CN116192912A - Distributed wireless dynamic strain acquisition method, system, device and medium - Google Patents
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Abstract
The invention discloses a distributed wireless dynamic strain acquisition method, which comprises the following steps: the receiving module continuously collects and acquires stress information; converting the acquired stress information into an electrical signal; inputting the electric signals to the matched interfaces according to the judgment of the main control module; transmitting the electric signals received by each interface to the cloud and storing the electric signals in a self-set TF card, so that lossless transmission of information is realized, and the acquisition work of different types of sensors is satisfied; the distributed wireless dynamic strain acquisition method provided by the invention reduces the power consumption in the network and uses an access port; the measurement precision, stability and reliability are ensured; the main control chip can fully exert the maximum performance of each module of the system, and no performance bottleneck exists; the conditioning signals of the sensors of different types are switched to the AD front end, so that the collection work of the sensors of different types can be met through a software configuration mode instead of a mode of replacing collection cards of different types.
Description
Technical Field
The invention relates to the technical field of strain acquisition, in particular to a distributed wireless dynamic strain acquisition method, a system, a device and a medium.
Background
With the development of computer and information technology, all instruments in the technology are developed towards digitization, intellectualization, networking, microminiaturization and portability.
The digitization and intelligence degree of most test measurement devices reach a certain level, and the strain measurement devices are also the same. The traditional strain acquisition system and the strain gauge and the display recorder are strictly matched due to the data transmission problem, so that the debugging and the use are troublesome, the measured data cannot be further analyzed, processed and stored, the function is single, the expansion is difficult, and various different requirements of the strain acquisition system caused by the diversity of strain measurement contents are difficult to meet. The current international research development trend of the strain gauge is as follows:
the sensor is intelligent, automatically reads the acquired data without identifying channel numbers and sensitivity coefficients, reduces the recorded information quantity of field test, has less preparation work and has high sensitivity and test precision.
The hardware integration of the device, the recording, the filtering and the amplifying of the data are all completed by a microprocessor, and the digital-analog conversion and the balance component are specially designed and highly integrated in a single chip.
The multifunctional equipment can be compatible with different types of sensors, such as strain sensors, accelerometers, displacement meters, inclinometers, load meters and the like, and can meet the measurement of different mechanical parameters. Meanwhile, the system can be used for static test and dynamic test.
And the data transmission is wireless, and the standard 802.11b/g broadband wireless communication protocol is adopted to carry out data transmission and acquisition control. Because of adopting the wireless mode, the system omits the problems of complex cable connection and on-site power supply searching, and is a practical system especially suitable for on-site work.
The software is multifunctional, the acquisition software adopts an interactive design, and the operation is easy. The analysis software adopts a comprehensive structure assessment method, namely strain data acquired on site is used as a basis for calibrating a finite element analysis model, and the current bearing capacity of the structure is assessed more accurately and rapidly on the basis of the obtained accurate calculation model.
The continuous data recording adopts the functions of continuous measurement and synchronous transmission and recording of large data volume, expands the information volume, not only provides the behavior information of the whole structure, but also improves the reliability of the measured data and the scientificity and accuracy of evaluation. And carrying out data comparison on the field test data and a calculated theoretical value of the finite element analysis model, further correcting the theoretical analysis model and load verification by means of parameter identification and model calibration, and finally evaluating the bearing capacity of the structure on the calibrated analysis model.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: the existing acquisition method has the problems that the CPU performance cannot be fully exerted to handle the good data processing, algorithm calculation and communication encryption tasks, data loss and how to meet the acquisition work of different types of sensors without changing different types of acquisition cards.
In order to solve the technical problems, the invention provides the following technical scheme: a distributed wireless dynamic strain acquisition method comprising:
the receiving module continuously collects and acquires stress information;
converting the acquired stress information into an electrical signal;
inputting the electric signals to the matched interfaces according to the judgment of the main control module;
and transmitting the electric signals received by each interface to the cloud and storing the electric signals in a self-set TF card, so that the lossless transmission of information is realized, and the acquisition work of different types of sensors is satisfied.
The distributed wireless dynamic strain acquisition method provided by the invention is characterized in that: the stress information is converted into an electrical signal, comprising: the mechanical signals received by the receptor are converted into electric signals, so that the functions of transmission and storage are achieved.
The distributed wireless dynamic strain acquisition method provided by the invention is characterized in that: the judging of the main control module comprises the following steps:
converting the stress range of the test object into an electric signal range, wherein omega is a sample point set, and the distance d (x, y) is a function of omega×Ω→r+, and the conditions are satisfied:
d(x,y)≥0,x,y∈Ω；
d (x, y) =0 if and only if x=y
d(x,y)＝d(y,x),x,y∈Ω；
d(x,y)≤d(x,z)+d(x,y),x,y,z∈Ω.
