Weighing method of hook scale

Abstract

The invention relates to the technical field of casting production devices, and discloses a weighing method of a hook scale, which adopts a dynamic weighted filtering and weight locking double-logic collaborative parallel working mechanism: in the dynamic weighted filtering process, weight difference and transverse acceleration are calculated by collecting weight and acceleration data in real time; fusing the basic weight and the acceleration weight factor to update a filtering value, and outputting stable filtering data; the weight locking process is controlled by a three-state machine, data are cached through an annular array, the weight is locked based on standard deviation and range verification, and signals are monitored in real time to dynamically unlock; double logics support locking judgment through filtering data, and a locking process responds to state change to realize collaboration. According to the invention, the precision and stability of dynamic weighing are significantly improved, low-frequency shaking noise is effectively suppressed, and the applicability and reliability of the hook scale in different scenes are broadened.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of casting production devices, in particular to a weighing method of a hook scale. BACKGROUND

[0002] The hook scale is an electronic weighing apparatus suspended on a hoisting device for real-time measurement of the weight of the hoisted object. Its core component is a weighing sensor, which converts physical weight into an electrical signal, and displays or transmits data after circuit processing. According to the structure and installation method, the hook scale can be divided into hook head suspension type, crane type, shaft seat type and embedded type, among which the embedded type does not affect the lifting height and does not need to modify the crane, becoming the development direction of the industry. According to the use environment, the hook scale can also be divided into normal temperature type, high temperature type, explosion-proof type, etc., and is widely used in steel, logistics, port, warehouse and other fields, especially suitable for scenes that need dynamic weighing or are inconvenient to fall on the ground. The hook scale usually uses a filtering algorithm to weigh the material in a shaking state, but the traditional filtering algorithm has poor suppression effect on 1-5Hz low-frequency shaking noise, and cannot meet the scene of severe shaking caused by the rapid trolley used for adding ingredients in casting production. SUMMARY

[0003] In order to solve the defects in the prior art, the present application provides a weighing method of a hook scale, which is realized by the following technical scheme: A weighing method of a hook scale adopts a dynamic weighting filtering process and a weight locking process to work cooperatively, and the two processes are executed in parallel, specifically including the following steps: (1) dynamic weighting filtering process: sequentially performing data acquisition and core parameter calculation, basic weight calculation, acceleration weighting calculation, maximum weight synthesis, and filter value updating to output stable weight filter value; (2) weight locking process: through three-state machine control, sequentially performing data cache preparation, data verification and locking judgment, locking maintenance and release to realize accurate weight locking and dynamic release; (3) cooperative logic: the filter value output by the dynamic weighting filtering process provides basic data for the weight locking process, and the weight locking process realizes locking by verifying the stability of the filtered data, and dynamically releases the locking when the object state changes or the weighing ends.

[0004] The data acquisition and core parameter calculation in the dynamic weighting filtering process specifically include: (1) real-time acquisition of core data: real-time weight W of the hoisted object, historical filter value FWD of the last measurement period, horizontal acceleration component X and vertical acceleration component Z of the hook scale; (2) calculating weight difference Di=|W-FWD| to represent the deviation degree of real-time weight and historical filter value; calculating lateral acceleration .

[0005] The base weight calculation in the dynamic weighted filtering process is specifically: (1) calculating the base weight A0 based on the weight difference Di, A0=0.7-0.7*(Di / 100); (2) base weight threshold verification: if A0<0, resetting A0 to 0.0; if A0>=0, keeping the A0 calculation result unchanged.

[0006] The acceleration weighting calculation in the dynamic weighted filtering process is specifically: (1) setting the acceleration effective range as 0.05g-0.5g; (2) calculating the scaling factor S based on the lateral acceleration La according to different scenarios: if La <0.05g, S=0.0; if 0.05g<La<0.5g, S=( La <0.5g, S=( La -0.05) / 0.45*0.7; if La >0.5g, S=1.0.

[0007] The maximum weight weight synthesis in the dynamic weighted filtering process is specifically: (1) fusing the base weight A0 and the acceleration weight factor S, the calculation formula is A=A0+S*(1-A0); (2) maximum weight threshold verification: if A>1.0, resetting A to 1.0; if A<=1.0, keeping the A calculation result unchanged.

