CN117470194B - Inclination angle measurement method, inclination angle measurement system, storage medium and computer - Google Patents
Inclination angle measurement method, inclination angle measurement system, storage medium and computer Download PDFInfo
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Abstract
The invention provides a dip angle measuring method, a dip angle measuring system, a storage medium and a computer, wherein the dip angle measuring method comprises the following steps of; acquiring a temperature drift angle error of the inclination angle sensor, and constructing a temperature drift compensation model according to the Wen Piaojiao degree error; acquiring angle data and temperature data of an inclination sensor in real time; the angle data and the temperature data acquired in real time are imported into a temperature drift compensation model, and the generated temperature drift data of the inclination angle sensor at the current working temperature are obtained; and calculating actual angle data of the structure according to the angle data of the sensor and the temperature drift data generated at the current temperature, and taking the actual angle data as an inclination angle measurement result. The inclination angle measurement method provided by the invention can effectively reduce the influence of the ambient temperature on the test and improve the accuracy of inclination angle measurement.
Description
Technical Field
The invention relates to the technical field of structural safety monitoring, in particular to an inclination angle measuring method, an inclination angle measuring system, a storage medium and a computer.
Background
The safety of a structure refers to the ability of the structure to remain intact and substantially deformed under various loads during normal construction and normal use, while maintaining the necessary overall stability.
The inclination of the building is the deviation degree of the building in the vertical direction, is a common building structure problem, is an important index of whether the building is safe or not, and is possibly caused by the problems in the building design and construction process, and also is possibly caused by natural disasters such as foundation settlement, earthquake and the like; the inclination degree of the house is difficult to effectively judge through naked eye observation, so that the inclination state of a structure needs to be measured in real time through inclination measuring equipment, and potential safety hazards are found in time.
At present, the conventional structure inclination monitoring is mainly performed by adopting an inclination sensor, however, the internal circuit and elements of the conventional inclination sensor are affected by temperature change, the properties are changed, a temperature drift phenomenon is generated, and the measured angle value is inaccurate.
Disclosure of Invention
Based on this, the present invention aims to provide a tilt angle measurement method, a system, a storage medium and a computer, so as to solve the technical problems existing in the prior art.
The invention provides an inclination angle measuring method, which comprises the following steps of;
acquiring a temperature drift angle error of the inclination angle sensor, and constructing a temperature drift compensation model according to the Wen Piaojiao degree error;
acquiring angle data and temperature data of the inclination angle sensor in real time;
the angle data and the temperature data which are acquired in real time are imported into the temperature drift compensation model, and temperature drift data generated by the inclination angle sensor at the current working temperature are obtained;
calculating actual angle data of a structure according to the angle data of the sensor and the temperature drift data generated at the current temperature, and taking the actual angle data as an inclination angle measurement result;
the step of obtaining the temperature drift angle error of the inclination angle sensor comprises the following steps:
acquiring a working temperature range and a working angle range of the inclination sensor in a training set, and dividing the working temperature range and the working angle range according to a preset temperature gradient and a preset angle gradient respectively to obtain m temperature data and n angle data, wherein m and n are integers larger than 1;
measuring actual angle data of the inclination angle sensor at different temperatures and different angles to obtain m x n sample data;
selecting a standard temperature from m temperature data, and selecting n actual angle data of the inclination angle sensor at the standard temperature and using the n actual angle data as standard sample data;
and calculating the temperature drift angle error of the inclination sensor according to n standard sample data and m (n-1) other sample data.
Preferably, the working temperature range of the inclination sensor is-45-85 ℃, the working angle range of the inclination sensor is-90 degrees, the preset temperature gradient is 10 degrees, and the preset angle gradient is 10 degrees.
Preferably, the step of constructing the temperature drift compensation model includes:
selecting a BP neural network as a training model, wherein the training model comprises an input layer, a hidden layer and an output layer, and determining the node numbers of the input layer, the hidden layer and the output layer;
determining an initial weight and an initial threshold of the training model according to the input layer;
performing genetic coding on the initial weight and the initial threshold in the hidden layer, and performing iterative operation of a genetic algorithm;
judging whether the output result of the genetic algorithm meets the termination condition of the genetic algorithm, if so, decoding an optimal solution, and replacing the initial weight and the initial threshold with the optimal solution;
and training the replaced training model by using the Wen Piaojiao degree error, judging whether the result output by the output layer of the training model meets the termination condition of the neural network training, and stopping training if the result meets the termination condition of the neural network training to obtain the temperature drift compensation model.
Preferably, the determining the node numbers of the input layer, the hidden layer and the output layer includes:
the input layer comprises two nodes of a measurement angle and an ambient temperature of the inclination sensor;
the nodes of the hidden layer are determined according to the mean square error in the training set, wherein the hidden layer comprises eight nodes;
the output layer comprises a node for predicting the temperature drift angle error.
