to form a strip light spot on the target stage, wherein the strip light spot has a first side length and a second side length, and the first side length is less than or equal to the second side length;

[0062] S400: When the target object passes through the strip light spot at a preset moving speed, a reflected light signal is obtained, and the reflected light signal is sent to the target photoelectric sensor to generate an electrical signal to be processed;

[0063] In one possible embodiment, a blue light source emitter emits blue light of a preset wavelength to the target stage, and a strip light spot is formed on the target stage through a photoelectric sensor. The strip light spot has two side lengths, wherein the first side length (short side) is less than or equal to the second side length (long side), forming a long strip light spot structure. When the target object passes through the strip light spot at a preset moving speed, the reflected light signal is captured by the photoelectric sensor and converted into an electrical signal. The electrical signal is processed by a control unit, and finally a high-precision detection result is obtained.

[0064] Specifically, a blue light source emitter is a device for generating light of a specific wavelength. A blue light source is usually selected because of its shorter wavelength, which can provide higher resolution and accuracy. The wavelength of blue light is usually between 450 nanometers and 495 nanometers. The target carrier is a platform on which the object to be detected is placed. It is usually necessary to ensure the stability and flatness of the platform to avoid affecting the detection results of the photoelectric sensor. The strip light spot has two side lengths, a first side length and a second side length. The first side length is less than or equal to the second side length, which means that the shape of the light spot is a long strip with a narrow width and a long length. The target photoelectric sensor is responsible for capturing the reflected light signal of the strip light spot on the target carrier and converting it into an electrical signal to be processed. The electrical signal will be further processed to obtain the detection result.

[0065] In a possible embodiment, the preset moving speed is a distance that a target object moves in a unit time when it moves on the target stage and passes through the strip-shaped light spot, which is preset by a person skilled in the art.

[0066] Exemplarily, the size of the strip light spot may be 5mm*45mm, 7mm*60mm, 7mm*40mm, or 7mm*60mm.

[0067] S500: Utilize a control unit to process the electrical signal to be processed to obtain a detection result.

[0068] In an embodiment of the present application, the captured electrical signal is processed by a control unit embedded in a control chip inside the sensor. First, an amplifier is used to enhance the signal strength, and then a bandpass filter is used to remove noise to obtain a denoised electrical signal. The control unit calls multiple signal processing models, processes and optimizes these models, and selects the optimal model to construct a target signal processing model.

[0069] In this process, by traversing multiple signal processing models, recording their respective processing time and accuracy, performing fitness identification, and finally selecting the top k models in fitness, calculating their parameter means, and constructing the final target signal processing model.

[0070] The optimal signal processing model is used to identify and analyze the de-noised electrical signal to obtain high-precision detection results. This series of steps ensures that the detection system can still provide accurate and stable detection results in complex environments and meet the requirements of high-precision detection.

[0071] Furthermore, the control unit is used to process the electrical signal to be processed to obtain a detection result. Step S500 of the embodiment of the present application further includes:

[0072] Amplifying the electrical signal to be processed by using an amplifier in the control unit to obtain an amplified electrical signal;

[0073] Using a bandpass filter in the control unit to remove noise from the amplified electrical signal to obtain a denoised electrical signal;

[0074] Calling multiple signal processing models in the control unit to perform screening and optimization to obtain a target signal processing model;

[0075] The signal processing model is used to identify the denoised electrical signal to obtain the detection result.

[0076] In a possible embodiment, first ensure that the target photoelectric sensor, control unit, amplifier and other related equipment are working properly. The output end of the target photoelectric sensor is correctly connected to the input end of the control unit, and an operational amplifier or a low-noise amplifier is used to amplify the electrical signal to be processed according to a preset gain parameter to obtain an amplified electrical signal.

[0077] According to the frequency characteristics of the signal to be processed, the low-frequency and high-frequency cutoff frequencies of the bandpass filter are set to ensure that the filter can effectively remove unnecessary noise frequency bands and retain effective signals. The amplified electrical signal is input into the bandpass filter, and then the bandpass filter processes the amplified electrical signal in real time, retains the effective signal frequency band, and obtains the denoised electrical signal.

[0078] Optionally, multiple signal processing models in the control unit are called to screen and optimize to obtain a target signal processing model. The multiple signal processing models are functional models stored in the control unit for response analysis of electrical signals. The target signal processing model is a functional model for response analysis of the denoised electrical signal. The denoised electrical signal is input into the signal processing model to identify the denoised electrical signal and obtain the detection result.

