Intelligent sensor and real-time transmission method of load signal

Abstract

The present invention relates to the technical field of digital information compression and transmission, and specifically to an intelligent sensor and a method for real-time transmission of load signals. The present invention first obtains data to be transmitted; further performs curve fitting on the target data; further obtains the initial compression rate of the target data based on the difference characteristics between the target data and the corresponding fitting curve; further obtains the corrected compression rate of the target data based on the difference characteristics between the target data and the data at the same time in the adjacent monitoring points of the target monitoring point, combined with the initial compression rate; finally, compresses and transmits the target data according to the corrected compression rate. The present invention integrates the two angles of the fluctuation of the target data itself and the similarity of the fluctuations of adjacent monitoring points to obtain an adaptive corrected compression rate, so that conventional data uses a high compression rate to reduce transmission resource consumption, and abnormal data uses a low compression rate to ensure data validity, thereby balancing monitoring data compression and transmission resource consumption.

Description

Technical Field

[0001] The present invention relates to the technical field of digital information compression transmission, and in particular to an intelligent sensor and a load signal real-time transmission method. Background Art

[0002] As an important part of heavy machinery and engineering vehicles, engineering tires play a key role in supporting load and transmitting power. Intelligent sensors are installed on engineering tires to collect the working signals of engineering tires, transmit the load signals in real time, and analyze the pressure and wear of engineering tires or warn the working status by analyzing the signal data to ensure work safety.

[0003] However, due to the complex working environment of engineering tires and limited data transmission resources, directly transmitting a large amount of monitoring data will take up too many resources, and simply using a high compression rate to compress the data may cause distortion of key data, thereby affecting the accuracy of analysis and decision-making. Therefore, how to balance the resource consumption of data transmission and the distortion risk of data compression becomes a key issue. Summary of the invention

[0004] In order to solve the technical problem of the difficulty in balancing the compression of monitoring data of engineering tires and the consumption of transmission resources, the purpose of the present invention is to provide an intelligent sensor and a method for real-time transmission of load signals. The technical solutions adopted are as follows:

[0005] A method for real-time transmission of load signals of an intelligent sensor, the method comprising:

[0006] Select any monitoring point as the target monitoring point; use the current data to be transmitted at the target monitoring point as the target data;

[0007] According to the local change characteristics of each data point in the target data, the fitting contribution rate of each data point is obtained; based on the fitting contribution rate of each data point, the target data is subjected to curve fitting; according to the difference characteristics between the target data and the corresponding fitting curve, the initial compression rate of the target data is obtained; according to the difference characteristics between the target data and the data of the same time in the adjacent monitoring points of the target monitoring point, the corrected compression rate of the target data is obtained in combination with the initial compression rate;

[0008] The target data is compressed and transmitted according to the modified compression ratio.

[0009] Furthermore, the method for obtaining the fitting contribution rate includes:

[0010] According to the difference characteristics between each data point and adjacent data in the target data, the fitting contribution rate of each data point is obtained; the difference characteristics between each data point and adjacent data are negatively correlated with the fitting contribution rate.

[0011] Furthermore, the method for performing curve fitting on the target data includes:

[0012] The fitting curve function of the target data is fitted by the gradient descent method, and the correction error is obtained by combining the fitting contribution rate of each data point; when the correction error of the target data is the smallest, a fitting curve and a fitting curve function are obtained; and the fitting contribution rate is positively correlated with the correction error.

[0013] Furthermore, the method for obtaining the initial compression rate includes:

[0014] Obtaining a fitting similarity parameter according to a difference between a broken line function formed by connecting adjacent data in the target data and the fitting curve function;

[0015] According to the target data and the fitting curve, the fitting error corresponding to each data in the target data is obtained; adjacent fitting errors are connected to form an error curve; and correction parameters are obtained according to the fluctuation characteristics of the error curve;

[0016] An initial compression rate of the target data is obtained according to the fitting similarity parameter and the correction parameter; both the fitting similarity parameter and the correction parameter are positively correlated with the initial compression rate.