Preferably, the expression for determining whether the output result of the genetic algorithm meets the termination condition of the genetic algorithm is:
wherein F represents the output threshold of the genetic algorithm, N represents the number of populations in the genetic algorithm,indicating fitness value of each individual in the population, < ->Representing the average fitness value of the individuals in the population, H representing defined adjustment parameters;
if F is less than 0, the genetic population reaches a stable state and reaches the termination condition of the genetic algorithm.
Preferably, the training model after the replacing is trained by using the Wen Piaojiao degree error, and whether the result output by the output layer of the training model meets the termination condition of the neural network training is judged, if the result meets the termination condition of the neural network training, the training is stopped, and the step of obtaining the temperature drift compensation model is as follows:
inputting Wen Piaojiao degrees of error data in the Wen Piaojiao degrees of error into the replaced training model for model training, and judging whether the difference value between the predicted temperature drift angle error output by the training model and the input temperature drift angle error is within a preset range;
if the difference value is within the preset range, stopping training to obtain the temperature drift compensation model;
if the difference value is not in the preset range, updating the weight and the threshold value in the training model according to the predicted temperature drift angle error, selecting another Wen Piaojiao degree error, inputting the other Wen Piaojiao degree error into the updated training model, repeating model training, and repeating difference value judgment until the difference value is in the preset range.
The invention also provides an inclination angle measurement system, which comprises:
the construction module is used for acquiring the temperature drift angle error of the inclination angle sensor and constructing a temperature drift compensation model according to the Wen Piaojiao degree error;
the step of obtaining the temperature drift angle error of the inclination angle sensor comprises the following steps:
acquiring a working temperature range and a working angle range of the inclination sensor in a training set, and dividing the working temperature range and the working angle range according to a preset temperature gradient and a preset angle gradient respectively to obtain m temperature data and n angle data, wherein m and n are integers larger than 1;
measuring actual angle data of the inclination angle sensor at different temperatures and different angles to obtain m x n sample data;
selecting a standard temperature from m temperature data, and selecting n actual angle data of the inclination angle sensor at the standard temperature and using the n actual angle data as standard sample data;
calculating the temperature drift angle error of the inclination sensor according to n standard sample data and m (n-1) other sample data;
the acquisition module is used for acquiring the angle data and the temperature data of the inclination angle sensor in real time;
the importing module is used for importing the angle data and the temperature data acquired in real time into the temperature drift compensation model to obtain temperature drift data generated by the inclination angle sensor at the current working temperature;
and the calculation module is used for calculating actual angle data of the structure according to the angle data of the sensor and the temperature drift data generated at the current temperature, and taking the actual angle data as an inclination angle measurement result.
The present invention also proposes a storage medium having stored thereon a computer program which, when executed by a processor, implements the above-mentioned tilt angle measurement method.
The invention also provides a computer, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the inclination angle measuring method when executing the computer program.
Compared with the prior art, the invention has the beneficial effects that: according to the inclination angle measurement method, firstly, a temperature change test is conducted according to a preset training set according to the working temperature range and the working angle range of an inclination angle sensor, a temperature drift angle error of the inclination angle sensor is obtained, a temperature drift compensation model is built according to Wen Piaojiao degrees error, temperature data and angle data are fitted in the temperature drift compensation model, a compensation relation between the temperature data and the angle data in the temperature drift compensation model is calculated, a compensation coefficient k is obtained according to the compensation relation, and the temperature drift compensation model is written into a storage module of a system; the method comprises the steps of acquiring angle data and environment temperature data of the inclination sensor in real time, importing the acquired angle data and environment temperature data into a temperature drift compensation model, acquiring temperature drift data generated by the inclination sensor at the current working temperature, and acquiring actual inclination angle data of a structure by the angle data and the temperature drift data read by the inclination sensor, so that the influence of temperature drift on inclination measurement is reduced, and the accuracy of system inclination measurement is improved.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a flow chart of a tilt angle measurement method according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram showing the structure of a tilt angle measuring system according to a first embodiment of the present invention;
fig. 3 is a block diagram showing a structure of a computer according to a fourth embodiment of the present invention.
The invention will be further described in the following detailed description in conjunction with the above-described figures.
Description of main reference numerals:
101. a solar panel; 102. a high-low temperature test chamber; 103. a lithium battery; 104. a 5V reference power supply; 106. a storage module; 107. a micro controller; 108. an inclination sensor; 109. a 4G communication module; 110. a serial port; 111. and an independent power supply circuit.
