Sensor temperature compensation method and device

Abstract

A sensor temperature compensation method comprises the following steps that 1, real-time environment temperature data are obtained, whether the environment temperature is within the working temperature of a built-in sensor or not is judged, if the environment temperature is within the working temperature, the step 2 is executed, and if the environment temperature is not within the working temperature, an abnormal prompt is output; step 2, acquiring a real-time output signal of the built-in sensor, determining an output signal compensation amount through a preset temperature compensation curve, and performing temperature compensation; and 3, after a set time interval, returning to the step 1 to obtain the environment temperature again for judgment. The invention further provides a sensor temperature compensation device. According to the invention, the output signal compensation amount is determined through the preset temperature compensation curve, and temperature compensation is carried out, so that the stability and the measurement precision of the built-in sensor under the temperature change condition are remarkably improved.

Description

Technical Field

[0001] The invention belongs to a steel bar corrosion monitoring technology in a construction project, and in particular relates to a temperature compensation method and a device for a built-in steel bar corrosion nondestructive monitoring sensor based on an electromagnetic field principle. Background Art

[0002] The necessity of long-life research on concrete lies in its crucial impact on the durability and stability of concrete structures in extreme environments. With the advancement of the national key research and development plan "Preparation and Application Technology of Long-Life Concrete in Extreme Environments", research in this field is particularly urgent. The project is committed to solving the most basic stability, damage mechanism of the microstructure of hydration products and repair mechanism of concrete in extreme environments, with innovative theories, concrete durability, breaking technology and key technologies, to form a system to build materials for the preparation and application of high-performance concrete.

[0003] In the study of the long life of concrete, the issue of steel bar corrosion occupies a core position, and its importance is self-evident. Steel bar corrosion will weaken the bearing capacity of steel bars and damage the bond between steel bars and concrete, which will affect the stability and safety of the entire structure. Especially under extreme conditions such as earthquakes, the degradation of the bonding performance of corroded steel bars will directly affect the bearing capacity and stiffness of the structure. Therefore, in-depth research on the bonding performance of corroded steel bars is crucial to ensure the safety and durability of the structure. In addition, this research can also bring significant economic benefits, reduce maintenance costs, extend the service life of structures, and help promote the sustainable development of the construction industry, fully reflecting the attitude of being responsible to the public.

[0004] Not only does the long life of concrete in extreme environments such as chloride erosion need to be studied, but also for China, a country obsessed with infrastructure construction, a large number of completed buildings also need to study steel corrosion. At present, the monitoring methods for steel corrosion are mainly divided into two categories: damage detection and non-destructive testing. Although damage detection can provide more accurate measurement results, it requires destructive sampling of reinforced concrete structures, and the damage to the structure is permanent. Therefore, it is not suitable for reinforced concrete structures in use. On the contrary, non-destructive testing technology has become the focus of research because it does not require the destruction of the structure, including half-cell potential method, acoustic emission technology and built-in monitoring technology. These nondestructive testing methods can qualitatively judge the probability of steel bar corrosion through the potential changes or acoustic emission parameters caused by electrochemical reactions, but they have limitations in accuracy and cannot quantitatively measure the degree of steel bar corrosion, and there is currently no unified judgment standard; patents have been published for nondestructive testing methods for steel bar corrosion based on the principle of magnetic field, such as China Patent Publication No. CN 110646505 A, published on January 3, 2020, entitled "External nondestructive monitoring sensor and test method for steel bar corrosion based on electromagnetic field principle" and China Patent Publication No. CN 113125551 A, published on July 16, 2021, entitled "A built-in magnetic sensor with four measuring points and its test method". Currently, related patents such as the nondestructive testing method for steel bar corrosion based on the principle of magnetic field provide a steel bar corrosion monitoring device built into concrete, which is used to monitor the corrosion of steel bars in concrete. It has high accuracy under constant temperature conditions, but has poor robustness to high temperature fluctuations in actual engineering. Summary of the invention

[0005] In order to overcome the shortcomings of the existing technology and to improve the environmental adaptability of a series of non-destructive testing technologies based on the magnetic induction principle, such as a built-in steel corrosion monitoring device, the present invention provides a sensor temperature compensation method and device. The temperature compensation method determines the output signal compensation amount through a preset temperature compensation curve and performs temperature compensation.

[0006] The technical solution adopted by the present invention to solve its technical problem is:

[0007] A sensor temperature compensation method comprises the following steps:

[0008] Step 1, obtain real-time ambient temperature data, and determine whether the ambient temperature is within the working temperature of the built-in sensor. If it is within the working temperature, proceed to step 2. If it is not within the working temperature, output an abnormal prompt;

[0009] Step 2, obtaining the real-time output signal of the built-in sensor, determining the output signal compensation amount through a preset temperature compensation curve, and performing temperature compensation;

[0010] Step 3: After an interval of 0.5 seconds, return to step 1 to re-obtain the ambient temperature for judgment.