[0008] The filter value update in the dynamic weighted filtering process is specifically: calculating the filter value FW of the current period based on the maximum weight A, the calculation formula is FW=A*FWD+(1-A)*W; the updated FW is used as the historical filter value FWD of the next measurement period for cyclic calculation.

[0009] The data cache preparation in the weight locking process is specifically: continuously collecting the real-time weight data W of the hoisted object, and storing it in a preset ring array in chronological order, to complete the base data accumulation.

[0010] The data verification and locking judgment in the weight locking process specifically includes: (1) calculating the standard deviation and range based on the cached weight data in the ring array; (2) locking condition judgment: if both the standard deviation<2 and the range<4 are met, entering the locking maintenance state, and setting the locking value as the mean value of all weight data in the ring array; if any of the above conditions is not met, clearing the locking flag, and continuing to collect and store the real-time weight data.

[0011] The locking maintaining and releasing in the weight locking process specifically comprises: (1) monitoring the mean deviation signal and the zeroing signal in real time; (2) locking state control: if the mean deviation is greater than or equal to 1 or W is less than 3 g, the locking is immediately released, the locking flag is cleared, the current weight data is stored in the ring array and the data buffering preparation step is returned to; if the mean deviation is less than 1 and W is greater than 3 g, the current locking value is maintained unchanged and the locking state is continuously maintained; the mean deviation signal is the difference between the mean value of the weight data in the current ring array and the locked value, and the zeroing signal is the real-time weight data W currently collected.

[0012] The technical scheme of the present application has the following advantages: The hook scale weighing method of the present application effectively solves the technical defects of the traditional filtering algorithm that the low-frequency shaking noise suppression effect is poor, and the severe shaking scene caused by the rapid headstock cannot be adapted, and significantly improves the weighing precision and stability in the dynamic weighing scene. In the dynamic weighted filtering process, the real-time weight W, the historical filtering value FWD, and the horizontal and vertical acceleration components X and Z of the hook scale of the hoisted object are collected in real time, the Di representing the deviation degree of the real-time weight and the historical filtering value and the lateral acceleration La representing the motion intensity are calculated, the base weight A0 is calculated based on Di through the formula A0=0.7-0.7×(Di / 100) and threshold verification is performed, the scaling factor S is calculated according to La in the effective interval of 0.05g-0.5g, the final weight A is obtained by fusing A0 and S through A=A0+S×(1-A0), and threshold verification is performed, and then the filtering value is updated through FW=A×FWD+(1-A)×W, so that the filtering process can adapt to the weight change amplitude and motion state of the hoisted object, the influence of the historical filtering value is reduced when the weight fluctuates greatly or the motion is intense, the real weight change is quickly responded, the weight of the historical filtering value is increased when the weight is stable or the motion is gentle, and the stability of the filtering value is ensured, so that a more reliable weight filtering value is output. In the weight locking process, the real-time weight data is continuously cached through a ring array, and the standard deviation and the range are calculated based on the cached data as the locking judgment basis, only when the standard deviation is <2 and the range is <4, the mean value of the data is set as the locking value and enters the locking maintenance state, and the mean value deviation signal and the zero signal are monitored in real time, when the mean value deviation is ≥1 or the real-time weight W≤3g, the locking is released in time and re-enters the data caching preparation stage, realizing the accurate locking and dynamic release of the weight, avoiding the locking value deviation caused by the slight shaking of the object during the weighing process, and quickly responding when the motion state of the object changes or the weighing is finished, ensuring the accuracy and real-time performance of the weighing result. The above-mentioned cooperation of the double logic makes the stable filtering value output by the dynamic weighted filtering process provide reliable basic data for the weight locking process, and the weight locking process realizes accurate locking and dynamic release by verifying the stability of the filtered data, finally realizes that in the complex dynamic weighing scene, the low-frequency shaking noise can be effectively suppressed, the weight change can be quickly responded, and the real weight can be accurately locked, which significantly improves the weighing performance of the hook scale, and widens its applicability and reliability in different application scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the specific embodiments of the present application or the prior art, the accompanying drawings needed to be used in the specific embodiments or prior art description will be briefly introduced as follows. Obviously, the accompanying drawings in the following description are some embodiments of the present application, and all other drawings obtained by those of ordinary skill in the art without creative effort based on these drawings belong to the protection scope of the present application.