[0079] Furthermore, step S500 of the embodiment of the present application further includes:

[0080] Traversing the plurality of signal processing models to perform processing record identification, and obtaining a plurality of processing time efficiencies and a plurality of processing accuracies;

[0081] Performing fitness identification on the multiple processing time effects and the multiple processing accuracies to obtain multiple model processing fitnesses;

[0082] Selecting the signal processing models that are in the top k positions among the multiple model processing fitnesses as K matching signal processing models, where k is an integer greater than or equal to 3;

[0083] The network parameters of the K matching signal processing models are averaged and the target signal processing model is constructed according to the calculation results.

[0084] In a possible embodiment, the multiple signal processing models are traversed to perform processing record identification, and the processing data of the multiple signal processing models in the historical time are extracted to obtain multiple processing time efficiencies and multiple processing accuracy. The multiple processing time efficiencies are the response time of the multiple signal processing models to the electrical signal. The multiple processing accuracy is the response accuracy of the multiple signal processing models to the electrical signal.

[0085] The fitness recognition network layer is used to perform fitness recognition on the multiple processing time efficiencies and the multiple processing accuracies to obtain multiple model processing fitnesses, wherein the higher the model processing fitness, the better the corresponding signal processing model.

[0086] Optionally, multiple sample processing times, multiple sample processing accuracies, and multiple sample model processing fitnesses are obtained as training data, and the training data is used to perform supervised training on a framework built based on a convolutional neural network. During the training, a two-to-one mapping relationship between processing time and processing accuracy, and model processing fitness is learned until the model converges, and a trained fitness recognition network layer is obtained.

[0087] Optionally, the signal processing models in the top k positions among the multiple model processing fitness are selected as K matching signal processing models, the network parameters of the K matching signal processing models are averaged, and the target signal processing model is constructed according to the calculation results. Wherein, k is an integer greater than or equal to 3. The technical effect of determining the model for processing the electrical signal and improving the detection accuracy and efficiency is achieved.

[0088] In summary, the embodiments of the present application have at least the following technical effects:

[0089] This application configures the photoelectric sensor parameters by collecting the basic information of the target object, the target detection response window and the target detection accuracy, obtains the target configuration parameters, and then adjusts the photoelectric sensor according to the target configuration parameters to obtain the target photoelectric sensor with the parameters adjusted, and then emits light of a preset wavelength to the target carrier through a blue light source transmitter, and the light passes through the target photoelectric sensor to form a strip light spot on the target carrier, wherein the strip light spot has a first side length and a second side length, and the first side length is less than or equal to the second side length, and when the target object passes through the strip light spot at a preset moving speed, a reflected light signal is obtained, and the reflected light signal is sent to the target photoelectric sensor to generate an electrical signal to be processed, and then the control unit is used to process the electrical signal to be processed to obtain the detection result. The technical effect of high-precision and high-reliability object detection is achieved.

[0090] Embodiment 2

[0091] Based on the same inventive concept as the high-precision detection method based on a photoelectric sensor in the above-mentioned embodiment, Figure 2 As shown, the present application provides a high-precision detection device based on a photoelectric sensor, and the device and method embodiments in the present application are based on the same inventive concept. The device includes:

[0092] The target configuration parameter acquisition module 11 is used to collect basic information of the target object, the target detection response window and the target detection accuracy to configure the photoelectric sensor parameters and obtain the target configuration parameters;

[0093] A target photoelectric sensor acquisition module 12 is used to adjust the photoelectric sensor according to the target configuration parameters to obtain a target photoelectric sensor after the adjustment;

[0094] The strip light spot forming module 13 is used to emit light of a preset wavelength to the target stage through a blue light source emitter, and the light passes through the target photoelectric sensor to form a strip light spot on the target stage, wherein the strip light spot has a first side length and a second side length, and the first side length is less than or equal to the second side length;

[0095] The to-be-processed electrical signal generating module 14 is used to obtain a reflected light signal when the target object passes through the strip-shaped light spot at a preset moving speed, and send the reflected light signal to the target photoelectric sensor to generate an to-be-processed electrical signal;

[0096] The detection result obtaining module 15 is used to use the control unit to process the electrical signal to be processed to obtain the detection result.

[0097] Furthermore, the basic information of the object includes object material, color type, surface characteristics, size, and object shape.

