Method for stabilizing weighing sensor under tank

Abstract

The invention relates to the technical field of weighing sensors, and particularly discloses a method for stabilizing a weighing sensor under a tank, which comprises the following steps of: arranging a plurality of detection points on the lower edge of a weighing hopper tank, processing and analyzing based on offset distance values of the detection points, judging whether the weighing hopper generates a calibration signal or not, and calibrating the scale by padding iron sheets with different thicknesses based on the calibration signal. Processing and analyzing the internal code values of the three sensors when the iron sheet with the current thickness is calibrated, judging whether the sensor corresponding to the standard internal code value is in a balanced state with the other two sensors, judging whether the corresponding normal time period is marked as a similar time period or not based on the normal signal, and if yes, performing calibration; and judging whether the M sensor and the N sensor are in a balanced state or not based on the number of the similar time periods and section similar value processing analysis corresponding to the similar time periods, and performing balance judgment on the inner code values of the three sensors by placing gaskets with different thicknesses under the weighing hopper groove to realize calibration.

Description

Technical Field

[0001] The invention relates to the technical field of weighing sensors, and in particular to a method for stabilizing a weighing sensor under a tank. Background Art

[0002] The accuracy of the under-tank weighing system is related to the stable and smooth operation of the furnace. During the use of the weighing bucket, the weighing bin may be displaced, which directly leads to uneven force on the sensor. The data value fluctuates during calibration, and calibration cannot change the status quo.

[0003] A Chinese invention patent with publication number CN109959438B discloses a weighing control method, device and material weighing system, including: storing weighing experience data of multiple weighing sensors for material weighing; judging whether a weighing sensor failure occurs in the current weighing process; in the event of a weighing sensor failure, predicting the expected weight detection value of the failed weighing sensor in the current weighing process based on the weighing experience data; and combining the expected weight detection value with the current weight detection value of the weighing sensor that has not failed to determine the material weight of the current weighing process.

[0004] However, in the above-mentioned prior art, there is a lack of a solution to the problem that calibration cannot make the under-tank weighing system in a balanced state. If the weighing data is inaccurate and the deviation is large, the correct weighing data cannot be obtained. Summary of the invention

[0005] The object of the present invention is to provide a method for stabilizing a weighing sensor under a tank to solve the technical problems in the above background.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] The present invention provides a method for stabilizing a weighing sensor under a tank, comprising:

[0008] Step 1: multiple detection points are set at the lower edge of the weighing bucket slot, and the distance data between the position of the detection point after weighing and the original position of the detection point is measured, wherein the distance data includes the offset distance value of the detection point, and the detection point is marked as an abnormal detection point and a normal detection point based on the offset distance value processing and analysis of the detection point, and the calibration state value JZ is obtained based on the number of abnormal detection points and the offset distance value of the abnormal detection point. Based on the calibration state value JZ, it is determined whether the weighing bucket generates a calibration signal;

[0009] Step 2: Based on the calibration signal, calibrate by padding iron sheets of different thicknesses to obtain the internal code values ​​of the three sensors when calibrating the iron sheet of the current thickness, select one sensor internal code value from the three sensor internal code values ​​and mark it as the standard internal code value, process and analyze the other two sensor internal code values ​​and the standard internal code value to obtain the equilibrium state value PZ, and judge whether the sensor corresponding to the standard internal code value is in equilibrium with the other two sensors based on the equilibrium state value PZ;

[0010] Step 3: Based on the normal signal, obtain the normal time period for generating the normal signal and the sensor internal code value corresponding to the normal time period, analyze the normal time period and the sensor internal code value of the normal time period to obtain the segment similarity value QD, and determine whether to mark the corresponding normal time period as a similar time period based on the segment similarity value QD;

[0011] Step 4: Based on the number of similar time periods and the segment similarity values ​​corresponding to the similar time periods, a curve similarity value QX is obtained, and based on the curve similarity value QX, it is determined whether the M sensor and the N sensor are in a balanced state.