[0011] Furthermore, in step 2, the process of presetting the temperature compensation curve is: using a temperature curve calibration device to calibrate the original sensor, by simulating the ambient temperature data within the working temperature range, obtaining the corresponding original signal of the built-in sensor, and generating an associated data set based on the ambient temperature data and the original signal of the built-in sensor as two variables in the coordinate axis, thereby obtaining a temperature compensation curve within the working temperature range.

[0012] A sensor temperature compensation device comprises a built-in sensor and a temperature curve calibration device, wherein the temperature curve calibration device comprises a temperature difference maintaining container, a temperature detection device and a constant temperature control device, wherein the temperature detection device is located in a medium of the temperature difference maintaining container, and the constant temperature control device is used to receive the temperature of the temperature detection device to adjust and maintain the temperature of the medium in the container, wherein the built-in sensor is placed as a whole in the temperature curve calibration device to simulate the working temperature range and obtain a corresponding temperature compensation curve.

[0013] The built-in sensor includes a built-in sensor module for exciting the Hall element by a magnetic circuit, a data reading module for converting a voltage signal into a digital signal, a data transmission module for transmitting data, and a data processing module for preset curve compensation;

[0014] The built-in sensor module includes a sensor permanent magnet, a sensor left and right magnetic core, a sensor left and right Hall sensors, a sensor temperature sensor and a sensor packaging shell. The sensor packaging shell is generally made of corrosion-resistant material die-casting, and has grooves reserved for placing various components. The sensor permanent magnet and the sensor left and right magnetic cores are connected as a whole through magnetic adsorption and placed in the reserved grooves; the sensor left and right Hall sensors and the sensor temperature sensor are welded on the PCB board and connected as a whole, and placed in the reserved grooves;

[0015] The sensor permanent magnet is the source of the stable magnetic field, the left and right magnetic cores of the sensor are tools for closing the stable magnetic field, the left and right Hall sensors of the sensor are tools for reading magnetic field data, the sensor temperature sensor is a tool for reading temperature data, and the sensor packaging shell is a tool for ensuring that the steel bar to be detected is in the correct position.

[0016] The data reading module includes a signal collector and a central controller. After the signal collector is powered on, it can read the data of the left and right Hall sensors and the temperature sensor of the sensor. The central controller makes a preliminary judgment on the read signal and generates a set interval signal to upload to the data transmission module.

[0017] The data transmission module has two modes: wired and wireless. The wired data transmission module is directly connected to the data processing module, and the wireless data transmission module uploads data to the private cloud for storage. The data processing module reads data from the private cloud.

[0018] The data processing module controls the data reading module to judge and feedback the read data, and compensates the original data through the temperature compensation curve.

[0019] The built-in sensor gives raw data after the following calibration process, and the calibration process is as follows:

[0020] 2.1.1. Manufacture and connect the built-in sensor module, data reading module, data transmission module and data processing module to form the sensor as a whole;

[0021] 2.1.2. Prepare ribbed steel bars of set length and diameter as calibration steel bars. The first group m 1-0 ~m 1-10 There are 11 steel bars of the same length, in five groups, namely m 1-0 ~m 1-10 To m 5-0 ~m 5-10 A total of 55 steel bars;

[0022] 2.1.3. Corrode the calibration steel bars according to the steel bar corrosion Tesla formula and steel bar corrosion rate formula;

[0023] Tesla formula Δm = MIt / zF, where Δm is the steel consumption in grams, M is the relative atomic mass, Fe is 55.8, I is the current in amperes, t is the time in seconds, z is the ion charge, Fe is 2, F: Faraday constant, value 96485A / s;

[0024] The steel bar corrosion rate formula is μ = Δm / m, where μ is the corrosion rate, Δm is the steel consumption in grams, and m is the steel mass;

[0025] The corrosion process is as follows: coating both ends of the steel bar with epoxy resin to cover both ends; selecting a container and preparing a 5% sodium chloride solution, placing the steel bar in the 5% sodium chloride solution; connecting the positive electrode to the steel bar and the negative electrode to the water surface; and according to the steel bar corrosion Tesla formula and the steel bar corrosion rate formula, each group of steel bars m 1-0 ~m 1-10 The theoretical corrosion rate is 0% to 10% respectively, and then the rusted calibration steel bar is derusted with a rust remover, weighed and the actual corrosion rate is obtained according to the steel bar corrosion rate formula. There are five groups of μ 1-0 ~μ 1-10 , and finally treated with rust inhibitor to stabilize the corrosion rate of the steel bars;