Where d (x, y) represents the distance between two sample points, referred to herein as the range of the electrical signal, and x, y, z represents the different points.
The distributed wireless dynamic strain acquisition method provided by the invention is characterized in that: the Minkowski distance is expressed as:
wherein d represents distance, x and y are values of two signals, q is an adjustment parameter, p represents a sample point and p variables are needed to be described, and k represents a variable.
The distributed wireless dynamic strain acquisition method provided by the invention is characterized in that: the judging of the main control module further comprises the following steps:
according to the range of the electric signals, the electric signals are divided into 8 clustering signals, the analog electronic switching technology is adopted, the conditioning signals of different types of sensors are switched to the AD front end, and different input ends are respectively injected.
The distributed wireless dynamic strain acquisition method provided by the invention is characterized in that: the electrical signal is input to a matched interface, comprising:
if the electric signal is not matched with the voltage range of the access port, judging that the electric signal is abnormal, and carrying out input judgment on the electric signal again;
when judging that the input port is the other input port, recording the current abnormality as an identification abnormality, and executing a re-judging access port;
when judging that the access port is not yet available, recording the abnormality as an operation abnormality after analyzing the error reason, and executing signal input of a source path;
if the electric signal is excessively abnormal, a characteristic signal is given when the electric signal is input, and the identified abnormal characteristic is reflected when the electric signal is stored and uploaded.
The distributed wireless dynamic strain acquisition method provided by the invention is characterized in that: the transmitting the electric signals received by the interfaces to the cloud end comprises the following steps:
if an abnormal signal is found, carrying out classification early warning according to abnormal characteristics in the uploading process;
if the abnormal signal is analyzed to be a device fault, a maintenance message is given in the uploading process, and the fault position is positioned according to the distributed structure, so that the rapid maintenance of the fault is realized;
and correcting the abnormal information stored in the TF card after the abnormality is relieved, and keeping a modification record for the cloud information.
A distributed wireless dynamic strain acquisition system, comprising:
the receiving module is used for receiving the strain condition of the detected unit and connecting the sensed information to the sensing module;
the sensing module converts the strain of the measured object into a proper signal by utilizing the sensing element, and distributes the signal to 8 channels according to the instruction of the control module;
the control module is used for controlling the timing sequence IO acquired by the high-speed AD and controlling the switching of different types of sensor conditioning signals to the front end of the AD;
and the conduction module is used for transmitting the information to the cloud and storing the information in the TF card.
A computer device, comprising: a memory and a processor; the memory stores a computer program characterized in that: the processor, when executing the computer program, implements the steps of the method of any of the present invention.
A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program, when executed by a processor, implements the steps of the method of any of the present invention.
The invention has the beneficial effects that: the distributed wireless dynamic strain acquisition method provided by the invention reduces the power consumption in the network and uses an access port; the measurement precision, stability and reliability are ensured; the main control chip can fully exert the maximum performance of each module of the system, and no performance bottleneck exists; the conditioning signals of the sensors of different types are switched to the AD front end, so that the collection work of the sensors of different types can be met through a software configuration mode instead of a mode of replacing collection cards of different types.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is an overall flowchart of a distributed wireless dynamic strain acquisition method according to a first embodiment of the present invention;
fig. 2 is a cluster scatter diagram in a distributed wireless dynamic strain acquisition method according to a second embodiment of the present invention;
fig. 3 is a clustering method diagram of a distributed wireless dynamic strain acquisition method according to a second embodiment of the present invention;
FIG. 4 is a hardware block diagram of a strain acquisition system according to a second embodiment of the present invention;
fig. 5 is an internal structural diagram of a computer device according to a second embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present invention have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1, for one embodiment of the present invention, a distributed wireless dynamic strain acquisition method is provided, including:
s1: the receiving module continuously collects and acquires stress information;
further, the acquired stress information is converted into an electrical signal.
It should be noted that the stress signal which cannot be transmitted is converted into a voltage signal which can be recorded and transmitted by the device built in the susceptor.
S2: inputting the electric signals to the matched interfaces according to the judgment of the main control module;
converting the stress range of the test object into an electric signal range, wherein omega is a sample point set, and the distance d (x, y) is a function of omega×Ω→r+, and the conditions are satisfied:
d(x,y)≥0,x,y∈Ω；
d (x, y) =0 if and only if x=y
d(x,y)＝d(y,x),x,y∈Ω；
d(x,y)≤d(x,z)+d(x,y),x,y,z∈Ω.