Detailed Description
In order that the invention may be readily understood, a more complete description of the invention will be rendered by reference to the appended drawings. Several embodiments of the invention are presented in the figures. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Example 1
Referring to fig. 1, a method for measuring an inclination angle according to a first embodiment of the present invention specifically includes steps S10 to S40:
s10, obtaining a temperature drift angle error of an inclination angle sensor, and constructing a temperature drift compensation model according to the Wen Piaojiao degree error;
in specific implementation, as shown in fig. 2, the inclination sensor 108 may use an accelerometer device ADXL355 to measure an inclination, and a 5V reference power supply 104 is used to supply power to the inclination sensor, where the ADXL355 sensor and the 5V reference power supply are both low noise devices; the 5V reference power supply has the advantages that the ripple of the reference power supply output power supply is small, the power supply voltage is less affected by temperature, the whole system is charged by adopting the solar panel 101, the lithium battery 103 is arranged in the solar panel, the inclination angle measuring system is further provided with the micro controller 107, the temperature sensor 105, the 4G communication module 109, the storage module 106 and the serial port 110, wherein the micro controller 107 and the 4G communication module 109 adopt UART communication, and data, instructions and information transmission is realized. The microcontroller 107 is in data communication with the memory module 106 using an SPI interface. The micro controller 107 and the accelerometer device ADXL355 adopt IIC communication, the serial port 110 is used for configuration and upgrading ports of equipment, the ADXL355 sensor in the system adopts a 5V reference power supply to supply power, and other devices adopt independent power supply circuits 111, so that mutual interference of power supplies is avoided.
Before the measurement of the inclination angle of a structure, firstly, the temperature drift angle error of an inclination angle sensor needs to be acquired, wherein Wen Piaojiao degrees error is the angle deviation measured by the inclination angle sensor at different environmental temperatures, a temperature drift compensation model is constructed according to the acquired temperature drift angle error, and a compensation relation between the angle and the temperature is fitted in the temperature drift compensation model.
Optionally, in this embodiment, the step of obtaining the temperature drift angle error of the tilt sensor includes:
acquiring a working temperature range and a working angle range of the inclination sensor in a training set, and dividing the working temperature range and the working angle range according to a preset temperature gradient and a preset angle gradient respectively to obtain m temperature data and n angle data, wherein m and n are integers larger than 1;
in a specific implementation, in order to obtain the temperature drift angle error of the inclination sensor, the inclination sensor may be fixed on a rotating platform in advance and placed in the high-low temperature test chamber 102 to perform repeated temperature change test. The working temperature range of the inclination sensor is-45-85 ℃, the working angle range of the inclination sensor is-90 degrees, the horizontal angle is 0 degrees, the vertical upward angle is positive angle, the vertical downward angle is negative angle, the preset temperature gradient is 10 ℃, and the preset angle gradient is 10 degrees, namely in the embodiment, the working temperature is-45-85 ℃ and is divided according to the gradient of 10 ℃ to obtain 14 pieces of temperature data of-45 ℃, -35 ℃ … ℃ and 85 ℃, the working angle range of the inclination sensor is-90 degrees and is divided according to the gradient of 10 degrees to obtain 19 pieces of angle data of-90 ℃, -80 DEG … and 90 DEG.
Measuring actual angle data of the inclination angle sensor at different temperatures and different angles to obtain m x n sample data sets;
in the specific implementation, respectively taking the temperature value as the transverse direction and the angle value as the longitudinal direction to obtain a 14 x 19 two-dimensional table, respectively adopting an upper computer to capture actual angle data of the inclination angle sensor under different temperature and different angle combinations to obtain 14 x 19 sample data, and correspondingly filling the 14 x 19 actual angle data to be tested into the two-dimensional table; in the embodiment, the high-low temperature test chamber is adjusted to be minus 35 ℃, the angle of the inclination angle sensor is adjusted to be minus 80 degrees through the rotary platform, the actual angle value of the inclination angle sensor is captured to be minus 79.63 degrees through the upper computer, and minus 79.63 degrees are filled in the blank corresponding to the table 1; the high-low temperature test chamber 102 is adjusted to 75 ℃, the angle of the inclination sensor is adjusted to 90 degrees through the rotary platform, the actual angle value of the inclination sensor is captured by the upper computer to 90.31 degrees, 90.31 degrees are filled in the blank corresponding to the table 1, and other actual angle values of the inclination sensor are obtained similarly, and are not repeated here.
Table 1 actual angle values of the tilt sensor
Selecting a standard temperature from the m temperature data, and selecting actual angle data of n tilt sensors at the standard temperature as standard sample data;
in specific implementation, 25 ℃ is selected as a standard temperature, and correspondingly, the actual angle data of 19 inclination angle sensors, namely-90 DEG, -80 DEG … DEG and-90 DEG, at 25 ℃ are selected as standard sample data. It will be appreciated that due to unavoidable measurement errors, it is difficult to match the angle values of the captured tilt sensor with theoretical angle values, and in this embodiment, the actual angle values of the captured tilt sensor at 25 ℃ are defined as standard sample data, and the temperature drift angle errors at other temperatures are all based on the actual angle at that temperature. For example, the high-low temperature test chamber is adjusted to 25 ℃, the angle of the inclination sensor is adjusted to-80 degrees by the rotary platform, and standard sample data when the actual angle value of the inclination sensor is-79.98 degrees, namely-79.98 degrees is taken as 80 degrees is captured by the upper computer; the angle of the inclination angle sensor is adjusted to 90 degrees through the rotary platform, and the actual angle value of the inclination angle sensor is captured by the upper computer to be 90.03 degrees, namely 90.03 degrees is taken as standard sample data when 90 degrees.