[0026] 2.1.4. In a 25℃ constant temperature laboratory, use the built-in sensor module to measure a total of five groups of m 1-0 ~m 1-10 The magnetic induction data of the steel bar is calibrated and five groups of B are obtained in the data processing module. 1-0 ~B 1-10 Magnetic induction data, based on the two variables μ and B, a total of five μ-B graphs are obtained. The least squares method is used to fit the curve to determine the linearity of all variables in the μ-B graph. The fitting curves of the five μ-B graphs should be straight lines with similar data. Finally, the fitting curves of the five μ-B graphs are averaged to obtain the μ-B 锈蚀率标定曲线(25℃) ;

[0027] 2.2.5. The calibration curve indicates the magnetic induction of the steel bar with a corrosion rate of 0% to 10%. The corrosion rate of the steel bar to be tested is reflected according to the calibration curve and the original magnetic induction data reflected by the steel bar to be tested, that is, the original corrosion rate data of the built-in sensor is obtained, thus completing the detection process of the built-in sensor based on the non-destructive testing technology based on the magnetic induction principle.

[0028] The calibration process of the temperature compensation curve is as follows:

[0029] 2.2.1. Manufacture and connect the built-in sensor module, data reading module, data transmission module and data processing module to form the sensor as a whole;

[0030] 2.2.2. Prepare three groups of samples, each with five samples. One group is the sensor permanent magnets N1 to N5, the second group is the sensor permanent magnets and the left and right magnetic cores NS1 to NS5, and the third group is the sensor as a whole MCD1 to MCD5. There are 15 samples in total in three groups.

[0031] 2.2.3. All samples were placed in the temperature curve calibration device, and the data were measured every 5°C from -20°C to 60°C by freezing the water area and heating it in a water bath. Groups 1 and 2 measured the original magnetic induction data through the Tesla meter, and group 3 measured the original magnetic induction data through the data processing module. According to the two variables T and B, a total of three groups of five TB graphs in each group were obtained. The least squares method was used for curve fitting to determine the linearity of all variables in the TB graph. The fitting curves of the five TB graphs in each of the three groups should all be straight lines, and the fitting curves of the five TB graphs in each group should have similar data. Finally, the fitting curves of the five μ-B graphs in each group were averaged to obtain three types of TB. 标定曲线 , that is, TB 标定曲线1(磁铁) TB 标定曲线2(磁铁+磁芯) TB 标定曲线3(传感器) The three calibration curves represent the effect of temperature on magnetism. From components to the whole, the three calibration curves conform to the laws of physics, that is, the measured data are qualified, so TB 标定曲线3(传感器) The final TB 温度补偿曲线(-20℃～60℃) .

[0032] The temperature compensation process is as follows: temperature compensation is performed in the data processing module, and the following is automatically executed by the computer: the actual working temperature T of the sensor is read 实际 , T 实际 -T 25℃ =ΔT, at TB 温度补偿曲线(-20℃～60℃) According to the actual temperature, B 实际 , (B 实际 -B 25℃ ) / B 25℃ =ΔB ％ , B 补偿 =B 实际 ×(1+ΔB ％ ), output B 补偿 The compensated magnetic induction data of the built-in sensor is used to reflect the corrosion rate of the steel bar to be tested based on the calibration curve and the original magnetic induction data reflected by the steel bar to be tested, that is, the compensated corrosion rate data of the built-in sensor is obtained.

[0033] The technical concept of the present invention is that the sensitivity of the nondestructive detection sensor for steel corrosion based on the magnetic field principle, i.e., the built-in sensor, is easily affected by the fluctuation of the ambient temperature, which affects the normal function of detecting the change of the corrosion rate and reduces its accuracy. To this end, after real-time monitoring of the ambient temperature data, the present invention uses a preset temperature compensation curve to calculate the compensation amount of the output signal and performs basic temperature compensation, so that the compensation value can be dynamically calculated for different ambient temperatures, effectively improving the accuracy and stability of the built-in sensor under various temperature conditions. At the same time, the temperature data and output signal of the built-in sensor are monitored in real time, and the output signal is adjusted in real time to ensure that the built-in sensor can still operate stably in a complex environment, further enhancing the accuracy of the corrosion rate detection. Through the above technical solution, the built-in sensor is subjected to real-time temperature compensation, its performance is optimized, its environmental adaptability is enhanced, and the reliability and practicality of the detector are increased.

[0034] The beneficial effects of the present invention are mainly manifested in:

[0035] 1. The present invention collects the response data of the corresponding built-in sensors by accurately simulating the ambient temperature under actual working conditions, and establishes a detailed correlation data set between the ambient temperature and the sensor output signal. The construction of this data set, based on the accumulation of a large amount of experimental data, ensures the data accuracy and reliability of the temperature compensation curve, and significantly improves the stability and measurement accuracy of the built-in sensor under temperature change conditions.

