Inductive sensor testing system and method

Abstract

The embodiment of the invention provides an inductive sensor testing system and method. The inductive sensor testing system comprises a shading box, a communication assembly and a processing assembly. An adjustable light source, at least one group of standard sensors and a slide rail device are arranged in the shading box, and the adjustable light source, the at least one group of standard sensors and the at least one group of to-be-tested sensors move along the slide rail device; the processing assembly is configured to respond to starting of the adjustable light source according to a target parameter, and obtain first readings of the at least one group of standard sensors and second readings of the at least one group of to-be-detected sensors; the target parameter comprises the illumination intensity of the adjustable light source; determining the reading accuracy of the at least one group of sensors to be detected based on the first reading and the second reading; in response to the fact that at least one reading accuracy meets an early warning condition, prompt information is sent out; wherein the difference between the number of the standard sensors and the number of the to-be-detected sensors is larger than a preset value, and the preset value is determined based on the illumination intensity.

Description

Technical Field

[0001] The present invention relates to the field of sensor testing, and in particular to a system and method for testing an inductive sensor. Background Art

[0002] As an indispensable key component in various industries and daily life, the accuracy and stability of inductive sensors play a decisive role in the application of many fields. In recent years, with the rapid development of intelligent and automated technologies, the application scope of inductive sensors has become increasingly wide, and the requirements for their performance have become increasingly stringent. Therefore, it is particularly important to conduct efficient and accurate testing of inductive sensors.

[0003] One of the key points in the testing process of inductive sensors is to compare the difference between the reading of the target sensor to be tested and the standard reading. The acquisition of readings is often carried out separately. In this process, due to the instability of the light source, the light it emits may be different, resulting in test errors. Multiple tests to obtain readings separately may lead to low test efficiency and reduced accuracy.

[0004] Therefore, it is hoped to provide an inductive sensor testing system and method that can improve testing efficiency and ensure testing accuracy. Summary of the invention

[0005] One of the embodiments of the present specification provides an inductive sensor testing system, including a light shielding box, a communication component and a processing component; an adjustable light source, at least one group of standard sensors and a slide rail device are arranged in the light shielding box, and at least one group of sensors to be tested is placed in the light shielding box for testing; the adjustable light source emits non-parallel light into the light shielding box; the adjustable light source, the at least one group of standard sensors and the at least one group of sensors to be tested move along the slide rail device; the processing component is respectively connected to the adjustable light source, the at least one group of standard sensors and the at least one group of sensors to be tested through the communication component; the processing component is configured to: in response to the adjustable light source being started according to a target parameter, obtain a first reading of the at least one group of standard sensors and a second reading of the at least one group of sensors to be tested; the target parameter includes the illumination intensity of the adjustable light source; based on the first reading and the second reading, determine the reading accuracy of the at least one group of sensors to be tested; in response to at least one of the reading accuracy meeting the warning condition, issue a prompt message; wherein the difference between the number of the standard sensors and the number of the sensors to be tested is greater than a preset value, and the preset value is determined based on the illumination intensity.

[0006] One of the embodiments of the present specification provides a sensing sensor testing method, which is executed by a processing component of the sensing sensor, including: in response to an adjustable light source being started according to a target parameter, obtaining a first reading of at least one group of standard sensors and a second reading of at least one group of sensors to be tested; the target parameter includes the light intensity of the adjustable light source; based on the first reading and the second reading, determining the reading accuracy of the at least one group of sensors to be tested; in response to at least one of the reading accuracies meeting a warning condition, issuing a prompt message; wherein the difference in the number of the standard sensors and the number of the sensors to be tested is greater than a preset value, and the preset value is determined based on the light intensity. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein: Figure 1 is an exemplary module diagram of an inductive sensor testing system according to some embodiments of this specification; Figure 2 is an exemplary flow chart of an inductive sensor testing method according to some embodiments of this specification; Figure 3 is a schematic diagram of determining setting data of a standard sensor according to some embodiments of this specification; Figure 4 It is a schematic diagram of determining the reading accuracy of a sensor to be tested according to some embodiments of this specification. DETAILED DESCRIPTION

[0008] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0009] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0010] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0011] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0012] Figure 1 is an exemplary module diagram of an inductive sensor testing system according to some embodiments of this specification. Figure 1 As shown, the inductive sensor testing system 100 may include a light shielding box 110 , a communication component 120 , and a processing component 130 .

[0013] The light shielding box 110 refers to a device for providing a stable environment for the experiment. The light shielding box 110 may be closed, and an adjustable light source 111, at least one standard sensor 112, and at least one sensor to be tested 114 may be sequentially installed inside the light shielding box 110. At least one sensor to be tested 114 is placed in the light shielding box 110 for testing.

[0014] The adjustable light source 111 refers to a component for emitting test light. In some embodiments, the adjustable light source 111 emits non-parallel light into the light shielding box 110. In some embodiments, the adjustable light source 111 emits light based on a target parameter.

[0015] For more information about the parameters of the light source to be measured, see Figure 3 and its related description.

[0016] The standard sensor 112 is a sensor of the same type as the sensor to be tested 114 and having a standard reading. In some embodiments, the standard sensor 112 is used to provide reference data, and the accuracy of the test data (e.g., the second reading) of the sensor to be tested 114 is determined based on the standard data (e.g., the first reading) of the standard sensor 112.

[0017] For more information on the sensors under test, reading accuracy, etc., see Figure 2 and its related description.

[0018] The slide rail device 113 is a device for guiding the movement of the sensor. In some embodiments, the adjustable light source 111, at least one standard sensor 112, and at least one sensor to be tested 114 move along the slide rail device. In some embodiments, the slide rail device 113 can be used to adjust the positions of the adjustable light source 111, the standard sensor 112, and the sensor to be tested 114. The position of the sensor to be tested 114 can be determined based on the parameters of the light source to be tested and the test requirements.

[0019] For more information about the parameters of the light source to be tested and the test requirements, please refer to Figure 3 and its related description.

[0020] The communication component 120 refers to a component that realizes communication connection between various components. In some embodiments, various components may include a processing component 130, a standard sensor 112, an adjustable light source 111, and the like.

[0021] In some embodiments, the communication component 120 includes any suitable network, terminal device, etc. that can facilitate information and / or data exchange of the inductive sensor testing system 100. In some embodiments, one or more components of the inductive sensor testing system 100 (e.g., adjustable light source, standard sensor, sensor under test, etc.) can exchange information and / or data with the processing component 130 of the inductive sensor testing system 100 through the communication component 120.

[0022] The processing component 130 refers to a component with computing capability, such as a computer, a computing cloud platform, software, etc. The processing component 130 may include a processing device, such as a CPU, etc. The processing component 130 is used to execute the inductive sensor testing method and process information and / or data related to the inductive sensor testing system.

[0023] In some embodiments, the processing component 130 can process data, information and / or processing results obtained from other devices or system components, and execute program instructions based on these data, information and / or processing results to perform one or more functions described in this specification. For example, the processing component 130 can obtain a first reading of at least one standard sensor and a second reading of at least one sensor to be tested; determine the accuracy of the reading of at least one sensor to be tested; and issue a prompt message.

[0024] In some embodiments, the processing component 130 can obtain a first reading of at least one standard sensor 112 and a second reading of at least one sensor to be tested 114 in response to the adjustable light source 111 being started according to target parameters, determine the reading accuracy of at least one sensor to be tested 114 based on the first reading and the second reading, and issue a prompt message in response to at least one reading accuracy meeting a warning condition, wherein the difference in the number of standard sensors and the number of sensors to be tested is greater than a preset value, and the preset value is determined based on the light intensity.