And calculating the temperature drift angle error of the inclination sensor according to the n standard sample data and m (n-1) other sample data.
In specific implementation, calculating the temperature drift angle errors of the inclination angle sensor under different angles according to the obtained 19 standard sample data and the other 13 x 19 sample data, for example, the high and low temperature test box is-35 ℃, the angle of the inclination angle sensor is adjusted to be-80 degrees through the rotating platform, the actual angle value of the inclination angle sensor is captured to be-79.63 degrees through the upper computer, and the angle difference is 0.37 degrees; the high and low temperature test chamber 102 is adjusted to the standard temperature of 25 ℃, the angle of the inclination sensor is adjusted to-80 degrees by the rotary platform, the actual angle value of the inclination sensor is captured to be-79.98 degrees by the upper computer, and the angle difference is 0.02 degrees, so that the temperature drift angle error of the inclination sensor at-35 ℃ is 0.37-0.02=0.35 degrees in the embodiment. Similarly, the temperature drift angle errors at other temperatures when the angle of the inclination sensor is-80 degrees can be sequentially calculated, and the temperature drift angle errors of the inclination sensor, which are-80 degrees and are-45 degrees, -35 degrees, … degrees and 85 degrees, can be obtained. Similarly, the temperature drift angle errors at other angles are sequentially calculated to obtain 13×19 temperature drift angle errors.
The temperature drift compensation model building step comprises the following steps:
the BP neural network is selected as a training model, wherein the training model comprises an input layer, a hidden layer and an output layer, and the node number of the input layer, the hidden layer and the output layer is determined;
in the implementation, the input layer includes two factors of the measurement angle of the inclination sensor and the ambient temperature, the two input nodes are used as two input nodes of the BP neural network, the nodes of the hidden layer are determined according to the mean square error in the training set, and when the number of nodes of the hidden layer is 8, the mean square error of the BP neural network is minimum, so in the embodiment, the number of nodes of the hidden layer is 8, the output result of the BP neural network only includes the predicted temperature drift angle error, and therefore the output layer includes one node, and the BP neural network forms a 2-8-1 three-layer neural network model.
Determining an initial weight and an initial threshold of a training model according to an input layer;
in specific implementation, initial weights and initial thresholds are assigned to the training model according to the input layer and the 2-8-1 three-layer neural network model.
Performing genetic coding on the initial weight and the initial threshold value in the hidden layer, and performing iterative operation of a genetic algorithm;
the genetic algorithm is a meta heuristic algorithm and an evolutionary algorithm, and is sequentially subdivided into a population, a chromosome and a gene, wherein the genetic coding mode can be that the chromosome is represented by a numerical value, so that a computer can conveniently process the genetic algorithm, for example, binary coding, integer coding and the like can be adopted, and the genetic algorithm iterative computation is carried out after the genetic algorithm is coded.
Judging whether the output result of the genetic algorithm meets the termination condition of the genetic algorithm, if so, decoding an optimal solution, and replacing the initial weight and the initial threshold with the optimal solution;
in specific implementation, the expression for judging whether the output result of the genetic algorithm meets the termination condition of the genetic algorithm is as follows:
wherein F represents the output threshold of the genetic algorithm, N represents the number of populations in the genetic algorithm,indicating fitness value of each individual in the population, < ->Representing the average fitness value of the individuals in the population, H representing defined adjustment parameters;
if F is less than 0, the genetic population reaches a stable state and reaches the termination condition of the genetic algorithm.
The optimal solution is decoded through a genetic algorithm, and the weight and the threshold value of the training model are replaced by the optimal solution, so that the training model has stronger global searching capability and faster convergence speed, and the accuracy of the neural network compensation model is improved.
And training the replaced training model by using the Wen Piaojiao degree error, judging whether the result output by the output layer of the training model meets the termination condition of the neural network training, and stopping training if the result meets the termination condition of the neural network training to obtain the temperature drift compensation model.
In specific implementation, inputting Wen Piaojiao-degree error data of the Wen Piaojiao-degree error into the replaced training model for model training, and judging whether the difference value between the predicted temperature drift angle error output by the training model and the input temperature drift angle error is within a preset range;
if the difference value is within the preset range, stopping training to obtain the temperature drift compensation model;
if the difference value is not in the preset range, updating the weight and the threshold value in the training model according to the predicted temperature drift angle error, selecting another Wen Piaojiao degree error, inputting the other Wen Piaojiao degree error into the updated training model, repeating model training, and repeating difference value judgment until the difference value is in the preset range.