Where d (x, y) represents the distance between two sample points, referred to herein as the range of the electrical signal, and x, y, z represents the different points.
The Minkowski distance is expressed as:
wherein d represents distance, x and y are values of two signals, q is an adjustment parameter, p represents a sample point and p variables are needed to be described, and k represents a variable.
When q=1, 2 or q →+++ infinity in the time-course of which the first and second contact surfaces, the following steps are respectively obtained:
chebyshev distance
Among the Minkowski distances, the euclidean distance is most commonly used, and its main advantage is that the euclidean distance remains the same when the coordinate axes are orthogonally rotated. Therefore, if the translation and rotation transformation is performed on the original coordinate system, the distance between the sample points after the transformation is identical to that before the transformation. It is noted that when using the Minkowski distance, the same dimensional variables must be used. If the dimensions of the variables are different, and the measured value variation ranges are greatly different, it is recommended to perform data normalization firstly and then calculate the distance. Multiple correlations of variables should also be avoided as much as possible when using the Minkowski distance. The overlapping of information due to multiple correlations may emphasize the importance of certain variables on one hand. Because of these drawbacks of the Minkowski distance, one improved distance is the Mahalanobis distance, defined as follows:
where x, y is the sample observations from the p-dimensional ensemble Z, Σ is the covariance matrix of Z, where Σ is often unknown in practice and often needs to be estimated with sample covariance. The mahalanobis distance is constant for all linear transformations and is therefore not affected by dimension. In addition, sample correlation coefficients, angle cosine and other relevance metrics can also be used as similarity metrics. With the deep research of data mining in recent years, a new method for the aspect is endless.
Defining a voltage range for each interface according to the voltage difference as a distance in cluster analysis; ensure that the electric signal can not be produced too big or too little, ensure the stability of electric signal.
It should be noted that: the judging of the main control module further comprises the following steps: according to the range of the electric signals, the electric signals are divided into 8 clustering signals, the analog electronic switching technology is adopted, the conditioning signals of different types of sensors are switched to the AD front end, and different input ends are respectively injected.
It should be appreciated that there are also: if the electric signal is not matched with the voltage range of the access port, judging that the electric signal is abnormal, and carrying out input judgment on the electric signal again;
when judging that the input port is the other input port, recording the current abnormality as an identification abnormality, and executing a re-judging access port;
when judging that the access port is not yet available, recording the abnormality as an operation abnormality after analyzing the error reason, and executing signal input of a source path;
if the electric signal is excessively abnormal, a characteristic signal is given when the electric signal is input, and the identified abnormal characteristic is reflected when the electric signal is stored and uploaded.
S3: and transmitting the electric signals received by the interfaces to the cloud and storing the electric signals in a self-set TF card.
It should be noted that if an abnormal signal is found, a class early warning is performed according to the abnormal characteristics in the uploading process; if the abnormal signal is analyzed to be a device fault, a maintenance message is given in the uploading process, and the fault position is positioned according to the distributed structure, so that the rapid maintenance of the fault is realized; and correcting the abnormal information stored in the TF card after the abnormality is relieved, and keeping a modification record for the cloud information.
The present embodiment also provides a computing device comprising, a memory and a processor; the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions to realize the motor rotor position compensation method based on the neural network in the stealth environment as proposed in the embodiment.
The nondestructive transmission of information is realized, and the acquisition work of different types of sensors is satisfied.
The present embodiment also provides a storage medium having stored thereon a computer program which, when executed by a processor, implements a method of compensating for a motor rotor position based on a neural network as proposed in the above embodiments.
The storage medium proposed in this embodiment belongs to the same inventive concept as the method for implementing compensation of the motor rotor position based on the neural network in the stealth environment proposed in the above embodiment, and technical details not described in detail in this embodiment can be seen in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile memory may include read only memory, magnetic tape, floppy disk, flash memory, optical memory, high density embedded nonvolatile memory, resistive memory, magnetic memory, ferroelectric memory, phase change memory, graphene memory, and the like. Volatile memory can include random access memory, external cache memory, or the like. By way of illustration, and not limitation, RAM can take many forms, such as static random access memory or dynamic random access memory. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
Example 2
Referring to fig. 2-4, a distributed wireless dynamic strain acquisition method is provided for one embodiment of the present invention, and in order to verify the beneficial effects of the present invention, scientific demonstration is performed through economic benefit calculation and simulation experiments.