[0017] Furthermore, the method for obtaining the correction parameters includes:

[0018] The correction parameter is obtained according to the integral value of the error curve; the integral value of the error curve is positively correlated with the correction parameter; and the correction parameter is greater than or equal to 1.

[0019] Furthermore, the method for obtaining the modified compression ratio includes:

[0020] The two adjacent monitoring points of the target monitoring point are respectively referred to as the target left adjacent monitoring point and the target right adjacent monitoring point;

[0021] According to the difference characteristics between the target data and the data at the same time in the monitoring point adjacent to the target to the left, a first change parameter is obtained; according to the difference characteristics between the target data and the data at the same time in the monitoring point adjacent to the target to the right, a second change parameter is obtained; according to the similarity characteristics between the first change parameter and the second change parameter, a correction parameter is obtained;

[0022] According to the initial compression rate and the correction parameter, a corrected compression rate of the target data is obtained; the initial compression rate and the correction parameter are both positively correlated with the corrected compression rate.

[0023] Furthermore, the method for compressing and transmitting the target data according to the modified compression ratio includes:

[0024] The modified compression ratio is also normalized; the modified compression ratio is used as an extraction ratio, data is extracted from the target data and discarded, and the remaining data is transmitted.

[0025] Furthermore, the data to be transmitted is stored in a temporary storage at each monitoring point.

[0026] The present invention also proposes an intelligent sensor, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any one of the steps of the method for real-time transmission of load signals of the intelligent sensor.

[0027] The present invention has the following beneficial effects:

[0028] The present invention first selects the current data to be transmitted of the target monitoring point as the target data, so as to facilitate orderly analysis one by one; further, according to the local change characteristics of each data point in the target data, the fitting contribution rate of each data point is obtained to reduce noise interference, so as to provide a basis for fitting a more accurate curve in the subsequent analysis and obtaining a more reliable compression rate; further, based on the fitting contribution rate of each data point, the target data is subjected to curve fitting, so as to facilitate the subsequent analysis of the regularity of the target data according to the fitting curve and obtain an adaptive compression rate; further, according to the difference characteristics between the target data and the corresponding fitting curve, the initial compression rate of the target data is obtained from the perspective of the fluctuation regularity of the target data itself, so as to prepare for the subsequent acquisition of a more accurate revised compression rate; further, according to the difference characteristics between the target data and the data at the same time in the adjacent monitoring points of the target monitoring point, the regularity of the target data is further analyzed from the perspective of the adjacent monitoring points, and a more accurate revised compression rate of the target data is obtained in combination with the initial compression rate; finally, the target data is compressed and transmitted according to the revised compression rate. The present invention integrates the two angles of the target data's own fluctuation and the similarity of the fluctuations of adjacent monitoring points to obtain an adaptive corrected compression rate, so that conventional data uses a high compression rate to reduce transmission resource consumption, and abnormal data uses a low compression rate to ensure data validity, thereby balancing monitoring data compression and transmission resource consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1A flow chart of a method for real-time transmission of load signals of an intelligent sensor provided by one embodiment of the present invention;

[0031] Figure 2 A flowchart of a method for obtaining an initial compression rate provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation, structure, features and effects of an intelligent sensor and a load signal real-time transmission method proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0033] 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.

[0034] The specific scheme of an intelligent sensor and a load signal real-time transmission method provided by the present invention is described in detail below with reference to the accompanying drawings.

[0035] See also Figure 1 , which shows a flow chart of a method for real-time transmission of load signals of an intelligent sensor provided by an embodiment of the present invention, specifically comprising:

[0036] Step S1: select any monitoring point as the target monitoring point; and use the current data to be transmitted at the target monitoring point as the target data.

[0037] In one embodiment of the present invention, an intelligent sensor is installed on an engineering tire, and the intelligent sensor includes at least an acceleration monitoring device. A total of 8 intelligent sensors are installed at intervals of 45 degrees, and each intelligent sensor serves as a monitoring point to monitor the acceleration signal of the engineering tire in the horizontal direction; at the same time, each sensor also includes a temporary storage for storing data to be transmitted, and data is collected at a sampling frequency of 1600HZ, and the data to be transmitted is stored in a corresponding temporary storage, which is convenient for analyzing the data to be transmitted, so as to determine the corrected compression rate, call the data transmission from the temporary storage, clear the temporary storage, store new data to be transmitted, and perform adaptive compression and transmission.