[0012] As a further solution of the present invention: the calibration state value JZ is obtained in the following manner:

[0013] The offset quantity ratio PS and the offset degree ratio PC are processed, and the calibration state value JZ is calculated by the formula: JZ=z1*PS+z2*PC, wherein z1 and z2 are preset proportional coefficients, and z1 and z2 are both greater than 0.

[0014] As a further solution of the present invention: the offset quantity ratio PS is obtained in the following manner:

[0015] Measure the distance between the position of the detection point after weighing and the original position of the detection point to obtain the offset distance value of the detection point, compare the offset distance value of the detection point with the standard value, if the offset distance value of the detection point is not equal to the standard value, then mark the detection point as an abnormal detection point, if the offset distance value of the detection point is equal to the standard value, then mark the detection point as a normal detection point;

[0016] The number of all detection points and the number of abnormal detection points among all detection points are counted, and the number of abnormal detection points is ratioed with the number of all detection points to obtain the offset number ratio PS of the abnormal detection points.

[0017] As a further solution of the present invention: the offset ratio PC is obtained in the following manner:

[0018] The offset distance value of the abnormal detection point is subtracted from the standard value to obtain the offset difference of the abnormal detection point. The offset differences of all abnormal detection points are summed and averaged to obtain the mean offset difference of all abnormal detection points. The mean offset difference of all abnormal detection points is compared with the standard value to obtain the offset degree ratio PC of all abnormal detection points.

[0019] As a further solution of the present invention: the equilibrium state value PZ is obtained in the following manner:

[0020] The normal curve quantity ratio ZS and the normal degree ratio ZC are processed, and the equilibrium state value PZ is calculated by the formula: PZ=a1*ZS+a2*ZC, wherein a1 and a2 are preset proportional coefficients, and a1 and a2 are both greater than 0.

[0021] As a further solution of the present invention: the normal curve quantity ratio ZS and the normal degree ratio ZC are obtained as follows:

[0022] Obtain the internal code values ​​of the three sensors when calibrating the iron sheet of the current thickness, select one sensor internal code value from the three sensor internal code values ​​and mark it as the standard internal code value, mark the other two sensor internal code values ​​as the M sensor internal code value and the N sensor internal code value, obtain the difference between the M sensor internal code value and the standard internal code value, mark it as the M value difference, obtain the difference between the N sensor internal code value and the standard internal code value, mark it as the N value difference, sum and average the M value difference and the N value difference, and obtain the average internal code value difference;

[0023] With the calibration time as the X-axis and the average internal code value difference within the calibration time as the Y-axis, an XY two-dimensional coordinate system is constructed, and the average internal code value differences of multiple time nodes within the calibration time are marked and connected in the XY two-dimensional coordinate system to obtain a value difference change curve;

[0024] Mark the preset average internal code value difference on the Y axis in the XY two-dimensional coordinate system, draw a straight line parallel to the X axis through the marked point, and mark it as the first reference line;

[0025] The value difference variation curve is segmented by the first reference line to obtain the total number of curve segments;

[0026] The value difference change curve higher than the first baseline is marked as an abnormal curve, and the value difference change curve lower than the first baseline is marked as a normal curve. The number of normal curves is counted and the ratio is processed with the total number of curve segments to obtain the normal curve number ratio ZS;

[0027] The calibration time period corresponding to the normal curve is obtained and marked as the normal time period. The duration corresponding to all normal time periods is obtained and marked as the normal duration. The normal duration is processed with the duration corresponding to the calibration time to obtain the normality ratio ZC.

[0028] As a further solution of the present invention: the segment similarity value QD is obtained as follows:

[0029] The relevant period value SJ and the relevant change variance XGC are processed by the formula: The segment similarity value QD of the current normal time period is calculated, where b1 and b2 are preset proportional coefficients, and both b1 and b2 are greater than 0.