[0036] 2. The present invention further analyzes the output signal changes of each sensor component and the sensor as a whole under different ambient temperatures, and constructs a data set of changes from local to overall. This data set enables us to gain a deeper understanding of the mechanism by which the accuracy of built-in sensors is affected at different ambient temperatures. This mechanism shows the specific process of the gradual attenuation of temperature effects from local to overall, so that the performance of sensors can be optimized for different working conditions and different working condition components. It is not limited to built-in sensors. The magnetic field-based non-destructive detection sensors for steel corrosion (built-in, external, split) can all be optimized according to this temperature compensation method.

[0037] 3. The present invention obtains a comprehensive temperature compensation curve by establishing a data set of temperature-affected parameter changes of multiple sets of built-in sensors. At the same time, by comparing multiple sets of parameters, the feasibility of the temperature compensation curve and temperature compensation algorithm being widely used in the same type of built-in sensors is proved. This helps the large-scale industrial production and large-scale practical application of sensors. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is the flow chart of temperature compensation algorithm.

[0039] Figure 2 It is a schematic diagram of the built-in sensor structure.

[0040] Figure 3 It is a schematic diagram of the structure of the temperature curve calibration device.

[0041] Figure 4 It is a fluctuation chart of sensor data in a certain season.

[0042] Figure 5 It is the sensor calibration curve graph.

[0043] Figure 6 It is the sensor temperature compensation curve. DETAILED DESCRIPTION

[0044] The present invention will be further described below in conjunction with the accompanying drawings.

[0045] Reference Figure 1 to Figure 6 , a sensor temperature compensation method, comprising the following steps:

[0046] Step 1, obtain real-time ambient temperature data, and determine whether the ambient temperature is within the working temperature of the built-in sensor. If it is within the working temperature, proceed to step 2. If it is not within the working temperature, output an abnormal prompt;

[0047] Step 2, obtaining the real-time output signal of the built-in sensor, determining the output signal compensation amount through a preset temperature compensation curve, and performing temperature compensation;

[0048] Step 3: After an interval of 0.5 seconds, return to step 1 to re-obtain the ambient temperature for judgment.

[0049] Furthermore, in step 2, the process of presetting the temperature compensation curve is: using a temperature curve calibration device to calibrate the original sensor, by simulating the ambient temperature data within the working temperature range, obtaining the corresponding original signal of the built-in sensor, and generating an associated data set based on the ambient temperature data and the original signal of the built-in sensor as two variables in the coordinate axis, thereby obtaining a temperature compensation curve within the working temperature range.

[0050] A sensor temperature compensation device comprises a built-in sensor and a temperature curve calibration device, wherein the temperature curve calibration device comprises a temperature difference maintaining container, a temperature detection device and a constant temperature control device, wherein the temperature detection device is located in a medium of the temperature difference maintaining container, and the constant temperature control device is used to receive the temperature of the temperature detection device to adjust and maintain the temperature of the medium in the container, wherein the built-in sensor is placed as a whole in the temperature curve calibration device to simulate the working temperature range and obtain a corresponding temperature compensation curve.

[0051] The shell of the temperature difference maintaining container is made of thermal insulation and sealing material, the upper cover can be kept open, and a medium with a large specific heat capacity (generally water) is filled inside. The temperature is maintained by reducing heat conduction and heat radiation to reduce the natural dissipation of heat; the temperature detection device is generally a thermometer in the form of a thermocouple, which is suspended and fixed in the medium in the container and is responsible for detecting the temperature of the medium in the container; the constant temperature control device can be a semiconductor heating refrigeration plate or a resistance wire heater, which is responsible for adjusting and maintaining the temperature of the medium in the container.

[0052] The built-in sensor includes a built-in sensor module for exciting the Hall element by a magnetic circuit, a data reading module for converting a voltage signal into a digital signal, a data transmission module for transmitting data, and a data processing module for preset curve compensation;

[0053] All modules have interfaces for data transmission: the built-in sensor module and the data reading module are connected via a data cable; the data reading module and the data transmission module are connected via a data cable; the data processing module can be in the form of a personal computer or a cloud host, the data processing module and the personal computer are connected via a data cable, and the data processing module and the cloud host are connected via a 4G wireless signal.

[0054] The built-in sensor module includes a sensor permanent magnet 1, a sensor left and right magnetic core 2, a sensor left and right Hall sensors 4, a sensor temperature sensor 5 and a sensor packaging shell (not shown); the sensor packaging shell is generally made of corrosion-resistant material die-casting, and has grooves reserved for placing various components. The sensor permanent magnet and the sensor left and right magnetic cores are connected as a whole through magnetic adsorption and placed in the reserved grooves; the sensor left and right Hall sensors and the sensor temperature sensor are welded on the PCB board and connected as a whole, and placed in the reserved grooves.