[0038] In other embodiments of the present invention, the implementer may adjust the installation scheme of the smart sensor according to actual needs, and may also monitor various data such as pressure and temperature and set other sampling frequencies.

[0039] It should be noted that, in one embodiment of the present invention, in order to avoid the data to be transmitted being too long, which is not conducive to distinguishing different working environments and load conditions, and to avoid the data to be transmitted being too short, which is not conducive to continuously analyzing the working state of the engineering tire, the length of the data to be transmitted is limited; considering that as the engineering tire rotates, the monitoring signal data also has a certain periodicity, the engineering tire rotates several times as a segment of data for framing, while utilizing the signal periodicity, it is also helpful to accurately analyze the working state of the engineering tire and reduce the analysis error caused by incomplete data segmentation; considering that the integral value of a signal of a cycle is zero, the collected data of the target monitoring point is integrated, and each time the integral value is zero, it is considered that the engineering tire rotates one circle;

[0040] Based on this, the collected data is integrated along the positive direction of the timing from the starting time of the collected data of the target monitoring point, and the time point when the integral value is zero each time is marked as the integral zero point; when a preset number of integral zero points appear, the integration is stopped and the integrated data is used as the data to be transmitted.

[0041] As an example, the preset number is 3, and data of the engineering tire rotating 3 times is selected as a frame of data to be transmitted.

[0042] In other embodiments of the present invention, the implementer may also preset a fixed time length, such as setting 10 seconds as a frame of data to be transmitted.

[0043] Step S2: According to the local change characteristics of each data point in the target data, obtain the fitting contribution rate of each data point; based on the fitting contribution rate of each data point, perform curve fitting on the target data; according to the difference characteristics between the target data and the corresponding fitting curve, obtain the initial compression rate of the target data; according to the difference characteristics between the target data and the data at the same time in the adjacent monitoring points of the target monitoring point, combined with the initial compression rate, obtain the corrected compression rate of the target data.

[0044] In an embodiment of the present invention, taking into account that normal monitoring data has a certain fluctuation pattern, the data that conforms to the corresponding fluctuation pattern is more likely to belong to conventional normal working data, and the contribution to abnormal warning or abnormal state analysis is lower. Therefore, it is necessary to perform curve fitting on the target data, and then analyze the compression rate applicable to the target data based on the fitting result, adaptively compress the target data, and balance the compression of monitoring data and transmission resource consumption.

[0045] Considering that the target data may contain some noise points when fitting the function, in order to avoid the interference of noise points on the fitting function, it is necessary to analyze the fitting contribution rate of each data point. Considering that the noise data points appear randomly and do not conform to the fluctuation characteristics of the monitoring data, especially the local changes are more obvious, the fitting contribution rate of each data point is obtained according to the local change characteristics of each data point in the target data, which provides a basis for fitting a more accurate curve and analyzing to obtain a more reliable compression rate.

[0046] Preferably, in one embodiment of the present invention, considering that the noise data points do not have the fluctuation pattern of the monitoring data, the noise data points are often greatly different from the data on the left and right sides of the time series, so the fitting contribution rate of each data point is obtained according to the difference characteristics between each data point and the adjacent data in the target data; the difference characteristics between each data point and the adjacent data are negatively correlated with the fitting contribution rate.

[0047] As an example, the calculation formula for the fitted contribution rate includes:

[0048] ;in, Indicates the target data The fitting contribution rate of data points; Indicated by natural constant The exponential function with base ; Represents the minimum function, which is used to obtain the minimum value of the data set in brackets; Indicates the target data The data value of the data point; represents the data value of the 𝑖+1th data point in the target data; Indicates the target data The data value of the data point; Indicates the preset zero division parameter to prevent the denominator from being zero. .