[0030] As a further solution of the present invention: the relevant time period value SJ and the relevant change variance XGC are obtained in the following manner:

[0031] With the calibration time as the X-axis and the sensor internal code value corresponding to the normal time period of generating the balance signal as the Y-axis, an XY two-dimensional coordinate system is constructed, and the M sensor internal code values ​​in the normal time period are marked and connected in the coordinate system to obtain the A change curve segment, and the N sensor internal code values ​​in the normal time period are marked and connected in the coordinate system to obtain the B change curve segment;

[0032] Obtain the maximum internal code value of the sensor of the A change curve segment within the sub-time period and the minimum internal code value of the sensor, and perform subtraction processing to obtain the internal code value difference A;

[0033] Obtain the maximum internal code value of the sensor of the B change curve segment in the sub-time period and the minimum internal code value of the sensor, and perform subtraction processing to obtain the internal code value difference B;

[0034] Compare the internal code value difference A in the sub-time period with the internal code value B in the corresponding sub-time period for positivity:

[0035] Count the total number of relevant time periods and compare it with the number of sub-time periods to obtain the relevant time period value SJ;

[0036] Obtain the inner code value difference A and the inner code value difference B of each relevant time period, perform ratio processing on the inner code value difference A and the inner code value difference B of the relevant time period to obtain the relevant change ratio of the relevant time period, integrate the relevant change ratios of all relevant time periods into a relevant change ratio data group, obtain the variance value of the relevant change ratio data group, and mark it as the relevant change variance XGC.

[0037] As a further solution of the present invention: the curve similarity value QX is obtained as follows:

[0038] The similarity quantity ratio XS and the similarity degree ratio XC are processed, and the curve similarity value QX is calculated by the formula: QX=c1*XS+c2*XC, where c1 and c2 are preset proportional coefficients, and c1 and c2 are both greater than 0.

[0039] As a further solution of the present invention: the similarity quantity ratio XS and the similarity degree ratio XC are obtained as follows:

[0040] Obtain the number of similar time periods in all normal time periods, and compare it with the number of normal time periods to obtain the similarity ratio XS;

[0041] Get the segment similarity value corresponding to the similar time period in all normal time periods, perform subtraction processing on the segment similarity value and the segment similarity threshold to obtain the segment similarity difference value, sum and average the segment similarity difference values ​​corresponding to all similar time periods to obtain the segment similarity difference mean value, perform ratio processing on the segment similarity difference mean value and the segment similarity threshold value to obtain the similarity ratio XC.

[0042] Beneficial effects of the present invention:

[0043] (1) The present invention sets a plurality of detection points at the lower edge of the weighing bucket groove, measures the distance data between the position of the detection point after weighing and the original position of the detection point, wherein the distance data includes the offset distance value of the detection point, and based on the processing and analysis of the offset distance value of the detection point, marks the detection point as an abnormal detection point and a normal detection point, and based on the number of abnormal detection points and the processing and analysis of the offset distance value of the abnormal detection point, obtains a calibration status value JZ, and judges whether the weighing bucket generates a calibration signal based on the calibration status value JZ. The present invention is conducive to the generation of a calibration signal, and based on the calibration signal, judges whether to calibrate the current weighing bucket.

[0044] (2) The present invention is based on a calibration signal. The calibration is performed by padding iron sheets of different thicknesses to obtain three sensor internal code values ​​when the iron sheet of the current thickness is calibrated. One sensor internal code value is selected from the three sensor internal code values ​​and marked as a standard internal code value. The other two sensor internal code values ​​are processed and analyzed with the standard internal code value to obtain a balance state value PZ. Based on the balance state value PZ, it is determined whether the sensor corresponding to the standard internal code value is in a balance state with the other two sensors. Based on the normal signal, a normal time period for generating a normal signal and the sensor internal code value corresponding to the normal time period are obtained. Based on the normal time period and the normal time The segment sensor internal code value is analyzed to obtain the segment similarity value QD. Based on the segment similarity value QD, it is determined whether to mark the corresponding normal time period as a similar time period. Based on the number of similar time periods and the segment similarity values ​​corresponding to the similar time periods, a curve similarity value QX is obtained. Based on the curve similarity value QX, it is determined whether the M sensor and the N sensor are in a balanced state. When calibrating the weighing bucket, the present invention places gaskets of different thicknesses under the weighing bucket slot to judge the balance of the internal code values ​​of the three sensors, and then determines whether the gasket of the current thickness makes the weighing bucket in a balanced state to achieve calibration. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The present invention will be further described below in conjunction with the accompanying drawings.