Optionally, fitting the angle-temperature relation in the temperature drift compensation model by adopting a least square method, calculating the compensation relation between the temperature data and the angle data in the temperature drift compensation model, acquiring a compensation coefficient k according to the compensation relation, and finally writing the temperature drift compensation model into the storage module.
S20, acquiring angle data and temperature data of the inclination sensor in real time;
the angle data of the inclination angle sensor can be read in real time through the ADXL355 sensor, and the ambient temperature data of the inclination angle sensor can be obtained in real time through the temperature sensor.
S30, importing the angle data and the temperature data acquired in real time into the temperature drift compensation model to obtain temperature drift data generated by the inclination angle sensor at the current working temperature;
s40, calculating actual angle data of the structure according to the angle data of the sensor and the temperature drift data generated at the current temperature, and taking the actual angle data as an inclination angle measurement result.
In particular, after determining the compensation coefficient k in the temperature drift compensation modelEstablishing a relationship between the ambient temperature and the compensation angle, preferably defining the data of the ambient temperature asThe angle data read by the inclination sensor is defined as θThe ambient temperature at the time of starting up is defined as +.> ，Temperature change amount->The method comprises the steps of carrying out a first treatment on the surface of the In combination with the compensation factor, the generated temperature drift data of the tilt sensor at the current operating temperature is known +.>The method comprises the steps of carrying out a first treatment on the surface of the Actual inclination angle data of the structure +.>θ/>Thereby reducing temperature effects.
In summary, according to the inclination angle measurement method provided by the application, firstly, a temperature change test is performed according to a preset training set according to the working temperature range and the working angle range of an inclination angle sensor, a temperature drift angle error of the inclination angle sensor is obtained, a temperature drift compensation model is built according to Wen Piaojiao degrees error, temperature data and angle data are fitted in the temperature drift compensation model, a compensation relation between the temperature data and the angle data in the temperature drift compensation model is calculated, a compensation coefficient k is obtained according to the compensation relation, and the temperature drift compensation model is written into a storage module of a system; the method comprises the steps of acquiring angle data and environment temperature data of the inclination sensor in real time, importing the acquired angle data and environment temperature data into a temperature drift compensation model, acquiring temperature drift data generated by the inclination sensor at the current working temperature, and acquiring actual inclination angle data of a structure by the angle data and the temperature drift data read by the inclination sensor, so that the influence of temperature drift on inclination measurement is reduced, and the accuracy of system inclination measurement is improved.
Example two
The present embodiment provides an inclination angle measurement system, including:
the construction module is used for acquiring the temperature drift angle error of the inclination angle sensor and constructing a temperature drift compensation model according to the Wen Piaojiao degree error;
the construction module comprises:
the dividing unit is used for acquiring the working temperature range and the working angle range of the inclination sensor in the training set, and dividing the working temperature range and the working angle range according to a preset temperature gradient and a preset angle gradient respectively to obtain m temperature data and n angle data, wherein m and n are integers larger than 1; the working temperature range of the inclination angle sensor is-45-85 ℃, the working angle range of the inclination angle sensor is-90 degrees, the preset temperature gradient is 10 ℃, and the preset angle gradient is 10 degrees.
The measuring unit is used for measuring actual angle data of the inclination angle sensor at different temperatures and different angles to obtain m-n sample data;
the selection unit is used for selecting a standard temperature from m temperature data, and selecting n actual angle data of the inclination angle sensor at the standard temperature and the n actual angle data as standard sample data;
and a calculation unit for calculating the temperature drift angle error of the inclination sensor according to the n standard sample data and the m (n-1) other sample data.
The device comprises a selecting unit, a selecting unit and a processing unit, wherein the selecting unit is used for selecting a BP neural network as a training model, the training model comprises an input layer, a hidden layer and an output layer, and the node numbers of the input layer, the hidden layer and the output layer are determined; the input layer comprises two nodes, namely a measurement angle of the inclination sensor and an ambient temperature;
the nodes of the hidden layer are determined according to the mean square error in the training set, wherein the hidden layer comprises eight nodes;
the output layer comprises a node for predicting the temperature drift angle error.