[0049] In the calculation formula of the fitting contribution rate, the data points in the target data are sorted along the positive direction of the time series by sampling time; It represents the absolute difference between the data point and the data points on both sides. Then, with the help of the data amplitude of the data point itself, the relative difference represents the difference characteristics between the data point and the adjacent data. The larger it is, the greater the difference between the data point and the data points on the left and right sides, the more likely it is a noise data point, and the smaller the fitting contribution rate is required. Therefore, negative correlation mapping and normalization adjustment are used to reduce the interference of noise points on the fitting function.

[0050] In other embodiments of the present invention, the implementer may also adopt other negative correlation mapping methods such as taking the inverse; may also adopt other methods such as maximum and minimum normalization for normalization; may also use Represents the difference characteristics between the data point and the adjacent data, and obtains the fitting contribution rate.

[0051] After obtaining the fitting contribution rate of each data point, curve fitting can be performed on the target data based on the fitting contribution rate of each data point.

[0052] Preferably, in one embodiment of the present invention, the gradient descent method is used to fit the fitting curve function of the target data, and the correction error is obtained in combination with the fitting contribution rate of each data point; when the correction error of the target data is minimized, the fitting curve and the fitting curve function are obtained; the fitting contribution rate is positively correlated with the correction error.

[0053] As an example, the function to be fitted is a sine function. First, construct a sine function: ; and construct the corrected error corrected by the fitting contribution rate: ;in, A fitting curve function representing the target data; represents amplitude; represents angular velocity; Indicates phase; Indicates the longitudinal offset; represents the time independent variable; represents the corrected error corrected by the fitting contribution rate; Indicates the number of data points in the target data; Indicates the target data The fitting contribution rate of data points; Indicates the target data The data value of the data point; Indicates the target data The fitted value of the data points.

[0054] By using the gradient descent method to determine , so that The smallest, at this time This is the final fitting curve function.

[0055] In the corrected error corrected by the fitting contribution rate, the smaller the fitting contribution rate, the more likely the corresponding data point is a noise data point. In the process of fitting the function, the error generated has a lower reference value. Therefore, weighted correction is performed through the fitting contribution rate to improve the accuracy of the corrected error and reduce noise interference.

[0056] It should be noted that fitting a sine function by the gradient descent method is a technical means well known to those skilled in the art and will not be described in detail here. In other embodiments of the present invention, the implementer may also obtain a fitting curve function by other methods such as Fourier series fitting.

[0057] After fitting a curve based on the target data, we can preliminarily evaluate the regular characteristics of the target data based on the difference characteristics between the target data and the corresponding fitting curve, and obtain the initial compression rate of the target data from the perspective of the regularity of the fluctuations of the target data itself, in preparation for obtaining a more accurate corrected compression rate later.

[0058] Preferably, in one embodiment of the present invention, the method for obtaining the initial compression rate includes:

[0059] See also Figure 2 , which shows a flow chart of a method for obtaining an initial compression rate provided by an embodiment of the present invention, specifically comprising:

[0060] Step S201: Obtain fitting similarity parameters according to the difference between the broken line function formed by connecting adjacent data in the target data and the fitting curve function.

[0061] Considering the broken line function formed by connecting adjacent data in the target data, the smaller the difference with the fitting curve function, the more the fluctuation of the target data conforms to the conventional sine function, and the more likely it is regular operation data. The compression rate can be higher during compression to save transmission resources, so the fitting similarity parameter is obtained. The larger the fitting similarity parameter, the greater the initial compression rate.

[0062] As an example, the area between the broken line function and the fitting curve function is calculated by the integral method, and then the area is negatively correlated and normalized to obtain the fitting similarity parameter, such as ;in represents the fitting similarity parameter; It represents the exponential function with the natural constant e as the base; Indicates the lower limit of integration, which is the starting time of the target data; Indicates the upper limit of the integral, which is the end time of the target data; A fitting curve function representing the target data; Represents the broken line function of the target data. In the formula, It represents the area between the broken line function and the fitting curve function. The smaller the area, the closer the broken line function and the fitting curve function are to each other, and the smaller the difference is. This reflects that the fluctuation characteristics of the target data are more consistent with the conventional sine function, and the more likely it is regular operation data. The larger the fitting similarity parameter, the greater the initial compression rate.