[0046] Figure 1 is a flowchart of the steps of a method for stabilizing a weighing sensor under a tank according to an embodiment of the present invention;

[0047] Figure 2 It is a flow chart of the steps of obtaining a curve similarity value QX of a method for a weighing sensor under a stable tank described in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Example 1

[0050] See also Figure 1 As shown, a method for stabilizing a weighing sensor under a tank according to an embodiment of the present invention includes:

[0051] Step 1: multiple detection points are set at the lower edge of the weighing bucket slot, and the distance data between the position of the detection point after weighing and the original position of the detection point is measured, wherein the distance data includes the offset distance value of the detection point, and the detection point is marked as an abnormal detection point and a normal detection point based on the offset distance value processing and analysis of the detection point, and the calibration state value JZ is obtained based on the number of abnormal detection points and the offset distance value of the abnormal detection point. Based on the calibration state value JZ, it is determined whether the weighing bucket generates a calibration signal;

[0052] Measure the distance between the position of the detection point after weighing and the original position of the detection point to obtain the offset distance value of the detection point, compare the offset distance value of the detection point with the standard value, if the offset distance value of the detection point is not equal to the standard value, then mark the detection point as an abnormal detection point, if the offset distance value of the detection point is equal to the standard value, then mark the detection point as a normal detection point;

[0053] Count the number of all detection points and the number of abnormal detection points among all detection points, and perform ratio processing on the number of abnormal detection points and the number of all detection points to obtain the offset number ratio PS of the abnormal detection points;

[0054] The offset distance value of the abnormal detection point is subtracted from the standard value to obtain the offset difference of the abnormal detection point. The offset differences of all abnormal detection points are summed and averaged to obtain the mean offset difference of all abnormal detection points. The mean offset difference of all abnormal detection points is compared with the standard value to obtain the offset degree ratio PC of all abnormal detection points.

[0055] The offset quantity ratio PS and the offset degree ratio PC are processed, and the calibration state value JZ is calculated by the formula: JZ = z1*PS + z2*PC, where z1 and z2 are preset proportional coefficients, and z1 and z2 are both greater than 0;

[0056] Compare the calibration state value JZ with the calibration state threshold:

[0057] If the calibration state value JZ is greater than the calibration state threshold, the current weighing bucket generates a calibration signal;

[0058] If the calibration state value JZ is less than or equal to the calibration state threshold, the current weighing bucket does not generate a calibration signal;

[0059] The technical solution of the embodiment of the present invention is mainly as follows: multiple detection points are set at the lower edge of the weighing bucket groove, and the distance data between the position of the detection point after weighing and the original position of the detection point is measured, wherein the distance data includes the offset distance value of the detection point, and based on the offset distance value processing and analysis of the detection point, the detection point is marked as an abnormal detection point and a normal detection point, and based on the number of abnormal detection points and the offset distance value processing and analysis of the abnormal detection points, a calibration status value JZ is obtained, and based on the calibration status value JZ, it is judged whether the weighing bucket generates a calibration signal. The present invention is conducive to the generation of calibration signals, and based on the calibration signal, it is judged whether the current weighing bucket is calibrated.

[0060] Example 2

[0061] Based on Example 1, please refer to Figure 1 and Figure 2 As shown, the method for stabilizing a weighing sensor under a trough according to an embodiment of the present invention further includes the following steps:

[0062] Step 2: Based on the calibration signal, calibrate by padding iron sheets of different thicknesses to obtain the internal code values ​​of the three sensors when calibrating the iron sheet of the current thickness, select one sensor internal code value from the three sensor internal code values ​​and mark it as the standard internal code value, process and analyze the other two sensor internal code values ​​and the standard internal code value to obtain the equilibrium state value PZ, and judge whether the sensor corresponding to the standard internal code value is in equilibrium with the other two sensors based on the equilibrium state value PZ;