[0025] In some embodiments, the processing component 130 can determine the setting data of at least one group of standard sensors by referring to a database based on the target parameters and the position distribution data of at least one group of sensors to be tested in the light shielding box, and the setting data characterizes the position distribution of at least one group of standard sensors in the light shielding box.

[0026] In some embodiments, the processing component 130 can also grid the light shielding box based on the light shielding parameters of the light shielding box to obtain at least one grid, and then evaluate the credibility of the at least one grid based on the target parameters and setting data, and then determine at least one second reference vector based on the light loss of the at least one grid, the number of sensors to be tested, and the credibility.

[0027] In some embodiments, the processing component 130 can determine the theoretical reading of the sensor to be tested based on the first reading, setting data of at least one set of standard sensors, position distribution data of at least one sensor to be tested, and target parameters, and then determine the reading accuracy based on the theoretical reading and the second reading.

[0028] In some embodiments, the processing component 130 can also construct a prediction map based on the first reading, setting data of at least one group of standard sensors, location distribution data of at least one group of sensors to be tested, and target parameters, and then determine the theoretical reading based on the prediction map through a reading prediction model, where the reading prediction model is a machine learning model.

[0029] For more information about the functions performed by the processing component 130, see Figure 2-Figure 4 Related description.

[0030] In some embodiments, the inductive sensor test system may include a movable console, a sensor panel, a test seat and a probe. The test seat and the probe are arranged below the movable console and are communicatively connected to the processing component. The sensor panel is arranged above the movable console, and at least one group of sensors to be tested may be arranged on the sensor panel, with the front of the sensors to be tested facing upward and the back of the sensors to be tested being in contact with the test probe.

[0031] In some embodiments, an adjustable light source is provided above the sensor panel. When the adjustable light source is turned on, the processing component controls the movable console to descend, so that the test probe contacts the bottom pads of at least one group of sensors to be tested on the sensor panel, and the sensors to be tested facing the adjustable light source are tested, and the processing component collects test data; after completing the above steps, the processing component controls the movable console to rise, controls the movable console to move, so that the adjustable light source faces the untested sensor, controls the movable console to descend again, and repeats the above test process until all sensors to be tested on the sensor panel are tested.

[0032] In some embodiments, a gray card may be provided at a preset distance above the sensor panel. The processing component controls the movable console to descend, so that the test probe contacts the bottom pads of at least one group of sensors to be tested on the sensor panel, tests the sensors to be tested facing the gray card, and the processing component collects test data; after completing the above steps, the processing component controls the movable console to rise, controls the movable console to move, so that the gray card faces the untested sensor, controls the movable console to descend again, and repeats the above test process until all sensors to be tested on the sensor panel are tested.

[0033] It should be noted that the above description of the inductive sensor test system and its modules is only for the convenience of description and cannot limit the present specification to the scope of the embodiments. It is understandable that for those skilled in the art, after understanding the principle of the system, it is possible to arbitrarily combine the modules or form a subsystem to connect with other modules without deviating from this principle. In some embodiments, Figure 1 The light shielding box 110, the communication component 120 and the processing component 130 disclosed in the specification can be different modules in a system, or one module can realize the functions of two or more modules. For example, each module can share a storage module, or each module can have its own storage module. Such variations are all within the scope of protection of this specification.

[0034] Figure 2 is an exemplary flow chart of the inductive sensor testing method according to some embodiments of this specification. Figure 2 As shown, the process 200 includes the following steps. In some embodiments, the following steps may be performed by the processing component 130 .

[0035] Step 210 , in response to the adjustable light source being started according to the target parameters, obtaining a first reading of at least one standard sensor and a second reading of at least one sensor to be tested.

[0036] The target parameter is a light emitting parameter of the adjustable light source related to the test requirements. In some embodiments, the target parameter may include at least one of the illumination type and the illumination intensity.

[0037] The light intensity refers to the light intensity of the test light emitted by the adjustable light source 111 irradiating the standard sensor and the sensor to be tested.

[0038] The illumination type refers to the type of test light emitted by the adjustable light source 111. In some embodiments, the illumination type may include the wavelength of the light.

[0039] In some embodiments, the target parameters can be determined based on user needs and obtained by manual presetting. For example, in order to verify whether the sensor can accurately detect the light intensity of a light source with a wavelength of λ and an intensity of θ, the user needs to set the light source target parameters to a wavelength of λ and an intensity of θ.

[0040] The sensor under test is the sensor that needs to be tested for reading accuracy.

[0041] In some embodiments, the processing component can control the adjustable light source to emit test light, and obtain at least one first reading reflecting the intensity of the test light generated by at least one standard sensor in the light shielding box, and at least one second reading reflecting the intensity of the test light generated by at least one sensor to be tested. Through at least one first reading and at least one second reading, the test of the sensor to be tested is completed, and the accuracy of its reading is determined. For more relevant content, please refer to this manual Figure 4 See the relevant instructions in .

[0042] The first reading refers to a reading of a standard sensor. In some embodiments, the first reading may be a light intensity value.

[0043] The second reading refers to the reading of the sensor to be tested. In some embodiments, the second reading can be a light intensity value.

[0044] In some embodiments, the processing component 130 can communicate with the standard sensor and the sensor to be tested respectively through the communication component 120 to obtain the first reading and the second reading. In some embodiments, the standard sensor and the sensor to be tested can send the readings based on the actual test to the processing component through the communication component.

[0045] Step 220, determining the reading accuracy of at least one sensor to be tested based on the first reading and the second reading.

[0046] Reading accuracy refers to the accuracy of the reading of the sensor to be tested. In some embodiments, the reading accuracy can determine whether the reading of the sensor to be tested actually meets the standard. The sensor to be tested with lower reading accuracy will be prompted to the user by the processing component.

[0047] In some embodiments, the processing component can determine the reading accuracy of each sensor under test based on the at least one first reading, the target parameter, and the at least one second reading in a variety of ways.

[0048] For example, for any sensor to be tested, the processing component can select a standard sensor that is closest to the sensor to be tested, and obtain the first reading I1 of the standard sensor, the second reading I2 of the sensor to be tested, the distance R1 between the standard sensor and the light source, and the distance R2 between the sensor to be tested and the light source. The processing component can calculate the reading accuracy based on the first reading I1 of the standard sensor, the second reading I2 of the sensor to be tested, the distance R1 between the standard sensor and the light source, and the distance R2 between the sensor to be tested and the light source.

[0049] As an example only, the reading accuracy can be obtained by calculation based on the second reading of the sensor to be tested and the theoretical reading of the sensor to be tested. For example, the reading accuracy of the sensor to be tested satisfies the following formula (1): Reading accuracy = Where I2 is the theoretical reading of the sensor to be tested, and I2' is the second reading of the sensor to be tested. Formula (1) calculates the reading accuracy by the difference between the theoretical reading and the second reading.

[0050] As an example only, the theoretical reading I2 can be obtained by calculation based on the first reading of the standard sensor, the distance between the standard sensor and the light source, and the distance between the sensor to be tested and the light source. For example, the theoretical reading of the sensor to be tested satisfies formula (2):

[0051] Where I1 is the first reading of the standard sensor, R1 is the distance between the standard sensor and the light source, and R2 is the distance between the sensor to be tested and the light source. Formula (2) is obtained by the inverse law of light intensity attenuation, which can be expressed as .