[0055] The sensor permanent magnet 1 is the source of the stable magnetic field, the sensor left and right magnetic cores 2 are tools for closing the stable magnetic field, the sensor left and right Hall sensors 4 are tools for reading magnetic field data, the sensor temperature sensor 5 is a tool for reading temperature data, and the sensor packaging shell is a tool for ensuring that the steel bars to be detected are in the correct position.

[0056] The production process of the built-in sensor module is as follows:

[0057] The first step is to connect the sensor permanent magnet 1 and the left and right magnetic cores 2 of the sensor. The two parts are automatically attracted by magnetism. After the connection is completed, the two parts are placed in a sensor packaging shell of a good size.

[0058] The second step is to weld the left and right Hall sensors 4 and the temperature sensor 5 to the built-in PCB board of the sensor (not shown), put the welded built-in PCB board of the sensor into the sensor packaging shell of a good size, continue to weld the interface connecting wires, and pass the connecting wires through the reserved opening of the sensor packaging shell.

[0059] The third step is to connect the data reading module to preliminarily determine whether the built-in sensor is poorly soldered. After the data test is completed, the packaging work is carried out.

[0060] The fourth step is to pour epoxy resin through the glue filling port on the sensor packaging shell. After standing for 24 hours, connect the data reading module again to test the data. After the test is completed, the sensor is completed.

[0061] The data reading module includes a signal collector and a central controller. The data reading module is packaged separately by several chips, PCBs, and interfaces and can be produced separately. After the signal collector is powered, it can read the relevant data of the left and right Hall sensors 4 and the temperature sensor 5. The central controller makes a preliminary judgment on the read signal and generates a set interval signal to upload to the data processing module.

[0062] The production process of the data reading module is as follows:

[0063] The first step is to preliminarily program the central controller through programming software. The central controller has a pre-soldered mature product development board, which can be programmed to achieve the set goals.

[0064] The second step is to weld several chips such as the central controller and signal collector to the PCB board, install the PCB board to the data reading module housing, continue to weld the interface connection wires, and pass the connection wires through the reserved opening of the data reading module housing.

[0065] The third step is to buckle the data reading module shell without glue filling to complete the data reading module production.

[0066] The data transmission module has two modes: wired and wireless. The wired data transmission module is directly connected to the data processing module. The wireless data transmission module uploads the interval signal to the private cloud storage, and the data processing module reads data from the private cloud.

[0067] The data processing module controls the data reading module to judge and feedback the read data, and compensates the original data through the temperature compensation curve.

[0068] The built-in sensor gives raw data after the following calibration process, and the calibration process is as follows:

[0069] 2.1.1. Manufacture and connect the built-in sensor module, data reading module, data transmission module and data processing module to form the sensor as a whole;

[0070] 2.1.2. Prepare 10CM long ribbed steel bars with set diameter as calibration steel bars. The first group of m 1-0 ~m 1-10 There are 11 steel bars of the same length, in five groups, and m 1-0 ~m 1-10 To m 5-0 ~m 5-10 A total of 55 steel bars;

[0071] 2.1.3. Corrode the calibrated steel bars according to the steel bar corrosion Tesla formula (Δm=MIt / zF) and the steel bar corrosion rate formula (μ=Δm / m);

[0072] The 10CM ribbed steel bar with a set diameter weighs about 230g, and the mass loss of 1% corrosion rate is 2.3g. According to the Tesla formula for steel bar corrosion, 0.3 amperes of current is passed for a total of 7.5 hours, and the steel bar corrosion mass is 2.3422g, reaching the theoretical corrosion rate of 1.0183%, 2.1094%, 3.0986%, 4.1144%, 5.1302%, 6.1460%, 7.1618%, 8.1776%, 9.1934%, and 10.2092%. The theoretical corrosion rate is obtained, and then actual corrosion is carried out. The corrosion process is as follows: apply epoxy resin to both ends of the steel bar to cover both ends; select a suitable container and prepare a 5% sodium chloride solution, and place the steel bar in the 5% sodium chloride solution; connect the positive electrode to the steel bar and the negative electrode to the water surface. According to the Tesla formula for steel bar corrosion and the steel bar corrosion rate formula, each group of steel bars m 1-0 ~m 1-10 The theoretical corrosion rates of 0% to 10% were reached respectively. The rusted calibration steel bars were then derusted with a rust remover, weighed, and the actual corrosion rates of 1.0480%, 2.1684%, 3.0612%, 4.2730%, 5.3784%, 6.3776%, 7.5680%, 8.5672%, 9.5238%, and 10.4379% were obtained according to the steel bar corrosion rate formula. The above is one of the data sets, with a total of five sets of μ 1-0 ~μ 1-10 , and finally treated with rust inhibitor to stabilize the corrosion rate of the steel bars.