The determining unit is used for determining an initial weight and an initial threshold of the training model according to the input layer;
the execution unit is used for carrying out genetic coding on the initial weight and the initial threshold value in the hidden layer and executing iterative operation of a genetic algorithm;
the first judging unit is used for judging whether the output result of the genetic algorithm meets the termination condition of the genetic algorithm, if so, decoding an optimal solution, and replacing the initial weight and the initial threshold value with the optimal solution;
wherein, the expression for judging whether the output result of the genetic algorithm meets the termination condition of the genetic algorithm is as follows:
wherein F represents the output threshold of the genetic algorithm, N represents the number of populations in the genetic algorithm,indicating fitness value of each individual in the population, < ->Representing the average fitness value of the individuals in the population, H representing defined adjustment parameters;
if F is less than 0, the genetic population reaches a stable state and reaches the termination condition of the genetic algorithm.
And the second judging unit is used for training the replaced training model by using the Wen Piaojiao-degree error, judging whether the result output by the output layer of the training model meets the termination condition of the neural network training, and stopping training if the result meets the termination condition of the neural network training, so as to obtain the temperature drift compensation model.
The step of training the replaced training model by using the Wen Piaojiao degree error, judging whether the result output by the output layer of the training model meets the termination condition of the neural network training, and stopping training if the result meets the termination condition of the neural network training, wherein the step of obtaining the temperature drift compensation model comprises the following steps of:
inputting Wen Piaojiao degrees of error data in the Wen Piaojiao degrees of error into the replaced training model for model training, and judging whether the difference value between the predicted temperature drift angle error output by the training model and the input temperature drift angle error is within a preset range;
if the difference value is within the preset range, stopping training to obtain the temperature drift compensation model;
if the difference value is not in the preset range, updating the weight and the threshold value in the training model according to the predicted temperature drift angle error, selecting another Wen Piaojiao degree error, inputting the other Wen Piaojiao degree error into the updated training model, repeating model training, and repeating difference value judgment until the difference value is in the preset range.
The acquisition module is used for acquiring the angle data and the temperature data of the inclination angle sensor in real time;
the importing module is used for importing the angle data and the temperature data acquired in real time into the temperature drift compensation model to obtain temperature drift data generated by the inclination angle sensor at the current working temperature;
and the calculation module is used for calculating actual angle data of the structure according to the angle data of the sensor and the temperature drift data generated at the current temperature, and taking the actual angle data as an inclination angle measurement result.
Example III
The present embodiment proposes a storage medium having stored thereon a computer program which, when executed by a processor, implements the tilt angle measurement method as described above.
Example IV
The present invention also proposes a computer, referring to fig. 3, which shows a computer according to a fourth embodiment of the present invention, including a memory 10, a processor 20, and a computer program 30 stored in the memory 10 and executable on the processor 20, wherein the processor 20 implements the above-mentioned inclination measurement method when executing the computer program 30.
The memory 10 includes at least one type of storage medium including flash memory, a hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. Memory 10 may in some embodiments be an internal storage unit of a computer, such as a hard disk of the computer. The memory 10 may also be an external storage device in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), etc. Further, the memory 10 may also include both internal storage units and external storage devices of the computer. The memory 10 may be used not only for storing application software installed in a computer and various types of data, but also for temporarily storing data that has been output or is to be output.
The processor 20 may be, in some embodiments, an electronic control unit (Electronic Control Unit, ECU), a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, a microprocessor, or other data processing chip, for executing program codes or processing data stored in the memory 10, such as executing an access restriction program, or the like.
It should be noted that the structure shown in fig. 3 does not constitute a limitation of a computer, and in other embodiments, the computer may include fewer or more components than shown, or may combine certain components, or may have a different arrangement of components.
Those of skill in the art will appreciate that the logic and/or steps represented in the flow diagrams or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (7)
1. A method of measuring tilt angle, comprising;
acquiring a temperature drift angle error of the inclination angle sensor, and constructing a temperature drift compensation model according to the Wen Piaojiao degree error;
acquiring angle data and temperature data of the inclination angle sensor in real time;
the angle data and the temperature data which are acquired in real time are imported into the temperature drift compensation model, and temperature drift data generated by the inclination angle sensor at the current working temperature are obtained;
calculating actual angle data of a structure according to the angle data of the sensor and the temperature drift data generated at the current temperature, and taking the actual angle data as an inclination angle measurement result;
the step of obtaining the temperature drift angle error of the inclination angle sensor comprises the following steps:
acquiring a working temperature range and a working angle range of the inclination sensor in a training set, and dividing the working temperature range and the working angle range according to a preset temperature gradient and a preset angle gradient respectively to obtain m temperature data and n angle data, wherein m and n are integers larger than 1;
measuring actual angle data of the inclination angle sensor at different temperatures and different angles to obtain m x n sample data;
selecting a standard temperature from m temperature data, and selecting n actual angle data of the inclination angle sensor at the standard temperature and using the n actual angle data as standard sample data;
calculating the temperature drift angle error of the inclination sensor according to n standard sample data and m (n-1) other sample data;
the step of constructing the temperature drift compensation model comprises the following steps:
selecting a BP neural network as a training model, wherein the training model comprises an input layer, a hidden layer and an output layer, and determining the node numbers of the input layer, the hidden layer and the output layer;
determining an initial weight and an initial threshold of the training model according to the input layer;
performing genetic coding on the initial weight and the initial threshold in the hidden layer, and performing iterative operation of a genetic algorithm;
judging whether the output result of the genetic algorithm meets the termination condition of the genetic algorithm, if so, decoding an optimal solution, and replacing the initial weight and the initial threshold with the optimal solution;
training the replaced training model by using the Wen Piaojiao degree error, judging whether the result output by an output layer of the training model meets the termination condition of the neural network training, and stopping training if the result meets the termination condition of the neural network training to obtain the temperature drift compensation model;
the expression for judging whether the output result of the genetic algorithm meets the termination condition of the genetic algorithm is as follows:
wherein F represents the output threshold of the genetic algorithm, N represents the number of populations in the genetic algorithm,indicating fitness value of each individual in the population, < ->Representing the average fitness value of the individuals in the population, H representing defined adjustment parameters;
if F is less than 0, the genetic population reaches a stable state and reaches the termination condition of the genetic algorithm.