[0052] The above formula (1) and formula (2) are only used as examples and cannot limit the method of calculating the reading accuracy and the theoretical reading of the sensor to be tested. Other methods for determining the reading accuracy based on the positions of the standard sensor and the sensor to be tested, and the readings of the standard sensor and the sensor to be tested can be applied to this embodiment.

[0053] In some embodiments, the processing component can also determine a theoretical reading in a preset manner based on the first reading, setting data of at least one standard sensor, position distribution data of at least one sensor to be tested, and target parameters, and determine the reading accuracy based on the theoretical reading and the second reading.

[0054] In some embodiments, the processing component may also determine a theoretical reading through a reading prediction model based on the first reading, setting data of at least one group of standard sensors, location distribution data of at least one group of sensors to be tested, and target parameters.

[0055] For more information on determining theoretical readings by presetting and predicting readings, see Figure 4 and related instructions.

[0056] Step 230: In response to at least one reading accuracy meeting a warning condition, a prompt message is issued.

[0057] The early warning condition refers to a condition for determining whether to remind the user that the sensor quality is low. In some embodiments, the early warning condition may include that the reading accuracy is less than an accuracy threshold. The accuracy threshold may be determined by manually presetting.

[0058] The prompt information refers to information used to remind the user that the sensor quality is too low. In some embodiments, the prompt information may include, for example, a combination of one or more of numbers, text, information, voice, etc.

[0059] In some embodiments, a prompt message may be sent to a user via a communication component to prompt the user that the quality of the sensor to be tested is low.

[0060] In some embodiments, in response to the reading accuracy of the sensor to be tested being less than the accuracy threshold and within the accuracy tolerance, the processor may determine the sensor to be tested as a sensor to be calibrated and calibrate the sensor to be calibrated based on a preset calibration rule.

[0061] The accuracy tolerance refers to the allowable range of deviation of the reading of the sensor to be tested from the first reading. In some embodiments, the accuracy tolerance can be determined based on prior experience and / or actual needs. As an example only, the standard tolerance can be 20%, that is, when the deviation of the reading of the sensor to be tested from the first reading is within 20%, the processor can determine the sensor to be tested as a sensor to be calibrated, and calibrate the sensor to be calibrated based on a preset calibration rule so that the accuracy of its reading is not less than the accuracy threshold.

[0062] The preset calibration rule refers to a preset strategy for calibrating the sensor to be calibrated. In some embodiments, the preset calibration rule can be determined based on prior experience and / or actual needs. As an example only, the preset calibration rule may include: adjusting the parameters of the sensor to be calibrated, such as gain, offset, etc., according to the comparison result of the first reading and the second reading, so that the reading of the sensor to be calibrated is as close to the first reading as possible. In some embodiments, the processor can adjust the parameters of the sensor to be calibrated according to the preset adjustment range, and obtain the reading of the sensor to be calibrated after the parameter adjustment. If the accuracy of the reading is less than the accuracy threshold, the adjustment is continued. If the accuracy of the reading is not less than the accuracy threshold, the calibration is stopped, and the adjusted sensor to be calibrated is determined to be a qualified sensor.

[0063] In some embodiments, the difference between the number of standard sensors and the number of sensors to be tested is greater than a preset value, wherein the preset value can be determined based on the illumination intensity of the adjustable light source.

[0064] As an example only, the preset value can be calculated based on the light intensity of the target parameter and the recognition range of the sensor. The sensor recognition range refers to the range of measured values ​​within which the error of the sensor is within the specified limit range. In some embodiments, the error within the sensor recognition range can be ignored. For example, the preset value satisfies the following formula (3): Default value = *Standard presets (3) Among them, x is the light intensity of the target parameter, a is the lower limit of the sensor recognition range, and b is the upper limit of the sensor recognition range.

[0065] In some embodiments, the greater the sensor detection error, the more standard sensors are required.

[0066] It can be understood that the farther the light intensity of the target parameter is from the boundary of the sensor recognition range, the more accurate the sensor detection is, and the number of standard sensors can be reduced. The closer the light intensity of the target parameter is to the boundary of the sensor recognition range, the greater the error of sensor detection will be, and more standard sensors will be needed to obtain more accurate detection results.

[0067] In some embodiments of the present specification, the inductive sensor testing system arranges multiple standard sensors and multiple sensors to be tested in a light shielding box, and enables an adjustable light source to provide illumination under the same target parameters. The system can obtain the first reading and the second reading corresponding to each of the standard sensors and the sensors to be tested through a single illumination, and judge the quality of the sensors to be tested by calculating the accuracy of the readings. Multiple sensors to be tested can be tested at one time, thereby reducing possible errors in sensor testing and improving test accuracy and efficiency.

[0068] It should be noted that the above description of the process 200 is only for example and illustration, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the process 200 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0069] Figure 3 is an exemplary schematic diagram of determining setting data of a standard sensor according to some embodiments of the present specification.

[0070] In some embodiments, the processing component may determine the setting data 370 of the at least one group of standard sensors by referring to the database 360 ​​according to the target parameter 310 and the position distribution data 320 of the at least one group of sensors to be tested in the light shielding box.

[0071] The position distribution data reflects the position distribution of the sensors to be tested in the light shielding box.

[0072] In some embodiments, the position distribution data may be represented by a position distribution coordinate set. The position distribution coordinate set includes the coordinates of several sensors to be measured. For example, the processing component may construct a three-dimensional space coordinate system based on a preset origin, and use the coordinates of the sensor to be measured in the light shielding box as elements of the position distribution coordinate set. The preset origin may be preset based on prior experience. For example, the geometric center of the light shielding box may be determined as the preset origin.

[0073] In some embodiments, location distribution data may be determined based on target parameters and user testing requirements.

[0074] User test requirements are test requirements set by users for the sensor to be tested. In some embodiments, user test requirements may include the received light intensity of the sensor to be tested, the distance between the sensor to be tested and the adjustable light source, etc. For example, when the user test requirement is to detect a target parameter of 100Lux, whether the sensor to be tested 5m away can accurately detect the light intensity, the processing component can determine several positions 5m away from the adjustable light source as target positions, and determine the position distribution data based on the aforementioned target positions. For another example, when the user test requirement is to detect whether the reading of the sensor to be tested is accurate when the received light intensity is 50Lux, at this time, the processing component can calculate the position that can make the received light intensity of the sensor to be tested 50Lux based on methods such as the light attenuation equation, and determine the coordinates corresponding to the position as the position distribution data. For more information about the light attenuation equation, see Figure 2 It is understandable that when setting the position of the sensor to be tested, each sensor to be tested should be placed at a position where the test light emitted by the adjustable light source can directly illuminate it.

[0075] The setting data 370 may reflect the position distribution of the standard sensors within the light shielding box.

[0076] In some embodiments, the position distribution data may be represented by setting a coordinate set. The configuration of the setting coordinate set is similar to that of the position distribution coordinate set, and reference may be made to the above related description.

[0077] In some embodiments, the setting data may be determined by reference to a database.

[0078] The reference database is a database used to determine the setting data. In some embodiments, the reference database includes at least one reference vector. The reference vector may include a first reference vector 310 and a second reference vector 320.

[0079] The first reference vector is one of the reference vectors used to determine the spatial distribution of the standard sensor.

[0080] The first reference vector includes at least one position element representing the position of the standard sensor and one parameter element representing the reference target parameter. In some embodiments, the first reference vector corresponds to the situation where the standard sensors are irregularly distributed in the light shielding box.