[0073] 2.1.4. In a 25℃ constant temperature laboratory, use the built-in sensor module to measure a total of five groups of m 1-0 ~m 1-10 The magnetic induction data of the steel bar is calibrated and five groups of B are obtained in the data processing module. 1-0 ~B 1-10 Magnetic induction data, based on the two variables μ and B, a total of five μ-B graphs are obtained. The least squares method is used to fit the curve to determine the linearity of all variables in the μ-B graph. The fitting curves of the five μ-B graphs should be straight lines with similar data. Finally, the fitting curves of the five μ-B graphs are averaged to obtain the μ-B 锈蚀率标定曲线(25℃) ,like Figure 5 shown.

[0074] 2.1.5. The calibration curve represents the magnetic induction of the steel bar with a corrosion rate of 0% to 10%. According to the calibration curve and the original magnetic induction data reflected by the steel bar to be tested, the corrosion rate of the steel bar to be tested can be reflected, that is, the original corrosion rate data of the built-in sensor is obtained, and the detection process of the built-in sensor based on the non-destructive testing technology based on the magnetic induction principle is completed.

[0075] In this embodiment, the sensor temperature compensation method is calibrated by a specific process to give a temperature compensation curve, and the process is as follows:

[0076] 2.2.1. Manufacture and connect the built-in sensor module, data reading module, data transmission module and data processing module to form the sensor as a whole;

[0077] 2.2.2. Prepare three groups of samples, each with five samples. One group is the sensor permanent magnet 1, namely N1~N5, the second group is the sensor permanent magnet 1 + the sensor left and right magnetic cores 2, namely NS1~NS5, and the third group is the sensor as a whole, namely MCD1~MCD5. There are 15 samples in total in three groups.

[0078] 2.2.3. All samples were placed in the temperature curve calibration device, and the data were measured every 5°C from -20°C to 60°C by freezing the water area and heating it in a water bath (the original magnetic induction data of Groups 1 and 2 were measured by the Tesla meter, and the original magnetic induction data of Group 3 was measured by the data processing module). According to the two variables T and B, a total of three groups of five TB graphs in each group were obtained. The least squares method was used for curve fitting to determine the linearity of all variables in the TB graph. The fitting curves of the five TB graphs in each of the three groups should all be straight lines, and the fitting curves of the five TB graphs in each group should have similar data. Finally, the fitting curves of the five μ-B graphs in each group were averaged to obtain three types of TB. 标定曲线 , that is, TB 标定曲线1(磁铁) TB 标定曲线2(磁铁+磁芯) TB 标定曲线3(传感器) TB 标定曲线1(磁铁) It shows that the magnetism of the magnet decays with temperature, and some data of one group are: 3.29%, 3.50%, 4.12%, 4.53%, 5.35%, 5.76%, 5.97%, 6.79%, 8.02%, 8.23%. TB 标定曲线2(磁铁+磁芯) It shows that the magnetism of the magnet decays with temperature, and some data of one group are: 4.07%, 5.56%, 7.04%, 8.15%, 8.89%, 9.26%, 9.63%, 10.74%, 12.22%, 13.70%. TB 标定曲线3(传感器) It shows that the magnetism of the magnet decays with temperature. Some data of one group are: 0.28%, 0.40%, 0.62%, 0.87%, 1.00%, 1.29%, 2.42%, 3.73%, 5.08%, 5.11%. The three calibration curves represent the effect of temperature on magnetism. From the components to the whole, the three calibration curves conform to the laws of physics, that is, the measured data is qualified, so TB 标定曲线3(传感器) The final TB 温度补偿曲线(-20℃～60℃) ,like Figure 6 shown.

[0079] 2.2.4. Temperature compensation is performed in the data processing module. The following is automatically performed by the computer: Read the actual working temperature T of the sensor 实际 , T 实际-T 25℃ =ΔT, at TB 温度补偿曲线(-20℃～60℃) According to the actual temperature, B 实际 , (B 实际 -B 25℃ ) / B 25℃ =ΔB ％ , B 补偿 =B 实际 ×(1+ΔB ％ ), output B 补偿 The compensated magnetic induction data of the built-in sensor can be used to reflect the corrosion rate of the steel bar to be tested based on the calibration curve and the original magnetic induction data reflected by the steel bar to be tested, that is, the compensated corrosion rate data of the built-in sensor is obtained.

[0080] Comparative Example 1: The daily monitoring fluctuation of the sensor in a certain season. The sensor will have a large data fluctuation under the influence of temperature. Now the sensor data is selected for verification in the actual detection. The steps are as follows:

[0081] The first step is to take five groups of sensors that are working properly and place them in a daily use environment. No steel bars are placed in the monitoring area to prevent changes in the steel bar corrosion rate from affecting the sensor data.