[0063] Specifically, the three sensor internal code values ​​when the iron sheet of the current thickness is calibrated are obtained, one sensor internal code value is selected from the three sensor internal code values ​​and marked as the standard internal code value, and the other two sensor internal code values ​​are marked as the M sensor internal code value and the N sensor internal code value, respectively, and the difference between the M sensor internal code value and the standard internal code value is obtained, and it is marked as the M value difference, and the difference between the N sensor internal code value and the standard internal code value is obtained, and it is marked as the N value difference, and the M value difference and the N value difference are summed and averaged to obtain the average internal code value difference;

[0064] With the calibration time as the X-axis and the average internal code value difference within the calibration time as the Y-axis, an XY two-dimensional coordinate system is constructed, and the average internal code value differences of multiple time nodes within the calibration time are marked and connected in the XY two-dimensional coordinate system to obtain a value difference change curve;

[0065] It should be noted that the calibration time is the time period for calibration by padding iron sheets of different thicknesses. Three sensors are set under the weighing bucket slot. The three sensors are located at the three vertices of the weighing bucket to form an equilateral triangle.

[0066] Mark the preset average internal code value difference on the Y axis in the XY two-dimensional coordinate system, draw a straight line parallel to the X axis through the marked point, and mark it as the first reference line;

[0067] The value difference variation curve is segmented by the first reference line to obtain the total number of curve segments;

[0068] The value difference change curve higher than the first baseline is marked as an abnormal curve, and the value difference change curve lower than the first baseline is marked as a normal curve. The number of normal curves is counted and the ratio is processed with the total number of curve segments to obtain the normal curve number ratio ZS;

[0069] Obtain the calibration time period corresponding to the normal curve, mark it as the normal time period, obtain the duration corresponding to all normal time periods, mark it as the normal duration, perform ratio processing on the normal duration and the duration corresponding to the calibration time, and obtain the normality ratio ZC;

[0070] The normal curve quantity ratio ZS and the normal degree ratio ZC are processed, and the equilibrium state value PZ is calculated by the formula: PZ = a1*ZS+a2*ZC, where a1 and a2 are preset proportional coefficients, and a1 and a2 are both greater than 0;

[0071] Compare the equilibrium state value PZ with the equilibrium state threshold:

[0072] If the equilibrium state value PZ is greater than the equilibrium state threshold, a normal signal is generated in the corresponding normal time period, indicating that the M sensor is in equilibrium with the sensor corresponding to the standard internal code value, and the N sensor is in equilibrium with the sensor corresponding to the standard internal code value;

[0073] If the equilibrium state value PZ is less than or equal to the equilibrium state threshold, an abnormal signal is generated in the corresponding normal time period;

[0074] Step 3: Based on the normal signal, obtain the normal time period for generating the normal signal and the sensor internal code value corresponding to the normal time period, analyze the normal time period and the sensor internal code value of the normal time period to obtain the segment similarity value QD, and determine whether to mark the corresponding normal time period as a similar time period based on the segment similarity value QD;

[0075] With the calibration time as the X-axis and the sensor internal code value corresponding to the normal time period of generating the balance signal as the Y-axis, an XY two-dimensional coordinate system is constructed, and the M sensor internal code values ​​in the normal time period are marked and connected in the coordinate system to obtain the A change curve segment, and the N sensor internal code values ​​in the normal time period are marked and connected in the coordinate system to obtain the B change curve segment;

[0076] Obtain the maximum internal code value of the sensor of the A change curve segment within the sub-time period and the minimum internal code value of the sensor, and perform subtraction processing to obtain the internal code value difference A;

[0077] Obtain the maximum internal code value of the sensor of the B change curve segment in the sub-time period and the minimum internal code value of the sensor, and perform subtraction processing to obtain the internal code value difference B;

[0078] Compare the internal code value difference A in the sub-time period with the internal code value B in the corresponding sub-time period for positivity:

[0079] Count the total number of relevant time periods and compare it with the number of sub-time periods to obtain the relevant time period value SJ;

[0080] Obtain the inner code value difference A and the inner code value difference B of each relevant time period, perform ratio processing on the inner code value difference A and the inner code value difference B of the relevant time period to obtain the relevant change ratio of the relevant time period, integrate the relevant change ratios of all relevant time periods into a relevant change ratio data group, obtain the variance value of the relevant change ratio data group, and mark it as the relevant change variance XGC;

[0081] The relevant period value SJ and the relevant change variance XGC are processed by the formula: The segment similarity value QD of the current normal time period is calculated, where b1 and b2 are preset proportional coefficients, and both b1 and b2 are greater than 0;

[0082] Compare the segment similarity value QD with the segment similarity threshold:

[0083] If the segment similarity value QD is greater than the segment similarity threshold, the corresponding normal time period is marked as a similar time period;

[0084] If the segment similarity value QD is less than or equal to the segment similarity threshold, the normal time period is marked as a non-similar time period;

[0085] Step 4: Based on the number of similar time periods and the segment similarity values ​​corresponding to the similar time periods, the curve similarity value QD is obtained, and based on the curve similarity value QX, it is determined whether the M sensor and the N sensor are in a balanced state;

[0086] Obtain the number of similar time periods in all normal time periods, and compare it with the number of normal time periods to obtain the similarity ratio XS;

[0087] Obtain the segment similarity value corresponding to the similar time period in all normal time periods, perform subtraction processing on the segment similarity value and the segment similarity threshold to obtain the segment similarity difference value, sum and average the segment similarity difference values ​​corresponding to all similar time periods to obtain the segment similarity difference mean value, perform ratio processing on the segment similarity difference mean value and the segment similarity threshold value to obtain the similarity degree ratio XC;

[0088] The similarity number ratio XS and the similarity degree ratio XC are processed, and the curve similarity value QX is calculated by the formula: QX = c1*XS+c2*XC, where c1 and c2 are preset proportional coefficients, and c1 and c2 are both greater than 0;

[0089] Compare the curve similarity value QX with the curve similarity threshold:

[0090] If the curve similarity value QX is greater than the curve similarity threshold, it means that the A change curve segment is similar to the B change curve segment, indicating that the M sensor and the N sensor are in a balanced state, and the gasket of the current thickness is marked as a suitable gasket;

[0091] If the curve similarity value is less than or equal to the curve similarity threshold, it means that the A change curve segment is not similar to the B change curve segment, indicating that the M sensor and the N sensor are in an unbalanced state, and the gasket of the current thickness is marked as an unsuitable gasket;

[0092] The technical solution of the embodiment of the present invention is mainly as follows: based on the calibration signal, calibration is performed by padding iron sheets of different thicknesses, three sensor internal code values ​​when the iron sheet of the current thickness is calibrated are obtained, one sensor internal code value is selected from the three sensor internal code values ​​and marked as the standard internal code value, the other two sensor internal code values ​​and the standard internal code value are processed and analyzed to obtain a balance state value PZ, based on the balance state value PZ, it is determined whether the sensor corresponding to the standard internal code value is in a balance state with the other two sensors, based on the normal signal, a normal time period for generating a normal signal and the sensor internal code value corresponding to the normal time period are obtained, and based on the normal time period, a sensor internal code value corresponding to the normal time period is obtained. The code value of the sensor in the normal time period is analyzed to obtain the segment similarity value QD. Based on the segment similarity value QD, it is determined whether to mark the corresponding normal time period as a similar time period. Based on the number of similar time periods and the segment similarity values ​​corresponding to the similar time periods, a curve similarity value QX is obtained. Based on the curve similarity value QX, it is determined whether the M sensor and the N sensor are in a balanced state. When calibrating the weighing bucket, the present invention places gaskets of different thicknesses under the weighing bucket slot to judge the balance of the internal code values ​​of the three sensors, and then determines whether the gasket of the current thickness makes the weighing bucket in a balanced state to achieve calibration.