[0081] In some embodiments, the processing component may determine at least one first reference vector based on gold standard simulation data.

[0082] Gold standard simulation data refers to test-related data obtained by performing simulation tests on standard sensors.

[0083] In some embodiments, the gold standard simulation data may include reference position distribution data of a standard sensor, test readings, and reference target parameters used for simulation when performing a simulation test.

[0084] In some embodiments, the processing component can randomly generate multiple groups of reference position distribution data, each group of reference position distribution data corresponds to at least one reference target parameter. By randomly combining the aforementioned multiple groups of reference position distribution data and at least one reference target parameter, multiple groups of data to be simulated are obtained, each group of data to be simulated includes a group of position distribution data and a reference target parameter.

[0085] In some embodiments, the number of standard sensors in each set of reference position distribution data may be determined based on a preset number interval. For example, the preset number interval may be 20 to 50, and the number of standard sensors in the generated multiple sets of reference position distribution data may be any value between 20 and 50. The number of groups with different numbers of standard sensors is evenly distributed.

[0086] In some embodiments, each piece of gold standard simulation data has a corresponding label, which may be the number of the standard sensor used for the simulation test.

[0087] In some embodiments, the reference target parameter can be determined based on historical experience or actual demand. For example, the processing component can determine the reference target parameter based on at least one historical actual target parameter. For another example, the processing component can determine a new reference target parameter based on actual demand.

[0088] In some embodiments, the processing component may generate as much reference position distribution data and reference target parameters as possible to ensure that the number of samples for the simulation test is sufficient, thereby facilitating screening to obtain sufficient target data.

[0089] In some embodiments, the processing component can perform simulation tests on the aforementioned multiple groups of data to be simulated by various simulation methods to obtain test readings of the standard sensor under different reference target parameters. Based on the data to be simulated and its corresponding test readings, the gold standard simulation data is determined. The simulation method may include but is not limited to simulation experiments, computer simulation, etc.

[0090] In some embodiments, the processing component may screen the aforementioned gold standard simulation data, and determine the gold standard simulation data whose test results meet the retention conditions as the target data.

[0091] In some embodiments, the retention condition may include that the relative error of the test is less than a preset retention threshold. The smaller the relative error, the better the effect of the test under the gold standard simulation data.

[0092] In some embodiments, for a gold standard simulation data, the processing component can randomly select several standard sensors and their corresponding test readings from multiple standard sensors, and determine the test evaluation values ​​corresponding to the standard sensors based on the distances between the aforementioned several standard sensors and the test light source and the test readings.

[0093] For example, the processing component may record the ratio of the distance of each standard sensor from the test light source to the square of the test reading as the test evaluation value S. The variance of the test evaluation value S of at least one standard sensor is calculated. , the variance It is recorded as relative error. When the value is less than the preset retention threshold, it is considered that the gold standard simulation data meets the test requirements and the group of gold standard simulation data can be retained. The preset retention threshold can be preset based on prior experience.

[0094] For example, the processing component can randomly select 20 standard sensors from a gold standard simulation data, and the distance to the test light source and the test reading are recorded as (L1, R1), (L2, R2), ..., (L20, R20) respectively. Among them, the first element in the vector refers to the distance, and the second element is the test reading. The processing component can calculate the test evaluation value of each standard sensor, such as L1 / (R1^2), recorded as S1; calculate L2 / (R2^2), recorded as S2; and so on, obtain S1, S2, ..., S20 and calculate the variance to obtain the relative error corresponding to the gold standard simulation data. . judge Whether it is less than the preset retention threshold, if it is less than the preset retention threshold, it is determined to retain the gold standard simulation data.

[0095] For another example, the processing component may randomly select different numbers of standard sensors multiple times, and calculate the relative error multiple times based on the above method based on the distance between the selected standard sensors and the test light source and the test readings. , in response to the relative error If the average value of is less than the preset retention threshold, the gold standard simulation data is retained.

[0096] Through the above method, the processing component can determine at least one target data. In some embodiments, the processing component can construct a first vector based on the target data. For example, based on a preset encoding rule and an encoding upper limit, the position coordinates of the standard sensor in the target data are encoded; the encoded position coordinates are sorted according to the encoding value, and the first reference vector is constructed in combination with the reference target parameters contained in the target data.

[0097] In some embodiments, the first reference vector has a label, and the label can be determined based on the label of the corresponding target data.

[0098] In some embodiments, the processing component may encode the placement position of the sensor in the light shielding box by using a coding rule, so that each position corresponds to a unique coding value.

[0099] In some embodiments, the encoding value may be a positive integer. For example, for a position to be encoded, the processing component may obtain the coordinates of the position in a three-dimensional space coordinate system and encode based on an encoding rule. As an example only, the encoding rule may be: sorting from small to large according to the value of the coordinate X axis; for the same coordinates on the X axis, sorting the Y axis from small to large; for the same coordinates on the X axis and the Y axis, sorting the Z axis from small to large.

[0100] After the sorting is completed, 1 is used as the encoding start value, and the first-ranked coordinate (that is, the smallest value on the X-axis) is encoded as 1, the second-ranked coordinate is encoded as 2, and so on. When a coordinate that needs to be encoded appears again, the processing component can compare the coordinate with the previously encoded coordinate. When the coordinate has a code, the existing code is used. When there is no corresponding code for the coordinate, the processing component can sort from small to large along the X-axis, Y-axis, and Z-axis in sequence, select the smallest code value that does not have a corresponding coordinate as the encoding start value, and encode according to the sorting.

[0101] For example, target data 1 includes 3 standard sensors, and the corresponding coordinates are: [(0.5, 0.5, 0.5), (0.5, 1, 0.5), (1, 0.5, 1)]. The processing component can sort the X-axis, Y-axis, and Z-axis in descending order, and encode the coordinate (0.5, 0.5, 0.5) as 1, the coordinate B (0.5, 1, 0.5) as 2, and the coordinate (1, 0.5, 1) as 3. Target data 2 includes 5 standard sensors, and the corresponding coordinates are: [(0.5, 0.5, 0.5), (0.5, 1, 0.5), (1, 0.5, 1), (0.5, 1, 1), (1, 2.5, 2)]; the coordinates (0.5, 0.5, 0.5), (0.5, 1, 0.5), and (1, 0.5, 1) have been encoded as 1, 2, and 3, so the previous encoding is directly used. The coordinates (0.5, 1, 1) and (1, 2.5, 2) have not appeared before and are not encoded. Sort them from small to large according to the X-axis, Y-axis, and Z-axis. Coordinates (0.5, 1, 1) are encoded as 4, and coordinates (1, 2.5, 2) are encoded as 5. And so on, all positions where standard sensors can be placed are encoded.

[0102] In some embodiments, the processing component can construct an initial first reference vector based on the code corresponding to the standard sensor position in the target data and the target parameter. For example, when the code corresponding to the standard sensor position in target data 1 is 1, 2, 3 and the target parameter is the luminous intensity of 400 Lux, the corresponding first reference vector is (1, 2, 3; 400 Lux).

[0103] In some embodiments, in order to facilitate comparison and calculation between different vectors, the processing component can pad the initial first reference vector based on the coding upper limit so that the number of elements contained in all first reference vectors is consistent. The padding method can be: first add a number of 0 elements to the end of the initial first reference vector to pad the number of elements of the first reference vector to the coding upper limit W+ΔW. Then reorder the elements in the initial first reference vector, move the element coded as n to the nth position of the vector, and fill the remaining positions with 0 elements to obtain the first reference vector.