[0063] Step S202: according to the target data and the fitting curve, obtain the fitting error corresponding to each data in the target data; connect adjacent fitting errors to form an error curve; and obtain correction parameters according to the fluctuation characteristics of the error curve.

[0064] Considering that the monitoring signal of engineering tires may fluctuate significantly due to bumpy road conditions or turns, these fluctuation data may not have a high similarity with the sine function, but are still normal data during the operation of engineering tires, so this situation needs to be analyzed; considering that these fluctuation data will last for a period of time, there is a significant difference compared to the isolated noise that appears randomly, so the fluctuation characteristics of the entire curve are analyzed to obtain the correction parameters.

[0065] As an example, the absolute value of the difference between the data value of each data and the fitting value on the fitting curve is taken as the fitting error, so as to obtain the error curve.

[0066] Step S203: Obtaining the initial compression rate of the target data according to the fitting similarity parameter and the correction parameter.

[0067] Among them, the fitting similarity parameter and the correction parameter are both positively correlated with the initial compression rate.

[0068] As an example, the product of the fitting similarity parameter and the correction parameter is taken as the initial compression rate.

[0069] Preferably, in one embodiment of the present invention, considering that noise appears randomly, it has little effect on the integral value of the entire error curve, and the integral value approaches 0; for fluctuating data such as bumps, it has a certain duration, which has a greater impact on the integral value of the error curve, and the integral value is far away from 0 and is positive, so the correction parameter is obtained according to the integral value of the error curve; the integral value of the error curve is positively correlated with the correction parameter.

[0070] As an example, the error curve is integrated, and the product of the integral value of the error curve and the preset control coefficient is mapped to the inverse tangent trigonometric function, and the mapped value is added with a constant 1 to be used as a correction parameter.

[0071] Among them, the preset control coefficient is , which is used to control the rate of change of the inverse tangent trigonometric function. The integral value is positively correlated with the inverse tangent trigonometric function. At the same time, when the integral value approaches 0, it means that the target data is less likely to contain fluctuation data such as bumps, and the accuracy of the fitting similarity parameter is higher. At this time, the mapping value of the inverse tangent trigonometric function also approaches 0, so a constant 1 is added for correction to avoid the correction parameter from reducing the fitting similarity parameter. When the integral value is far away from 0, it means that the target data contains normal fluctuation data such as bumps, and the fitting error is normal. The fitting similarity parameter is small, and the mapping value of the inverse tangent trigonometric function is a positive value. Adding a constant 1 can amplify the fitting similarity parameter and improve the accuracy of the initial compression rate.

[0072] In other embodiments of the present invention, the implementer may also use other positive correlation functions for mapping, and may also add an amplification amplitude parameter to adjust the amplification factor of the correction parameter; for example, a sigmoid function is used for mapping, and the calculation formula is: , ;in represents the correction parameter; To enlarge the amplitude parameter; Represents the integral value of the error curve; at the same time, due to , so when hour, Distributed in [1,2).

[0073] In the embodiment of the present invention, it is also considered that the data between adjacent monitoring points at the same time have certain similarities, or show certain phase differences; when the engineering tire is operating normally, due to the rotation of the engineering tire, the monitoring data of adjacent monitoring points are relatively consistent in form, and there will be a certain time delay between different monitoring points; and when the target data is abnormal, it will cause obvious differences between the target data and the data at the same time in the adjacent monitoring points, so according to the difference characteristics between the target data and the data at the same time in the adjacent monitoring points of the target monitoring point, the regularity of the target data is further analyzed from the perspective of the adjacent monitoring points, and the corrected compression rate of the target data is obtained in combination with the initial compression rate;

[0074] Preferably, in one embodiment of the present invention, two adjacent monitoring points of a target monitoring point are respectively referred to as a target left neighbor monitoring point and a target right neighbor monitoring point; data of the target left neighbor monitoring point at the same time as the target data is taken as left neighbor data; data of the target right neighbor monitoring point at the same time as the target data is taken as right neighbor data;

[0075] According to the difference characteristics of all the data points of the same sequence between the target data and the left adjacent data, the first change parameter is obtained; according to the difference characteristics of all the data points of the same sequence between the target data and the right adjacent data, the second change parameter is obtained; according to the similarity characteristics of the first change parameter and the second change parameter, the correction parameter is obtained;

[0076] The corrected compression rate of the target data is obtained according to the initial compression rate and the correction parameter; the initial compression rate and the correction parameter are both positively correlated with the corrected compression rate.