2. The tilt angle measurement method according to claim 1, wherein the tilt angle sensor has an operating temperature range of-45 ° -85 ℃, an operating angle range of-90 ° -90 °, the preset temperature gradient is 10 ℃, and the preset angle gradient is 10 °.
3. The tilt angle measurement method of claim 1, wherein determining the number of nodes of the input layer, the hidden layer, and the output layer comprises:
the input layer comprises two nodes of a measurement angle and an ambient temperature of the inclination sensor;
the nodes of the hidden layer are determined according to the mean square error in the training set, wherein the hidden layer comprises eight nodes;
the output layer comprises a node for predicting the temperature drift angle error.
4. The inclination angle measurement method of claim 3 wherein the step of training the replaced training model using the Wen Piaojiao degree error, determining whether the result output by the output layer of the training model meets the termination condition of the neural network training, stopping training if the result meets the termination condition of the neural network training, and obtaining the temperature drift compensation model comprises the steps of:
inputting Wen Piaojiao degrees of error data in the Wen Piaojiao degrees of error into the replaced training model for model training, and judging whether the difference value between the predicted temperature drift angle error output by the training model and the input temperature drift angle error is within a preset range;
if the difference value is within the preset range, stopping training to obtain the temperature drift compensation model;
if the difference value is not in the preset range, updating the weight and the threshold value in the training model according to the predicted temperature drift angle error, selecting another Wen Piaojiao degree error, inputting the other Wen Piaojiao degree error into the updated training model, repeating model training, and repeating difference value judgment until the difference value is in the preset range.
5. A tilt angle measurement system, comprising;
the construction module is used for acquiring the temperature drift angle error of the inclination angle sensor and constructing a temperature drift compensation model according to the Wen Piaojiao degree error;
the step of obtaining the temperature drift angle error of the inclination angle sensor comprises the following steps:
acquiring a working temperature range and a working angle range of the inclination sensor in a training set, and dividing the working temperature range and the working angle range according to a preset temperature gradient and a preset angle gradient respectively to obtain m temperature data and n angle data, wherein m and n are integers larger than 1;
measuring actual angle data of the inclination angle sensor at different temperatures and different angles to obtain m x n sample data;
selecting a standard temperature from m temperature data, and selecting n actual angle data of the inclination angle sensor at the standard temperature and using the n actual angle data as standard sample data;
calculating the temperature drift angle error of the inclination sensor according to n standard sample data and m (n-1) other sample data;
the acquisition module is used for acquiring the angle data and the temperature data of the inclination angle sensor in real time;
the importing module is used for importing the angle data and the temperature data acquired in real time into the temperature drift compensation model to obtain temperature drift data generated by the inclination angle sensor at the current working temperature;
the calculation module is used for calculating actual angle data of the structure according to the angle data of the sensor and the temperature drift data generated at the current temperature, and taking the actual angle data as an inclination angle measurement result; the step of constructing the temperature drift compensation model comprises the following steps:
selecting a BP neural network as a training model, wherein the training model comprises an input layer, a hidden layer and an output layer, and determining the node numbers of the input layer, the hidden layer and the output layer;
determining an initial weight and an initial threshold of the training model according to the input layer;
performing genetic coding on the initial weight and the initial threshold in the hidden layer, and performing iterative operation of a genetic algorithm;
judging whether the output result of the genetic algorithm meets the termination condition of the genetic algorithm, if so, decoding an optimal solution, and replacing the initial weight and the initial threshold with the optimal solution;
training the replaced training model by using the Wen Piaojiao degree error, judging whether the result output by an output layer of the training model meets the termination condition of the neural network training, and stopping training if the result meets the termination condition of the neural network training to obtain the temperature drift compensation model;
the expression for judging whether the output result of the genetic algorithm meets the termination condition of the genetic algorithm is as follows:
wherein F represents the output threshold of the genetic algorithm, N represents the number of populations in the genetic algorithm,indicating fitness value of each individual in the population, < ->Representing the average fitness value of the individuals in the population, H representing defined adjustment parameters;
if F is less than 0, the genetic population reaches a stable state and reaches the termination condition of the genetic algorithm.