[0104] For example, the current encoding upper limit is 10, and the initial first reference vector corresponding to standard data 2 is (1, 3, 4, 7, 9; 400 Lux). First, add several 0-position elements to the vector to fill the vector, and get (1, 3, 4, 7, 9, 0, 0, 0, 0, 0; 400 Lux). Then reorder the vector, move the position elements encoded as 1, 3, 4, 7, 9 to the 1st, 3rd, 4th, 7th, and 9th positions of the vector respectively, and use 0 elements to fill the remaining positions, and get the first reference vector (1, 0, 3, 4, 0, 0, 7, 0, 9, 0; 400 Lux).

[0105] In some embodiments, the processing component can count the total number W of different emission positions of the standard sensor in the gold standard simulation data whose test results meet the requirements, and set W+ΔW as the upper limit of the encoding of the position element of the first reference vector. ΔW is the element threshold, and the value of ΔW can be determined based on prior experience.

[0106] The second reference vector is one of the reference vectors used to determine the spatial distribution of the standard sensor.

[0107] In some embodiments, the second reference vector includes at least one position element representing the position of the standard sensor and a parameter element representing the reference target parameter. In some embodiments, the second reference vector corresponds to a situation where the standard sensors are neatly arranged in the light shielding box.

[0108] In some embodiments, the processing component can also grid the shading box based on the shading parameters to obtain at least one grid; evaluate the credibility 330 of at least one grid according to the target parameters and position distribution data; and determine the at least one second reference vector 320 based on the light loss 350 of at least one grid, the number 340 of the standard sensors and the credibility 330.

[0109] In some embodiments, the second reference vector may have a label, and the label may be determined based on the number of standard sensors in the second reference vector.

[0110] The shading parameters refer to parameters that can reflect the shading properties of the shading box. In some embodiments, the shading parameters may include the length, width, height, etc. of the interior of the shading box. In some embodiments, the shading parameters may be obtained by reading the specification data of the shading box and / or by obtaining manual input.

[0111] In some embodiments, the processing component can perform gridding processing on the light shielding box according to the grid parameters based on the light shielding parameters of the light shielding box to obtain at least one grid, wherein the grid parameters may include grid shape (e.g., cuboid, cube, etc.) and grid size (e.g., length, width, height, etc.).

[0112] In some embodiments, the maximum number of sensors in each grid is 1, the grid parameters can be preset based on prior experience, and the grid size should be at least larger than the average size of the sensor. In some embodiments, the processing component can model the internal space of the shading box in a three-dimensional space coordinate system according to the shading parameters, and divide the modeling based on the grid parameters to obtain a plurality of grids with grid shapes and grid sizes.

[0113] In some embodiments, the processing component can screen the grids to exclude grids located in the shadow of the sensor, thereby ensuring that the light intensity in the remaining grids is not affected by the sensor. The processing component can randomly select a preset number of grids whose grid credibility meets the requirements from the remaining grids as distributable grids where the standard sensor can be placed, select a position for placing the standard sensor from each distributable grid, and obtain the coordinates of the position, encode the position coordinates with reference to the encoding rules, and use the encoded value as the position element of the initial second reference vector. The corresponding target parameter is used as the target parameter element of the initial second reference vector. The initial second reference vector is padded to obtain the second reference vector. By continuously repeating the above steps, several second reference vectors can be obtained.

[0114] The grid credibility satisfies the requirement that the grid credibility is greater than a preset grid credibility threshold. The preset distributable number is positively correlated to the number of standard sensors; the specific steps of constructing and completing the second reference vector are similar to those of the first reference vector, and the relevant content can be found above.

[0115] The credibility can reflect the reliability of the readings of the sensors to be tested in a certain grid. In some embodiments, the credibility of the grid can be represented by a numerical value, and the closer the numerical value is to 1, the higher the reliability of the current grid.

[0116] In some embodiments, the processing component can determine the trustworthiness in a variety of ways.

[0117] In some embodiments, the processing component may determine the credibility of the grid based on a first preset table. The processing component may construct a first preset table based on historical target parameters, historical location distribution data, historical grid locations, and historical credibility. The first preset table contains the correspondence between different historical target parameters, historical location distribution data, historical grid locations, and different historical credibility. The processing component may determine the current credibility by querying the first preset table based on the current target parameters, location distribution data, and grid location. The grid location may be the coordinates of the geometric center point of the grid.

[0118] In some embodiments, the reliability of the grid can be determined based on the light intensity at key positions associated with the grid, wherein the key positions include the center point of the grid and the position of the sensor to be tested that is closest to the grid.

[0119] In some embodiments, the processing component may select a sensor to be tested that is closest to the geometric center of the grid, calculate a first light intensity at the center point of the sensor to be tested, and calculate a second light intensity at the center point of the grid; and determine credibility based on a ratio of the second light intensity to the first light intensity.

[0120] In some embodiments, the illumination intensity of the sensor to be tested or the center point of the grid is positively correlated to the illumination intensity of the adjustable light source, and negatively correlated to the distance from the center point of the sensor to be tested or the center point of the grid to the adjustable light source.

[0121] In some embodiments, the processing component can calculate the light intensity L at the center point of the sensor to be tested or the center point of the grid by formula (4):

[0122] Wherein, L represents the light intensity of the center point of the sensor to be tested or the center point of the grid, G represents the light intensity of the adjustable light source, R represents the distance from the center point of the sensor to be tested or the center point of the grid to the adjustable light source, and μ is a coefficient. The coefficient μ can be preset based on prior experience. The processing component can obtain the coordinates of the center point of the sensor to be tested or the center point of the grid, and calculate the distance R between the two coordinates using the Euclidean distance formula.

[0123] Some embodiments of the present specification divide the internal space of the light shielding box by using a grid so that there can be at most one sensor in each grid, thereby ensuring that the sensors are not arranged too closely to affect each other, ensuring that the sensors can be arranged in an orderly manner and convenient for installation and calculation; the second standard vector can be supplemented according to the current situation, which on the one hand reduces the amount of calculation, and on the other hand can make the vectors in the standard vector library more targeted, which can improve the calculation speed during matching.

[0124] In some embodiments, the grid credibility is also related to the adjustable light source position and at least one relative position. The processing component can evaluate the light loss degree of at least one grid through the light loss degree prediction model according to the target parameter, the position distribution data, the adjustable light source position and the relative position, and evaluate the grid credibility of at least one grid based on the light loss degree of at least one grid. For an explanation of the target parameters, please refer to this specification Figure 2 For the description of position distribution data and adjustable light source position, please refer to the previous description.

[0125] The relative position refers to the position of the grid relative to the light shielding box. In some embodiments, the relative position can be represented by a vector. The starting point of the vector can be the geometric center of the light shielding box, and the end point of the vector can be the geometric center of the grid.

[0126] Light loss refers to the phenomenon that the light emitted by the adjustable light source is lost during the propagation process. For example, diffuse reflection occurs during the propagation process, and it is absorbed by dust impurities in the air. Light loss will cause the actual light intensity received by the sensor to be less than the theoretical light intensity. In some embodiments, light loss can be expressed as a percentage of light loss during propagation.

[0127] The optical loss degree of the grid refers to the loss degree of the test light emitted by the adjustable light source when it reaches the position of the grid.

[0128] The light loss degree prediction model is a prediction model for determining light loss. In some embodiments, the light loss degree prediction model may be a machine learning model, such as a neural network (NN) model or any combination of other custom model structures.