[0077] As an example: the product of the initial compression ratio and the correction parameter is used as the correction compression ratio; the calculation formula of the correction parameter includes:

[0078] ;in, represents the corrected compression ratio of the target data; represents the standard normalization function; It represents the exponential function with the natural constant e as the base; Indicates the lower limit of integration, which is the starting time of the target data; Indicates the upper limit of the integral, which is the end time of the target data; A broken line function representing the target data; Represents the broken line function of the left neighbor data corresponding to the target data; Represents the broken line function of the right neighbor data corresponding to the target data; Indicates the initial compression ratio of the target data.

[0079] In the formula, The difference characteristics of all the data points of the same sequence between the target data and the left adjacent data are used as the first variation parameter; The difference characteristics of all the data points of the same sequence between the target data and the right adjacent data are used as the second change parameter; the difference between the first change parameter and the second change parameter is analyzed by the absolute value of the difference, and the similar characteristics of the first change parameter and the second change parameter are represented by negative correlation mapping. As a correction parameter; the larger the correction parameter is, the stronger the consistency of the target data and the data at the same time in two adjacent monitoring points is, the lower the possibility of abnormality in the target data, and the more likely it is regular normal data, requiring a larger compression rate. Therefore, it is multiplied by the initial compression rate, and the two angles of the fluctuation of the target data itself and the similarity of the fluctuation of adjacent monitoring points are integrated to obtain a more accurate corrected compression rate, thereby balancing the compression of monitoring data and the consumption of transmission resources, so that regular data uses a high compression rate to reduce transmission resource consumption, and abnormal data uses a low compression rate to ensure the validity of the data.

[0080] It should be noted that since the target detection point needs to be compared with both adjacent monitoring points, which side is left or right does not affect the result. Here, the positive direction of rotation of the engineering tire is right, and the reverse direction is left, and the target detection point needs to be on the same engineering tire as the two adjacent monitoring points.

[0081] Step S3: compress the target data according to the modified compression ratio and transmit it.

[0082] After obtaining the adaptively corrected compression rate of each target data, the target data can be compressed and transmitted according to the corrected compression rate, solving the technical problem of the difficulty in balancing the compression of monitoring data and the consumption of transmission resources.

[0083] Preferably, in one embodiment of the present invention, the modified compression ratio is used as the extraction ratio, data is extracted from the target data and discarded, and the remaining data is transmitted.

[0084] As an example: the corrected compression rate of a certain target data is 0.6, 60% of the target data is equally sampled and discarded, and the remaining data is transmitted as compressed data; this is similar to compressing 44.1kHz CD-quality audio to a lower sampling rate such as 17.64kHz, which can significantly reduce file size and reduce transmission pressure.

[0085] In other embodiments of the present invention, the implementer may also sort by fitting contribution rate. For example, if the corrected compression rate of a target data is 0.6, the data with the fitting contribution rate in the top 60% will be discarded and the remaining data will be transmitted as compressed data.