[0082] The second step is to read the sensor data continuously at similar times on different dates. The fluctuation data of one group is as follows: -2.5710%, -0.8094%, -4.5053%, -3.8452%, 5.4904%, 4.7777%, 4.6071%, 4.3107%, 3.9404%, 3.6409%. The fluctuation data of the five groups of sensors should be similar. After verification, the five groups of sensor data are averaged to obtain the fluctuation graph of sensor data in a certain season, such as Figure 4 As shown in the figure, the temperature data is different from the actual temperature caused by the sun shining on the ground, and there are accurate data on the parameters of existing sensors with temperature, so this data chart is used as a reference and auxiliary verification.

[0083] The temperature compensation scheme of this embodiment can improve the availability of related non-destructive detection sensors for steel corrosion based on the magnetic field principle, improve the environmental adaptability of the sensor, enhance the practical application and large-scale production capabilities of non-destructive detection sensors for steel corrosion, and provide assistance for non-destructive detection of old buildings, bridges and other buildings, and new concrete buildings in harsh environments. It can reduce the damage to buildings, pollution to the environment, and increase in related costs caused by destructive testing.

[0084] The contents described in the embodiments of this specification are merely enumerations of implementation forms of the inventive concept and are for illustrative purposes only. The protection scope of the present invention should not be considered to be limited to the specific forms described in this embodiment, and the protection scope of the present invention also extends to equivalent technical means that can be thought of by ordinary technicians in this field based on the inventive concept.

Claims

1. A sensor temperature compensation method, characterized in that: The method comprises the following steps: Step 1, obtain real-time ambient temperature data, and determine whether the ambient temperature is within the working temperature of the built-in sensor. If it is within the working temperature, proceed to step 2. If it is not within the working temperature, output an abnormal prompt; Step 2, obtaining the real-time output signal of the built-in sensor, determining the output signal compensation amount through a preset temperature compensation curve, and performing temperature compensation; Step 3: After the interval setting time, return to step 1 to re-obtain the ambient temperature for judgment.

2. A sensor temperature compensation method as claimed in claim 1, characterized in that: In step 2, the process of presetting the temperature compensation curve is: calibrating the original sensor using a temperature curve calibration device, obtaining the corresponding original signal of the built-in sensor by simulating the ambient temperature data within the working temperature range, and generating an associated data set based on the ambient temperature data and the original signal of the built-in sensor as two variables in the coordinate axis, thereby obtaining a temperature compensation curve within the working temperature range.

3. A device for implementing the sensor temperature compensation method according to claim 1, characterized in that: The device includes a built-in sensor and a temperature curve calibration device. The temperature curve calibration device includes a temperature difference maintaining container, a temperature detection device and a constant temperature control device. The temperature detection device is located in the medium of the temperature difference maintaining container. The constant temperature control device is used to receive the temperature of the temperature detection device to adjust and maintain the temperature of the medium in the container. The built-in sensor is placed as a whole in the temperature curve calibration device to simulate the working temperature range to obtain a corresponding temperature compensation curve.

4. The device according to claim 3, characterized in that The built-in sensor includes a built-in sensor module for exciting the Hall element by a magnetic circuit, a data reading module for converting a voltage signal into a digital signal, a data transmission module for transmitting data, and a data processing module for preset curve compensation; The built-in sensor module includes a sensor packaging shell and a sensor permanent magnet, a sensor left and right magnetic core, a sensor left and right Hall sensors, and a sensor temperature sensor installed in the shell, wherein the sensor permanent magnet and the sensor left and right magnetic cores are connected as a whole through magnetic adsorption, and the sensor left and right Hall sensors and the sensor temperature sensor are welded on a PCB board and connected as a whole; The data reading module includes a signal collector and a central controller. After the signal collector is powered on, it can read the data of the left and right Hall sensors and the temperature sensor of the sensor. The central controller makes a preliminary judgment on the read signal and generates a set interval signal to upload to the data transmission module. The data transmission module has two modes: wired and wireless. The wired data transmission module is directly connected to the data processing module. The wireless data transmission module uploads data to the private cloud for storage, and the data processing module reads data from the private cloud. The data processing module controls the data reading module to judge and feedback the read data, and compensates the original data through the temperature compensation curve.

5. The device according to claim 4, characterized in that The built-in sensor gives raw data after the following calibration process, and the calibration process is as follows: 2.1.

1. Manufacture and connect the built-in sensor module, data reading module, data transmission module and data processing module to form the sensor as a whole; 2.1.

2. Prepare ribbed steel bars of set length and diameter as calibration steel bars. The first group m 1-0 ~m 1-10 There are 11 steel bars of the same length, in five groups, namely m 1-0 ~m 1-10 To m 5-0 ~m 5-10 A total of 55 steel bars; 2.1.