[0129] In some embodiments, the input of the light loss degree prediction model may include target parameters, position distribution data, adjustable light source positions, and relative positions of at least one grid, and the output may include the light loss degree of at least one grid.

[0130] In some embodiments, the processing component can obtain a light loss degree prediction model through training based on a large number of first training samples with first labels. In some embodiments, the first training samples can include sample target parameters, sample position distribution data, sample adjustable light source positions, and sample relative positions. The first label can be a sample light loss degree of at least one grid.

[0131] In some embodiments, the first training sample can be obtained based on historical data. The actual light loss degree corresponding to at least one grid can be obtained by using an instrument such as a light loss detector as the first label corresponding to the first training sample.

[0132] In some embodiments, the processing component can be trained by various methods based on the first training sample and the first label. For example, the training can be based on the gradient descent method. As an example only, a plurality of first training samples with the first label can be input into the optical loss degree prediction model, a loss function can be constructed by the first label and the result of the optical loss degree prediction model, and the parameters of the optical loss degree prediction model can be iteratively updated based on the loss function. When the loss function of the optical loss degree prediction model meets the preset conditions, the model training is completed, and a trained missed detection degree determination model is obtained. Among them, the preset conditions can be that the loss function converges, the number of iterations reaches a threshold, etc.

[0133] In some embodiments, the grid credibility may be determined based on the light loss corresponding to the grid, the light intensity at the center point of the grid, and the distance between the center point of the grid and the adjustable light source.

[0134] In some embodiments, the grid credibility D can be calculated by formula (5):

[0135] Where D represents the grid credibility, F represents the light loss corresponding to the grid, L2 represents the light intensity at the center of the grid, R represents the distance between the center of the grid and the adjustable light source, and μ is a coefficient. The coefficient μ can be preset based on prior experience.

[0136] Some embodiments of the present specification determine light loss using a light loss degree prediction model, which can fully utilize the model's data processing and analysis capabilities, comprehensively consider the impact of multiple factors on the light loss degree, and thus improve the accuracy and reliability of light intensity prediction.

[0137] The vector to be matched is a vector used to represent the position distribution of the sensor to be tested. In some embodiments, the vector to be matched may include several position elements and a target parameter element, each position element corresponds to the position of a sensor to be tested in the light shielding box, and the target parameter element corresponds to the target parameter.

[0138] In some embodiments, the processing component can determine the coordinates of the sensor to be tested based on the position distribution data of the sensor to be tested, encode according to the aforementioned encoding rules, use the encoding result as the position element of the initial vector to be matched, use the corresponding target parameter as the target parameter element, and fill the initial vector to be matched to obtain the vector to be matched. The filling method can be referred to the relevant description above.

[0139] In some embodiments, the processing component may perform similarity matching between the vector to be matched and the candidate reference vector, select the candidate reference vector with the highest similarity as the target reference vector, and determine the setting data of at least one standard sensor based on the target reference and the vector to be matched. The similarity matching algorithm may include but is not limited to the Manhattan distance method, the SVM method, etc.

[0140] The candidate reference vector may be a reference vector whose tag meets a selection condition, wherein the selection condition may be that the tag value is equal to the number of sensors in the vector to be matched, wherein the number of sensors in the vector to be matched is the sum of the number of standard sensors and the number of sensors to be tested.

[0141] In some embodiments, the processing component can calculate the absolute value of the difference between the target reference vector and the vector to be matched, and determine the elements in the absolute value that are not zero and are not light source parameters as setting data. For example, the vector to be matched is (1, 0, 0, 4, target parameter 5), and the target reference vector is (1, 2, 3, 4, target parameter 6); the absolute value of the difference is (0, 2, 3, 0, |target parameter 5-target parameter 6|), and the elements whose values ​​are not zero and are not light source parameters are determined as position distribution (2, 3). During testing, the standard sensor can be arranged at point B coded as 2 and point C coded as 3.

[0142] In some embodiments of the present specification, a reference database is constructed based on gold standard simulation data, and the required first vector is determined through similarity matching, thereby determining the placement position of the standard sensor. This can effectively ensure that the relative position of the standard sensor and the sensor to be tested is basically consistent, avoid deviations due to unreasonable position distribution, and improve the accuracy and reliability of sensor monitoring.

[0143] Figure 4 is an exemplary schematic diagram of determining reading accuracy according to some embodiments of the present specification.

[0144] In some embodiments, the processing component can determine the theoretical reading 450 of the sensor to be tested based on the first reading 410, the setting data 420 of at least one standard sensor, the position distribution data 320 of at least one sensor to be tested, and the target parameter 310; and determine the reading accuracy 470 based on the theoretical reading and the second reading 460.

[0145] The reading accuracy can reflect the accuracy of the light intensity measurement result of the sensor to be tested. In some embodiments, the reading accuracy can be negatively correlated to the reading difference value. The reading difference value refers to the difference between the second reading corresponding to the sensor to be tested and its theoretical reading.

[0146] In some embodiments, the reading accuracy corresponding to the sensor to be tested can be calculated by formula (6):

[0147] Wherein, η represents the reading accuracy, G represents the second reading corresponding to the sensor to be tested, and V represents the theoretical reading corresponding to the sensor to be tested. Wherein, the second reading corresponding to the sensor to be tested can be obtained based on the test result, and the relevant content of obtaining the theoretical reading can be referred to the following content.

[0148] The theoretical reading of the sensor to be tested refers to the reading of the sensor to be tested under an ideal state. In some embodiments, the sensor can determine the theoretical reading in a variety of ways.

[0149] In some embodiments, the processing component can determine the theoretical reading by a preset method based on the first reading and position data of the standard sensor, the position distribution data of the sensor to be tested, and the target parameter. For example, at least one candidate sensor group corresponding to the sensor to be tested is determined from the standard sensor; based on the isotropic credibility of each candidate sensor group, a target sensor group for calculating the theoretical reading of the sensor to be tested is determined; based on the reference theoretical reading of the reference sensor in the target sensor group, the theoretical reading of the sensor to be tested is determined.

[0150] In some embodiments, the processing component may determine the candidate reference sensors according to a selection rule based on the setting data of the standard sensors and the position distribution data of the sensor to be tested. In some embodiments, the selection rule may be: sorting the standard sensors according to the distance from the sensor to be tested from near to far, and selecting the first N standard sensors with the smallest distance as the candidate reference sensors.

[0151] In some embodiments, the processing component may randomly select a number of candidate reference sensors from the candidate reference sensors to form a candidate sensor group; and by repeating the aforementioned random selection steps, a number of candidate sensor groups may be obtained.

[0152] In some embodiments, the processing component may calculate the isotropic credibility of each candidate sensor group, and screen out the target sensor based on the isotropic credibility.

[0153] The isotropy credibility can reflect the overall reliability of the sensor readings in the candidate sensor group. The larger the value of the isotropy credibility is, the higher the credibility of the sensor readings included in the candidate sensor group is, and the higher the spatial isotropy is.

[0154] In some embodiments, the processing component may select any two candidate reference sensors from the candidate sensor group and obtain their corresponding first readings, and calculate the theoretical relative distance ratio between the two candidate reference sensors and the adjustable light source based on the first readings. , based on the setting data and the position distribution data, calculate the actual relative distance ratio between the two sensors and the adjustable light source .

[0155] calculate The value of is recorded as ∝.