[0093] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for stabilizing a weighing sensor under a tank, characterized in that: include: Step 1: multiple detection points are set at the lower edge of the weighing bucket slot, and the distance data between the position of the detection point after weighing and the original position of the detection point is measured, wherein the distance data includes the offset distance value of the detection point, and the detection point is marked as an abnormal detection point and a normal detection point based on the offset distance value processing and analysis of the detection point, and the calibration state value JZ is obtained based on the number of abnormal detection points and the offset distance value of the abnormal detection point. Based on the calibration state value JZ, it is determined whether the weighing bucket generates a calibration signal; Step 2: Based on the calibration signal, calibrate by padding iron sheets of different thicknesses to obtain the internal code values ​​of the three sensors when calibrating the iron sheet of the current thickness, select one sensor internal code value from the three sensor internal code values ​​and mark it as the standard internal code value, process and analyze the other two sensor internal code values ​​and the standard internal code value to obtain the equilibrium state value PZ, and judge whether the sensor corresponding to the standard internal code value is in equilibrium with the other two sensors based on the equilibrium state value PZ; Step 3: Based on the normal signal, obtain the normal time period for generating the normal signal and the sensor internal code value corresponding to the normal time period, analyze the normal time period and the sensor internal code value of the normal time period to obtain the segment similarity value QD, and determine whether to mark the corresponding normal time period as a similar time period based on the segment similarity value QD; Step 4: Based on the number of similar time periods and the segment similarity values ​​corresponding to the similar time periods, a curve similarity value QX is obtained, and based on the curve similarity value QX, it is determined whether the M sensor and the N sensor are in a balanced state.

2. A method for stabilizing a weighing sensor under a tank according to claim 1, characterized in that: The calibration state value JZ is obtained in the following manner: The offset quantity ratio PS and the offset degree ratio PC are processed, and the calibration state value JZ is calculated by the formula: JZ=z1*PS+z2*PC, wherein z1 and z2 are preset proportional coefficients, and z1 and z2 are both greater than 0.

3. A method for stabilizing a weighing sensor under a tank according to claim 2, characterized in that: The offset quantity ratio PS is obtained as follows: Measure the distance between the position of the detection point after weighing and the original position of the detection point to obtain the offset distance value of the detection point, compare the offset distance value of the detection point with the standard value, if the offset distance value of the detection point is not equal to the standard value, then mark the detection point as an abnormal detection point, if the offset distance value of the detection point is equal to the standard value, then mark the detection point as a normal detection point; The number of all detection points and the number of abnormal detection points among all detection points are counted, and the number of abnormal detection points is ratioed with the number of all detection points to obtain the offset number ratio PS of the abnormal detection points.

4. A method for stabilizing a weighing sensor under a tank according to claim 2, characterized in that: The offset ratio PC is obtained as follows: The offset distance value of the abnormal detection point is subtracted from the standard value to obtain the offset difference of the abnormal detection point. The offset differences of all abnormal detection points are summed and averaged to obtain the mean offset difference of all abnormal detection points. The mean offset difference of all abnormal detection points is compared with the standard value to obtain the offset degree ratio PC of all abnormal detection points.

5. A method for stabilizing a load cell under a tank according to claim 1, characterized in that: The equilibrium state value PZ is obtained as follows: The normal curve quantity ratio ZS and the normal degree ratio ZC are processed, and the equilibrium state value PZ is calculated by the formula: PZ=a1*ZS+a2*ZC, wherein a1 and a2 are preset proportional coefficients, and a1 and a2 are both greater than 0.