Table 1 shows the comparison of the invention with the strain collection at home and abroad:
table 1 comparison of Strain acquisition System at home and abroad and key parameters of the project
Setting the voltage range of the electric signal converted by the acquisition device to 7-23V;
the voltage range of the 8 input interfaces is then: 7-9;9-11;11-13;13-15;15-17;17-19;19-21;21-23; the voltage range is stabilized by the regulation of the analog signal amplifier.
When the detection position senses the stress of 8N and 16N, the voltage is converted into 8V and 16V.
The electric signal realizes that each channel independently collects various types of analog signals through an analog switch technology: and by adopting an analog electronic switch technology, the conditioning signals of the sensors of different types are switched to the AD front end, so that the acquisition work of the sensors of different types can be met through a software configuration mode instead of a mode of replacing acquisition cards of different types, and the acquisition work of the sensors of different types is respectively accessed into the first channel and the seventh channel.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. The distributed wireless dynamic strain acquisition method is characterized by comprising the following steps of:
the receiving module continuously collects and acquires stress information;
converting the acquired stress information into an electrical signal;
inputting the electric signals to the matched interfaces according to the judgment of the main control module;
and transmitting the electric signals received by each interface to the cloud and storing the electric signals in a self-set TF card, so that the lossless transmission of information is realized, and the acquisition work of different types of sensors is satisfied.
2. The distributed wireless dynamic strain acquisition method of claim 1, wherein: the stress information is converted into an electrical signal, comprising: the mechanical signals received by the receptor are converted into electric signals, so that the functions of transmission and storage are achieved.
3. The distributed wireless dynamic strain acquisition method of claim 1 or 2, wherein: the judging of the main control module comprises the following steps:
converting the stress range of the test object into an electric signal range, wherein omega is a sample point set, and the distance d (x, y) is a function of omega×Ω→r+, and the conditions are satisfied:
d(x,y)≥0,x,y∈Ω；
d (x, y) =0 if and only if x=y
d(x,y)＝d(y,x),x,y∈Ω；
d(x,y)≤d(x,z)+d(x,y),x,y,z∈Ω.
Where d (x, y) represents the distance between two sample points, referred to herein as the range of the electrical signal, and x, y, z represents the different points.
4. The distributed wireless dynamic strain acquisition method of claim 3, wherein: the Minkowski distance is expressed as:
wherein d represents distance, x and y are values of two signals, q is an adjustment parameter, p represents a sample point and p variables are needed to be described, and k represents a variable.
5. The distributed wireless dynamic strain acquisition method of claim 4, wherein: the judging of the main control module further comprises the following steps:
according to the range of the electric signals, the electric signals are divided into 8 clustering signals, the analog electronic switching technology is adopted, the conditioning signals of different types of sensors are switched to the AD front end, and different input ends are respectively injected.
6. The distributed wireless dynamic strain acquisition method of claim 5, wherein: the electrical signal is input to a matched interface, comprising:
if the electric signal is not matched with the voltage range of the access port, judging that the electric signal is abnormal, and carrying out input judgment on the electric signal again;
when judging that the input port is the other input port, recording the current abnormality as an identification abnormality, and executing a re-judging access port;
when judging that the access port is not yet available, recording the abnormality as an operation abnormality after analyzing the error reason, and executing signal input of a source path;
if the electric signal is excessively abnormal, a characteristic signal is given when the electric signal is input, and the identified abnormal characteristic is reflected when the electric signal is stored and uploaded.
7. The distributed wireless dynamic strain acquisition method of claim 6, wherein: the transmitting the electric signals received by the interfaces to the cloud end comprises the following steps:
if an abnormal signal is found, carrying out classification early warning according to abnormal characteristics in the uploading process;
if the abnormal signal is analyzed to be a device fault, a maintenance message is given in the uploading process, and the fault position is positioned according to the distributed structure, so that the rapid maintenance of the fault is realized;
and correcting the abnormal information stored in the TF card after the abnormality is relieved, and keeping a modification record for the cloud information.
8. A distributed wireless dynamic strain acquisition system, comprising:
the receiving module is used for receiving the strain condition of the detected unit and connecting the sensed information to the sensing module;
the sensing module converts the strain of the measured object into a proper signal by utilizing the sensing element, and distributes the signal to 8 channels according to the instruction of the control module;
the control module is used for controlling the timing sequence IO acquired by the high-speed AD and controlling the switching of different types of sensor conditioning signals to the front end of the AD;
and the conduction module is used for transmitting the information to the cloud and storing the information in the TF card.
9. A computer device, comprising: a memory and a processor; the memory stores a computer program characterized in that: the processor, when executing the computer program, implements the steps of the method of any one of claims 1 to 8.
10. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program implementing the steps of the method of any one of claims 1 to 8 when executed by a processor.
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