[0156] Traverse all combinations of two candidate reference sensors selected from the candidate sensor group to obtain several groups of ∝ values, and calculate the average value of the aforementioned several groups of ∝ values Then the isotropic credibility of the candidate reference sensor group is equal to 1- .

[0157] In some embodiments, the processing component may sort the candidate sensor groups corresponding to the sensor to be tested from high to low according to isotropic credibility, select the candidate sensor group with the highest isotropic credibility, and determine all candidate reference sensors contained therein as target sensors.

[0158] In some embodiments, the processing component can calculate at least one candidate theoretical reading of the sensor to be tested based on the position distribution data of the sensor to be tested, the setting data of at least one target sensor, and the first reading of the standard sensor, and determine the theoretical reading of the sensor to be tested based on the statistical value (e.g., average, median, etc.) of at least one candidate theoretical reading. For example, the average value of at least one candidate theoretical reading can be determined as the theoretical reading of the sensor to be tested. The candidate theoretical reading of the sensor to be tested can be obtained based on the inverse square law of light intensity attenuation. For more information about the inverse square law, please refer to Figure 2 See the relevant instructions in .

[0159] Some embodiments of the present specification can quantify the detection accuracy of the sensor to be tested by determining the theoretical reading of each sensor to be tested and the corresponding reading accuracy, which helps to evaluate the test effect of each sensor to be tested, thereby improving the consistency and reliability of the inductive sensor testing system.

[0160] In some embodiments, the position distribution data can be determined based on the parameters of the light source to be measured and the user's test requirements, and the setting data can be determined based on the vector matching method of the first reference vector. For relevant descriptions of the parameters of the light source to be measured, the user's test requirements, and the vector matching method, please refer to Figure 2 Related content in .

[0161] In some embodiments, the processing component can construct a prediction map 430 based on the first reading 410, the setting data 420 of at least one standard sensor, the position distribution data 320 of at least one sensor to be tested, and the target parameter 310; based on the prediction map 430, determine the theoretical reading 450 through the reading prediction model 440.

[0162] The prediction map is a map that shows the distribution of devices in the light-shielding box. The prediction map can reflect the characteristics of the distribution of standard sensors, sensors to be tested, and adjustable light sources in the light-shielding box.

[0163] In some embodiments, the prediction graph may be composed of at least one node and at least one edge, the node having corresponding node attributes, and the edge having corresponding edge attributes.

[0164] In some embodiments, the node attributes include at least the node coordinates.

[0165] The nodes include a standard node 432, a node to be tested 433, and a light source node 431. The standard node is used to characterize a standard sensor, the node to be tested is used to characterize a sensor to be tested, and the light source node is used to characterize an adjustable light source. The node attributes of the standard node also include a first reading, the node attributes of the sensor node to be tested also include a second reading, and the node attributes of the adjustable light source node also include a target parameter.

[0166] In some embodiments, the coordinates of the standard node may correspond to the coordinates of the standard sensor in the three-dimensional space coordinate system, the coordinates of the node to be measured may correspond to the coordinates of the standard sensor to be measured in the three-dimensional space coordinate system, and the coordinates of the light source node may correspond to the coordinates of the adjustable light source in the three-dimensional space coordinate system. The processing component may construct a three-dimensional space coordinate system in the light shielding box based on a preset origin, and determine the coordinates of the standard sensor to be measured and the adjustable light source based on the positions of the sensor to be measured and the adjustable light source in the three-dimensional space coordinate system.

[0167] In some embodiments, any two nodes may be connected via an edge. The attributes of the edge may include the distance between the nodes connected by the edge.

[0168] In some embodiments, the edges include first-type edges 435 and second-type edges 434. The first-type edges are used to connect sensors to sensors, and the second-type edges are used to connect adjustable light sources to sensors.

[0169] In some embodiments, the edge feature of the first type of edge further includes an influence coefficient.

[0170] The influence coefficient can reflect the degree to which a sensor is interfered with by other sensors. In some embodiments, since the light irradiated to the sensor surface may not be completely absorbed, reflection, refraction and other phenomena may occur, and sensors may block each other, causing interference between sensors, resulting in reduced sensor detection accuracy. Therefore, the influence coefficient can be used to measure the degree of such interference.

[0171] The influence coefficient determination model is a prediction model for determining the influence coefficient. In some embodiments, the influence coefficient determination model may be a neural network (NN) model.

[0172] In some embodiments, the input of the influence coefficient determination model may include node attributes corresponding to two nodes connected by the first type of edge, and the output may be the influence coefficient of each sensor.

[0173] In some embodiments, the processing component can train an influence coefficient determination model based on a large number of third training samples with a third label. The processing component can determine the node coordinates and node types corresponding to two nodes connected by the first type of edge in the prediction map as the third training sample. The third label can be obtained based on a simulation experiment. For example, the test environment of the current prediction map can be simulated in the experiment, and only the sensor 1 corresponding to a node connected by the first type of edge is placed, and the reading A1 of the sensor is obtained. The sensor 2 corresponding to the other connected node is placed, and the reading A2 of the sensor is obtained. Calculate (A1-A2) / A1, determine the calculation result as the influence coefficient, repeat the experiment several times, and calculate the average value of the influence coefficients corresponding to all the first type edges of the prediction map, and determine it as the third label.

[0174] The specific steps of determining the training influence coefficient are similar to the steps of training the optical loss degree prediction model, and can refer to the relevant content above.

[0175] In some embodiments of the present specification, by using an influence coefficient determination model to determine the influence coefficient and including the influence coefficient in a third training sample, the influence coefficient determination model can comprehensively consider the mutual influence between multiple sensors and improve the generalization ability and adaptability of the model.

[0176] The reading prediction model is a prediction model for predicting the theoretical reading of the sensor to be tested. In some embodiments, the reading prediction model can be a machine learning model, such as a graph neural network (GNN) model or any combination of other custom model structures.

[0177] In some embodiments, the input of the reading prediction model may include a prediction graph. The output of the reading prediction model may include a theoretical reading of the sensor to be tested corresponding to the node.

[0178] In some embodiments, the processing component can train a reading prediction model based on a plurality of second training samples with second labels.

[0179] In some embodiments, the processing component can construct a sample prediction map based on historical first readings, historical setting data, and historical target parameters in historical test data, wherein the historical test data refers to data obtained based on simulated experiments or actual historical tests.

[0180] In some embodiments, for each second training sample, the processing component may determine the historical standard sensor based on the historical setting data, randomly select a first preset number of historical standard sensors therefrom, and construct a first preset number of sample nodes in the sample prediction map. The position of the sample node in the sample prediction map corresponds to the coordinate of the historical standard sensor in the light shielding box.

[0181] In some embodiments, the first preset number corresponding to each second training sample may be the same or different, the first preset number may be selected from a first preset range, the first preset range may be positively correlated to the number of historical standard sensors, and the value of the first preset range may be determined based on prior experience.

[0182] In some embodiments, the processing component can determine the historical coordinates and historical target parameters of the adjustable light source based on the historical setting data, construct a corresponding sample light source node in the sample prediction map, and determine the historical target parameters as the sample target parameters of the sample light source node.

[0183] In some embodiments, the processing component may select a second preset number of sample nodes from the sample prediction graph and determine them as sample test nodes, and determine the unselected sample nodes as sample standard nodes. The second preset number may be preset based on prior experience.

[0184] In some embodiments, the second preset numbers corresponding to each second training sample may be the same or different.

[0185] In some embodiments, when generating the second training sample, the processing component may perform random sampling in at least one preset proportion, and determine the second preset number based on the result of the random sampling.