6. A storage medium having stored thereon a computer program, which when executed by a processor implements the tilt angle measurement method according to any of claims 1 to 4.
7. A computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the inclination measuring method according to any of claims 1 to 4 when executing the computer program.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009025188A (en) * | 2007-07-20 | 2009-02-05 | Fujikura Ltd | Temperature compensation method of physical quantity and temperature compensation type optical fiber sensor |
| CN102353356A (en) * | 2011-07-14 | 2012-02-15 | 上海辉格科技发展有限公司 | Digitalized clinometer and measuring method thereof |
| CN103593538A (en) * | 2013-11-28 | 2014-02-19 | 东南大学 | Fiber optic gyroscope temperature drift modeling method by optimizing dynamic recurrent neural network through genetic algorithm |
| CN104697497A (en) * | 2015-02-28 | 2015-06-10 | 湖北三江航天红峰控制有限公司 | Digital tilt sensor and temperature nonlinear compensation method thereof |
| CN109724539A (en) * | 2018-12-27 | 2019-05-07 | 西南技术物理研究所 | A kind of strain-type angular transducer temperature drift zero compensation method |
| CN109993088A (en) * | 2019-03-22 | 2019-07-09 | 江南大学 | A kind of blind calibration method of wireless sensor network data wander |
| CN110388897A (en) * | 2018-04-19 | 2019-10-29 | 郑州航天电子技术有限公司 | A kind of wide warm high-precision tilt angle sensor and its precision compensation method |
| CN111521155A (en) * | 2020-06-29 | 2020-08-11 | 宁波中车时代传感技术有限公司 | Method for acquiring inclination angle of small intelligent inclination angle sensor |
| CN115014289A (en) * | 2022-08-05 | 2022-09-06 | 长江水利委员会长江科学院 | Data processing method and circuit of fixed inclinometer |
| CN115342963A (en) * | 2022-07-29 | 2022-11-15 | 北京空间飞行器总体设计部 | Temperature drift compensation method for joint force sensor of mechanical arm of space station |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10317421B2 (en) * | 2014-03-31 | 2019-06-11 | Stmicroelectronics S.R.L | Positioning apparatus comprising an inertial sensor and inertial sensor temperature compensation method |
| JP2021167736A (en) * | 2020-04-09 | 2021-10-21 | 株式会社トプコン | Inclination sensor and data acquisition device |
| US20230126732A1 (en) * | 2020-04-15 | 2023-04-27 | A&D Company, Limited | Inclination detecting method and apparatus for the same |
-
2023
- 2023-12-28 CN CN202311824732.8A patent/CN117470194B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009025188A (en) * | 2007-07-20 | 2009-02-05 | Fujikura Ltd | Temperature compensation method of physical quantity and temperature compensation type optical fiber sensor |
| CN102353356A (en) * | 2011-07-14 | 2012-02-15 | 上海辉格科技发展有限公司 | Digitalized clinometer and measuring method thereof |
| CN103593538A (en) * | 2013-11-28 | 2014-02-19 | 东南大学 | Fiber optic gyroscope temperature drift modeling method by optimizing dynamic recurrent neural network through genetic algorithm |
| CN104697497A (en) * | 2015-02-28 | 2015-06-10 | 湖北三江航天红峰控制有限公司 | Digital tilt sensor and temperature nonlinear compensation method thereof |
| CN110388897A (en) * | 2018-04-19 | 2019-10-29 | 郑州航天电子技术有限公司 | A kind of wide warm high-precision tilt angle sensor and its precision compensation method |
| CN109724539A (en) * | 2018-12-27 | 2019-05-07 | 西南技术物理研究所 | A kind of strain-type angular transducer temperature drift zero compensation method |
| CN109993088A (en) * | 2019-03-22 | 2019-07-09 | 江南大学 | A kind of blind calibration method of wireless sensor network data wander |
| CN111521155A (en) * | 2020-06-29 | 2020-08-11 | 宁波中车时代传感技术有限公司 | Method for acquiring inclination angle of small intelligent inclination angle sensor |
| CN115342963A (en) * | 2022-07-29 | 2022-11-15 | 北京空间飞行器总体设计部 | Temperature drift compensation method for joint force sensor of mechanical arm of space station |
| CN115014289A (en) * | 2022-08-05 | 2022-09-06 | 长江水利委员会长江科学院 | Data processing method and circuit of fixed inclinometer |
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