[0086] The present invention also proposes an intelligent sensor, which at least includes a memory, a processor and a computer program, wherein the memory is used to store a corresponding computer program, the processor is used to run the corresponding computer program, and the computer program can implement the real-time transmission method of the load signal of the intelligent sensor described in steps S1-S3 when it runs in the processor. In summary, in order to solve the technical problem that the compression of monitoring data of engineering tires and the consumption of transmission resources are difficult to balance, the present invention proposes an intelligent sensor and a real-time transmission method of load signals. The present invention first obtains the current data to be transmitted at the target monitoring point as the target data; further performs curve fitting on the target data; further obtains the initial compression rate of the target data according to the difference characteristics between the target data and the corresponding fitting curve; further obtains the corrected compression rate of the target data according to the difference characteristics between the target data and the data at the same time in the adjacent monitoring points of the target monitoring point, combined with the initial compression rate; finally, compresses and transmits the target data according to the corrected compression rate. The present invention integrates the two angles of the fluctuation of the target data itself and the similarity of the fluctuation of the adjacent monitoring points to obtain an adaptive corrected compression rate, so that the conventional data adopts a high compression rate to reduce the consumption of transmission resources, and the abnormal data adopts a low compression rate to ensure the data is valid, thereby balancing the compression of the monitoring data and the consumption of transmission resources.

[0087] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0088] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for real-time transmission of load signals of an intelligent sensor, characterized in that: The method comprises: Select any monitoring point as the target monitoring point; use the current data to be transmitted at the target monitoring point as the target data; According to the local change characteristics of each data point in the target data, the fitting contribution rate of each data point is obtained; based on the fitting contribution rate of each data point, the target data is subjected to curve fitting; according to the difference characteristics between the target data and the corresponding fitting curve, the initial compression rate of the target data is obtained; according to the difference characteristics between the target data and the data of the same time in the adjacent monitoring points of the target monitoring point, the corrected compression rate of the target data is obtained in combination with the initial compression rate; compressing and transmitting the target data according to the modified compression ratio; The method for obtaining the fitting contribution rate includes: According to the difference characteristics between each data point and adjacent data in the target data, a fitting contribution rate of each data point is obtained; the difference characteristics between each data point and adjacent data are negatively correlated with the fitting contribution rate; The method for performing curve fitting on the target data comprises: The fitting curve function of the target data is fitted by using the gradient descent method, and the correction error is obtained by combining the fitting contribution rate of each data point; when the correction error of the target data is the smallest, a fitting curve and a fitting curve function are obtained; the fitting contribution rate is positively correlated with the correction error; The method for obtaining the initial compression rate includes: Obtaining a fitting similarity parameter according to a difference between a broken line function formed by connecting adjacent data in the target data and the fitting curve function; According to the target data and the fitting curve, the fitting error corresponding to each data in the target data is obtained; adjacent fitting errors are connected to form an error curve; and correction parameters are obtained according to the fluctuation characteristics of the error curve; According to the fitting similarity parameter and the correction parameter, an initial compression rate of the target data is obtained; the fitting similarity parameter and the correction parameter are both positively correlated with the initial compression rate; The method for obtaining the correction parameters includes: The correction parameter is obtained according to the integral value of the error curve; the integral value of the error curve is positively correlated with the correction parameter; the correction parameter is greater than or equal to 1; The method for obtaining the modified compression ratio includes: The two adjacent monitoring points of the target monitoring point are respectively referred to as the target left adjacent monitoring point and the target right adjacent monitoring point; According to the difference characteristics between the target data and the data at the same time in the monitoring point adjacent to the target to the left, a first change parameter is obtained; according to the difference characteristics between the target data and the data at the same time in the monitoring point adjacent to the target to the right, a second change parameter is obtained; according to the similarity characteristics between the first change parameter and the second change parameter, a correction parameter is obtained; According to the initial compression rate and the correction parameter, obtaining the corrected compression rate of the target data; the initial compression rate and the correction parameter are both positively correlated with the corrected compression rate; The method for compressing and transmitting the target data according to the modified compression ratio includes: The modified compression ratio is also normalized; the modified compression ratio is used as an extraction ratio, data is extracted from the target data and discarded, and the remaining data is transmitted.

2. The method for real-time transmission of load signals of an intelligent sensor according to claim 1, characterized in that: The data to be transmitted is stored in a temporary storage at each monitoring point.

3. The method for real-time transmission of load signals of an intelligent sensor according to claim 1, characterized in that: The fitting curve function is a sine function.

4. An intelligent sensor, comprising at least a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for real-time transmission of load signals of an intelligent sensor as described in any one of claims 1 to 3 are implemented.

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