3. Corrode the calibration steel bars according to the steel bar corrosion Tesla formula and steel bar corrosion rate formula; Tesla formula Δm=MIt / zF, where Δm is the steel consumption in grams, M is the relative atomic mass, which is 55.8 for Fe, I is the current in amperes, t is the time in seconds, z is the ion charge, which is 2 for Fe, and F is the Faraday constant, which is 96485 A / s; The steel bar corrosion rate formula is μ = Δm / m, where μ is the corrosion rate, Δm is the steel consumption in grams, and m is the steel mass; The corrosion process is as follows: coating both ends of the steel bar with epoxy resin to cover both ends; selecting a container and preparing a 5% sodium chloride solution, placing the steel bar in the 5% sodium chloride solution; connecting the positive electrode to the steel bar and the negative electrode to the water surface; and according to the steel bar corrosion Tesla formula and the steel bar corrosion rate formula, each group of steel bars m 1-0 ~m 1-10 The theoretical corrosion rate is 0% to 10% respectively, and then the rusted calibration steel bar is derusted with a rust remover, weighed and the actual corrosion rate is obtained according to the steel bar corrosion rate formula. There are five groups of μ 1-0 ~μ 1-10 , and finally treated with rust inhibitor to stabilize the corrosion rate of the steel bars; 2.1.

4. In a 25℃ constant temperature laboratory, use the built-in sensor module to measure a total of five groups of m 1-0 ~m 1-10 The magnetic induction data of the steel bar is calibrated and five groups of B are obtained in the data processing module. 1-0 ~B 1-10 Magnetic induction data, based on the two variables μ and B, a total of five μ-B graphs are obtained. The least squares method is used to fit the curve to determine the linearity of all variables in the μ-B graph. The fitting curves of the five μ-B graphs should be straight lines with similar data. Finally, the fitting curves of the five μ-B graphs are averaged to obtain the μ-B 锈蚀率标定曲线(25℃) ; 2.2.

5. The calibration curve indicates the magnetic induction of the steel bar with a corrosion rate of 0% to 10%. The corrosion rate of the steel bar to be tested is reflected according to the calibration curve and the original magnetic induction data reflected by the steel bar to be tested, that is, the original corrosion rate data of the built-in sensor is obtained, thus completing the detection process of the built-in sensor based on the non-destructive testing technology based on the magnetic induction principle.

6. The device according to claim 4, characterized in that The calibration process of the temperature compensation curve is as follows: 2.2.

1. Manufacture and connect the built-in sensor module, data reading module, data transmission module and data processing module to form the sensor as a whole; 2.2.

2. Prepare three groups of samples, each with five samples. One group is the sensor permanent magnets N1 to N5, the second group is the sensor permanent magnets and the left and right magnetic cores NS1 to NS5, and the third group is the sensor as a whole MCD1 to MCD5. There are 15 samples in total in three groups. 2.2.

3. All samples were placed in the temperature curve calibration device, and the data were measured every 5℃ from -20℃ to 60℃ by freezing the water area and heating it in a water bath. The first and second groups measured the original magnetic induction data through the Tesla meter, and the third group measured the original magnetic induction data through the data processing module. According to the two variables T and B, a total of three groups of five TB graphs were obtained in each group. The least squares method was used to fit the curve to determine the linearity of all variables in the TB graph. The fitting curves of the five TB graphs in each group of three groups should all be straight lines, and the fitting curves of the five TB graphs in each group should have similar data. Finally, the fitting curves of the five μ-B graphs in each group were averaged to obtain three types of TB. 标定曲线 , that is, TB 标定曲线1(磁铁) TB 标定曲线2(磁铁+磁芯) TB 标定曲线3(传感器) The three calibration curves represent the effect of temperature on magnetism. From components to the whole, the three calibration curves conform to the laws of physics, that is, the measured data are qualified, so TB 标定曲线3(传感器) The final TB 温度补偿曲线(-20℃～60℃) . The temperature compensation process is as follows: temperature compensation is performed in the data processing module, and the following is automatically executed by the computer: the actual working temperature T of the sensor is read 实际 , T 实际 -T 25℃ =ΔT, at TB 温度补偿曲线(-20℃～60℃) According to the actual temperature, B 实际 , (B 实际 -B 25℃ ) / B 25℃ =ΔB ％ , B 补偿 =B 实际 ×(1+ΔB ％ ), output B 补偿 The compensated magnetic induction data of the built-in sensor is used to reflect the corrosion rate of the steel bar to be tested based on the calibration curve and the original magnetic induction data reflected by the steel bar to be tested, that is, the compensated corrosion rate data of the built-in sensor is obtained.

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