6. A method for stabilizing a load cell under a tank according to claim 5, characterized in that: The normal curve quantity ratio ZS and the normal degree ratio ZC are obtained as follows: Obtain the internal code values ​​of the three sensors when calibrating the iron sheet of the current thickness, select one sensor internal code value from the three sensor internal code values ​​and mark it as the standard internal code value, mark the other two sensor internal code values ​​as the M sensor internal code value and the N sensor internal code value, obtain the difference between the M sensor internal code value and the standard internal code value, mark it as the M value difference, obtain the difference between the N sensor internal code value and the standard internal code value, mark it as the N value difference, sum and average the M value difference and the N value difference, and obtain the average internal code value difference; With the calibration time as the X-axis and the average internal code value difference within the calibration time as the Y-axis, an XY two-dimensional coordinate system is constructed, and the average internal code value differences of multiple time nodes within the calibration time are marked and connected in the XY two-dimensional coordinate system to obtain a value difference change curve; Mark the preset average internal code value difference on the Y axis in the XY two-dimensional coordinate system, draw a straight line parallel to the X axis through the marked point, and mark it as the first reference line; The value difference variation curve is segmented by the first reference line to obtain the total number of curve segments; The value difference change curve higher than the first baseline is marked as an abnormal curve, and the value difference change curve lower than the first baseline is marked as a normal curve. The number of normal curves is counted and the ratio is processed with the total number of curve segments to obtain the normal curve number ratio ZS; The calibration time period corresponding to the normal curve is obtained and marked as the normal time period. The duration corresponding to all normal time periods is obtained and marked as the normal duration. The normal duration is processed with the duration corresponding to the calibration time to obtain the normality ratio ZC.

7. A method for stabilizing a load cell under a tank according to claim 1, characterized in that: The segment similarity value QD is obtained as follows: The relevant period value SJ and the relevant change variance XGC are processed by the formula: The segment similarity value QD of the current normal time period is calculated, where b1 and b2 are preset proportional coefficients, and both b1 and b2 are greater than 0.

8. A method for stabilizing a load cell under a tank according to claim 7, characterized in that: The method of obtaining the relevant time period value SJ and the relevant change variance XGC is as follows: With the calibration time as the X-axis and the sensor internal code value corresponding to the normal time period of generating the balance signal as the Y-axis, an XY two-dimensional coordinate system is constructed, and the M sensor internal code values ​​in the normal time period are marked and connected in the coordinate system to obtain the A change curve segment, and the N sensor internal code values ​​in the normal time period are marked and connected in the coordinate system to obtain the B change curve segment; Obtain the maximum internal code value of the sensor of the A change curve segment within the sub-time period and the minimum internal code value of the sensor, and perform subtraction processing to obtain the internal code value difference A; Obtain the maximum internal code value of the sensor of the B change curve segment in the sub-time period and the minimum internal code value of the sensor, and perform subtraction processing to obtain the internal code value difference B; Compare the internal code value difference A in the sub-time period with the internal code value B in the corresponding sub-time period for positivity: Count the total number of relevant time periods and compare it with the number of sub-time periods to obtain the relevant time period value SJ; Obtain the inner code value difference A and the inner code value difference B of each relevant time period, perform ratio processing on the inner code value difference A and the inner code value difference B of the relevant time period to obtain the relevant change ratio of the relevant time period, integrate the relevant change ratios of all relevant time periods into a relevant change ratio data group, obtain the variance value of the relevant change ratio data group, and mark it as the relevant change variance XGC.

9. A method for stabilizing a load cell under a tank according to claim 1, characterized in that: The curve similarity value QX is obtained as follows: The similarity quantity ratio XS and the similarity degree ratio XC are processed, and the curve similarity value QX is calculated by the formula: QX=c1*XS+c2*XC, where c1 and c2 are preset proportional coefficients, and c1 and c2 are both greater than 0.

10. A method for stabilizing a load cell under a tank according to claim 9, characterized in that: The similarity quantity ratio XS and the similarity degree ratio XC are obtained as follows: Obtain the number of similar time periods in all normal time periods, and compare it with the number of normal time periods to obtain the similarity ratio XS; Get the segment similarity value corresponding to the similar time period in all normal time periods, perform subtraction processing on the segment similarity value and the segment similarity threshold to obtain the segment similarity difference value, sum and average the segment similarity difference values ​​corresponding to all similar time periods to obtain the segment similarity difference mean value, perform ratio processing on the segment similarity difference mean value and the segment similarity threshold value to obtain the similarity ratio XC.

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