[0186] The preset proportion refers to the percentage of the preset second preset number to the first preset number. In some embodiments, the processing component can determine at least one preset proportion by random generation, based on manual input and / or according to historical test data.

[0187] In some embodiments, at least one preset proportion has certain distribution characteristics, such as a discrete degree.

[0188] In some embodiments, the distribution characteristic of at least one preset ratio can be represented by a variance. The larger the variance, the more dispersed the distribution of at least one preset true ratio.

[0189] In some embodiments, the variance of at least one preset proportion is related to the size parameter of the light shielding box. For example, the variance may be positively correlated to the size parameter of the light shielding box, and the larger the volume of the light shielding box, the larger the variance of at least one preset proportion.

[0190] In some embodiments, when determining the second preset number, the processing component may perform a random sampling from at least one preset proportion to obtain at least one target proportion, and determine at least one second preset number based on the target proportion and the first preset number.

[0191] Since the larger the size of the light shielding box, the greater the instability of the test results, therefore, in some embodiments of this specification, when constructing the second training sample, for a light shielding box with a larger size, the variance of at least one preset proportion can be increased to ensure the diversity of the second training sample and the stability of the test results.

[0192] In some embodiments, the processing component can determine the node attributes of the sample to-be-tested node and the sample standard node. For the sample to-be-tested node, the historical first reading of the corresponding historical standard sensor is determined as the sample second reading; for the sample standard node, the historical first reading of the corresponding historical standard sensor is determined as the sample first reading. The coordinates of the node corresponding to the historical standard sensor are determined as the coordinates of the sample to-be-tested node and the sample standard node.

[0193] In some embodiments, the processing component can determine the sample edge and the corresponding sample edge attribute. The processing component can connect any two sample nodes and determine whether the sample edge is a first-class edge or a second-class edge based on the types of the two sample nodes (sample standard node, sample test node, or sample light source node). Based on the path distance between the two sample nodes, the sample distance included in the edge attribute is determined.

[0194] In some embodiments, the processing component may generate a required number of sample prediction graphs as second training samples by repeating the above steps.

[0195] In some embodiments, the second label may be a sample second reading of a sample node to be tested corresponding to the second training sample. The sample second reading may be determined based on a historical second reading in the historical data.

[0196] The specific steps of training the reading prediction model are similar to the steps of training the light loss degree prediction model, and can be found in the relevant content above.

[0197] In some embodiments of the present specification, the theoretical reading is determined by constructing a prediction graph as an input to a machine learning model, and the data processing and data analysis capabilities of the machine learning model can be fully utilized to quickly and accurately obtain the required theoretical reading.

[0198] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0199] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0200] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0201] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0202] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining the digits. Although the numerical domains and parameters used to confirm the breadth of the range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0203] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification is hereby incorporated by reference in its entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and / or use of terms in this specification shall prevail.

[0204] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. An inductive sensor testing system, characterized in that: Includes a light shielding box, a communication component, and a processing component; An adjustable light source, at least one set of standard sensors and a slide rail device are arranged in the light shielding box, and at least one set of sensors to be tested is placed in the light shielding box for testing; The adjustable light source emits non-parallel light into the light shielding box; The adjustable light source, the at least one group of standard sensors, and the at least one group of sensors to be tested move along the slide rail device; The processing component is respectively connected to the adjustable light source, the at least one group of standard sensors, and the at least one group of sensors to be tested through the communication component; The processing component is configured to: In response to the adjustable light source being activated according to a target parameter, obtaining a first reading of the at least one group of standard sensors and a second reading of the at least one group of sensors to be tested; the target parameter includes the light intensity of the adjustable light source; Determining the accuracy of the readings of the at least one group of sensors to be tested based on the first readings and the second readings; In response to at least one of the readings meeting the warning condition, a prompt message is issued; wherein, The difference between the number of the standard sensors and the number of the sensors to be tested is greater than a preset value, and the preset value is determined based on the light intensity.

2. The system according to claim 1, characterized in that The processing component is further configured to: According to the target parameters and the position distribution data of the at least one group of sensors to be tested in the light shielding box, setting data of the at least one group of standard sensors is determined by referring to a database, wherein the setting data represents the position distribution of the at least one group of standard sensors in the light shielding box; The reference database includes at least one first reference vector.

3. The system according to claim 2, characterized in that The reference database further includes at least one second reference vector; The processing component is further configured to: Performing gridding processing on the light shielding box based on the light shielding parameters of the light shielding box to obtain at least one grid; Evaluating the credibility of the at least one grid according to the target parameters and the location distribution data; The at least one second reference vector is determined based on the optical loss of the at least one grid, the number of the sensors to be measured and the confidence level.

4. The system according to claim 1, characterized in that The processing component is further configured to: Determine a theoretical reading of the sensor to be tested based on the first reading, the setting data of the at least one group of standard sensors, the position distribution data of the at least one sensor to be tested, and the target parameter; Based on the theoretical reading and the second reading, the reading accuracy is determined.

5. The system according to claim 4, characterized in that The processing component is further configured to: constructing a prediction map based on the first reading, the setting data of the at least one group of standard sensors, the position distribution data of the at least one group of sensors to be tested, and the target parameter; Based on the prediction map, the theoretical reading is determined by a reading prediction model, and the reading prediction model is a machine learning model.

6. A method for testing an inductive sensor, characterized in that: The method is performed by a processing component of an inductive sensor testing system, and includes: In response to the adjustable light source being activated according to a target parameter, obtaining first readings of at least one set of standard sensors and second readings of at least one set of sensors to be tested; the target parameter includes the light intensity of the adjustable light source; Determining the accuracy of the readings of the at least one group of sensors to be tested based on the first readings and the second readings; In response to at least one of the readings meeting the warning condition, a prompt message is issued; wherein, The difference between the number of the standard sensors and the number of the sensors to be tested is greater than a preset value, and the preset value is determined based on the light intensity.

7. The method according to claim 6, characterized in that The method further comprises: According to the target parameters and the position distribution data of the at least one group of sensors to be tested in the light shielding box, setting data of the at least one group of standard sensors is determined by referring to a database, wherein the setting data represents the position distribution of the at least one group of standard sensors in the light shielding box; The reference database includes at least one first reference vector.

8. The method according to claim 7, characterized in that The reference database further includes at least one second reference vector; The method further comprises: Performing gridding processing on the light shielding box based on the light shielding parameters of the light shielding box to obtain at least one grid; Evaluating the credibility of the at least one grid according to the target parameters and the location distribution data; The at least one second reference vector is determined based on the optical loss of the at least one grid, the number of the sensors to be measured and the confidence level.

9. The method according to claim 6, characterized in that The step of determining the reading accuracy of the at least one group of sensors to be tested based on the first reading and the second reading includes: Determine a theoretical reading of the sensor to be tested based on the first reading, the setting data of the at least one group of standard sensors, the position distribution data of the at least one sensor to be tested, and the target parameter; Based on the theoretical reading and the second reading, the reading accuracy is determined.

10. The system according to claim 9, characterized in that The step of determining a theoretical reading of the sensor to be tested based on the first reading, the setting data of the at least one group of standard sensors, the position distribution data of the at least one sensor to be tested, and the target parameter comprises: constructing a prediction map based on the first reading, the setting data of the at least one group of standard sensors, the position distribution data of the at least one group of sensors to be tested, and the target parameter; Based on the prediction map, the theoretical reading is determined by a reading prediction model, and the reading prediction model is a machine learning model.

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