[0098] Furthermore, the target configuration parameters include spot size, detection distance, output type, power supply voltage and installation method.

[0099] Furthermore, the detection result obtaining module 15 is used to perform the following steps:

[0100] Amplifying the electrical signal to be processed by using an amplifier in the control unit to obtain an amplified electrical signal;

[0101] Using a bandpass filter in the control unit to remove noise from the amplified electrical signal to obtain a denoised electrical signal;

[0102] Calling multiple signal processing models in the control unit to perform screening and optimization to obtain a target signal processing model;

[0103] The signal processing model is used to identify the denoised electrical signal to obtain the detection result.

[0104] Furthermore, the detection result obtaining module 15 is used to perform the following steps:

[0105] Traversing the plurality of signal processing models to perform processing record identification, and obtaining a plurality of processing time efficiencies and a plurality of processing accuracies;

[0106] Performing fitness identification on the multiple processing time effects and the multiple processing accuracies to obtain multiple model processing fitnesses;

[0107] Selecting the signal processing models that are in the top k positions among the multiple model processing fitnesses as K matching signal processing models, where k is an integer greater than or equal to 3;

[0108] The network parameters of the K matching signal processing models are averaged and the target signal processing model is constructed according to the calculation results.

[0109] Furthermore, the target configuration parameter acquisition module 11 is used to perform the following steps:

[0110] Constructing a parameter configuration space, wherein the parameter configuration space is constructed based on a three-dimensional coordinate system, the x-axis is basic information of the object, the y-axis is the detection response window, and the z-axis is the detection accuracy;

[0111] Collecting multiple sample object basic information, multiple target detection response windows, and multiple target detection precisions and inputting them into the parameter configuration space to obtain multiple sample points, and using multiple sample configuration parameters to identify the multiple sample points;

[0112] Inputting the basic object information, target detection response window and target detection accuracy of the target object into the parameter configuration space to obtain target coordinate points;

[0113] The target configuration parameters are obtained by optimizing the multiple sample points based on the target coordinate point.

[0114] Furthermore, the target configuration parameter acquisition module 11 is used to perform the following steps:

[0115] Matching a plurality of matching sample points within a preset step length from the target coordinate point in the parameter configuration space;

[0116] Perform transition edge recognition based on the multiple matching sample points to obtain a first transition step length;

[0117] A plurality of target sample points whose distance from the target coordinate point is the first transition step length are counted, and a plurality of target sample configuration parameters corresponding to the plurality of target sample points are averaged to obtain the target configuration parameters.

[0118] Furthermore, the target configuration parameter acquisition module 11 is used to perform the following steps:

[0119] Taking the target coordinate point as the center, determining a first transition region according to a preset edge step, wherein the first transition region has a first clustering density, wherein the preset edge step is smaller than the preset step;

[0120] Expanding the edge of the first transition region according to the preset edge step length to obtain a second transition region, wherein the second transition region has a second clustering density;

[0121] Determine whether the difference between the second clustering density and the first clustering density satisfies a preset transition difference, and if not, expand the edge of the second transition region again according to the preset edge step length to obtain a third transition region, wherein the third transition region has a third clustering density;

[0122] When the difference between the third clustering density and the second clustering density satisfies a preset transition difference, the distance from the target coordinate point to the edge of the second transition region is used as the first transition step length.

[0123] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0124] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0125] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A high-precision detection method based on a photoelectric sensor, characterized in that: The method comprises: Collect basic information of the target object, target detection response window and target detection accuracy to configure photoelectric sensor parameters and obtain target configuration parameters; Adjust the photoelectric sensor according to the target configuration parameters to obtain a target photoelectric sensor in which the adjustment is completed; The blue light source emitter emits light of a preset wavelength to the target stage, and the light passes through the target photoelectric sensor to form a strip light spot on the target stage, wherein the strip light spot has a first side length and a second side length, and the first side length is less than or equal to the second side length; When the target object passes through the strip-shaped light spot at a preset moving speed, a reflected light signal is obtained, and the reflected light signal is sent to the target photoelectric sensor to generate an electrical signal to be processed; Processing the electrical signal to be processed by using a control unit to obtain a detection result; Collect the basic information of the target object, the target detection response window and the target detection accuracy to configure the photoelectric sensor parameters and obtain the target configuration parameters, including: Constructing a parameter configuration space, wherein the parameter configuration space is constructed based on a three-dimensional coordinate system, the x-axis is basic information of the object, the y-axis is the detection response window, and the z-axis is the detection accuracy; Collecting multiple sample object basic information, multiple target detection response windows, and multiple target detection precisions and inputting them into the parameter configuration space to obtain multiple sample points, and using multiple sample configuration parameters to identify the multiple sample points; Inputting the basic object information, target detection response window and target detection accuracy of the target object into the parameter configuration space to obtain target coordinate points; The target configuration parameters are obtained by optimizing the multiple sample points based on the target coordinate point.

2. A high-precision detection method based on a photoelectric sensor as claimed in claim 1, characterized in that: The basic information of the object includes the object material, color type, surface characteristics, size, and shape.

3. A high-precision detection method based on a photoelectric sensor as claimed in claim 1, characterized in that: The target configuration parameters include spot size, detection distance, output type, power supply voltage and installation method.

4. A high-precision detection method based on a photoelectric sensor as claimed in claim 1, characterized in that: Processing the electrical signal to be processed by a control unit to obtain a detection result includes: Amplifying the electrical signal to be processed by using an amplifier in the control unit to obtain an amplified electrical signal; Using a bandpass filter in the control unit to remove noise from the amplified electrical signal to obtain a denoised electrical signal; Calling multiple signal processing models in the control unit to perform screening and optimization to obtain a target signal processing model; The signal processing model is used to identify the denoised electrical signal to obtain the detection result.

5. A high-precision detection method based on a photoelectric sensor as claimed in claim 4, characterized in that: include: Traversing the plurality of signal processing models to perform processing record identification, and obtaining a plurality of processing time efficiencies and a plurality of processing accuracies; Performing fitness identification on the multiple processing time effects and the multiple processing accuracies to obtain multiple model processing fitnesses; Selecting the signal processing models that are in the top k positions among the multiple model processing fitnesses as K matching signal processing models, where k is an integer greater than or equal to 3; The network parameters of the K matching signal processing models are averaged and the target signal processing model is constructed according to the calculation results.

6. A high-precision detection method based on a photoelectric sensor as claimed in claim 1, characterized in that: include: Matching a plurality of matching sample points within a preset step length from the target coordinate point in the parameter configuration space; Perform transition edge recognition based on the multiple matching sample points to obtain a first transition step length; A plurality of target sample points whose distance from the target coordinate point is the first transition step length are counted, and a plurality of target sample configuration parameters corresponding to the plurality of target sample points are averaged to obtain the target configuration parameters.

7. A high-precision detection method based on a photoelectric sensor as claimed in claim 6, characterized in that: Performing transition edge identification based on the multiple matching sample points to obtain a first transition step length includes: Taking the target coordinate point as the center, determining a first transition region according to a preset edge step, wherein the first transition region has a first clustering density, wherein the preset edge step is smaller than the preset step; Expanding the edge of the first transition region according to the preset edge step length to obtain a second transition region, wherein the second transition region has a second clustering density; Determine whether the difference between the second clustering density and the first clustering density satisfies a preset transition difference, and if not, expand the edge of the second transition region again according to the preset edge step length to obtain a third transition region, wherein the third transition region has a third clustering density; When the difference between the third clustering density and the second clustering density satisfies a preset transition difference, the distance from the target coordinate point to the edge of the second transition region is used as the first transition step length.

8. A high-precision detection device based on a photoelectric sensor, characterized in that: The device is used to implement a high-precision detection method based on a photoelectric sensor according to any one of claims 1 to 7, and the device comprises: A target configuration parameter acquisition module is used to collect basic information of the target object, the target detection response window and the target detection accuracy to configure the photoelectric sensor parameters and obtain the target configuration parameters; A target photoelectric sensor acquisition module is used to adjust the photoelectric sensor according to the target configuration parameters to obtain the target photoelectric sensor after the adjustment; A strip light spot forming module, used for emitting light of a preset wavelength to a target stage through a blue light source emitter, and the light passes through the target photoelectric sensor to form a strip light spot on the target stage, wherein the strip light spot has a first side length and a second side length, and the first side length is less than or equal to the second side length; A module for generating an electrical signal to be processed, used for obtaining a reflected light signal when the target object passes through the strip-shaped light spot at a preset moving speed, sending the reflected light signal to the target photoelectric sensor, and generating an electrical signal to be processed; The detection result acquisition module is used to use the control unit to process the electrical signal to be processed to obtain the detection result.

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