[0014] Figure 1 Flowchart for updating the filter value; Figure 2 Flowchart for weight locking. EMBODIMENTS

[0015] The technical solutions of the present application will be described clearly and completely in combination with the accompanying drawings. Obviously, the described embodiments are some embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort belong to the protection scope of the present application.

[0016] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the modules or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second", "third" are only for the purpose of description, and cannot be understood as indicating or implying relative importance.

[0017] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connecting" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the communication inside two elements. For those of ordinary skill in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0018] In addition, the technical features involved in different embodiments of the present application described below can be combined with each other as long as there is no conflict.

[0019] As shown in the accompanying drawings Figure 1As shown, the present application provides a weighing method of a hook scale, and the core algorithm comprises dynamic weighting filtering and weight locking double logic: the filtering algorithm first calculates the absolute difference between the real-time weight and the filtering value and the transverse acceleration vector length, generates a basic weight, and then linearly superimposes an acceleration weight factor according to the basic weight value in the 0.05g to 0.5g interval, and finally outputs the fusion result.

[0020] I. Filtering value update Data acquisition and core parameter calculation.

[0021] Firstly, the real-time weight W, the current filtering value FWD, and the horizontal acceleration component X and the vertical acceleration component Z are obtained.

[0022] The hook scale has a three-axis acceleration sensor. When the hook scale is vertically hung, the positive direction of the Y axis is vertically downward, the positive direction of the X axis is toward the front of the hook scale, and the positive direction of the Z axis is toward the left. The X and Z parameters represent the accelerations in the X and Z directions. Since the gravitational acceleration is always vertically downward, when the hook scale is vertically hung, X and Z are both 0, and Y is equal to the gravitational acceleration G. When the hook scale is inclined, the components of the gravitational acceleration in the X and Z directions will increase, La which is actually the projection size of the gravitational acceleration on the XOZ plane, so the inclination degree of the hook scale can be judged according to the size of La .

[0023] Then, the weight difference Di is calculated and the transverse acceleration La is calculated La , that is, the absolute difference Di between the real-time weight and the filtering value is calculated, Di = |W - FWD|. La = sqrt(x^2 + z^2).

[0024] Basic weight calculation.

[0025] The basic weight A0 is calculated. The basic weight A0 is calculated according to the weight difference, and the formula is A0 = 0.7 - 0.7 x (Di / 100). Then, it is checked whether A0 is less than 0: if the condition is met, A0 is reset to 0.0; otherwise, the existing value of A0 is kept unchanged.

[0026] Acceleration weighting calculation.

[0027] It is judged whether the value of the transverse acceleration is between 0.05g and 0.5g, and a scaling factor S is calculated according to the value of the transverse acceleration La.

[0028] It is judged whether the value of the transverse acceleration La is greater than 0.05g: if La not greater than 0.05g, S is set to 0.0; if the transverse acceleration La is greater than 0.05g, it is further judged Lawhether greater than 0.5g, if yes La not greater than 0.5g, a scaling factor is calculated by the formula S=(La-0.05) / 0.45x0.7, if yes La greater than 0.5g, set S as 1.0. 0.45 and 0.7 are both parameter adjustment factors.

[0029] the final weight is synthesized.

[0030] the base weight A0 and the acceleration weighting factor S are combined to calculate the final weight A, the formula is A=A0+Sx(1-A0).

[0031] Then, check whether A is greater than 1.0: if the condition is met, reset A as 1.0, and then update the filter value FW; if A is not greater than 1.0, keep the current A and update FW. The updated FW is used as the current filter value FWD for the next measurement process.

[0032] FW=AxFWD+(1-A)XW.

[0033] The whole method adjusts the smoothing degree of filtering through weight difference and lateral acceleration, reduces the influence of historical value when the change is large or the acceleration is high, and increases the influence of historical value when the change is small or the acceleration is low, so as to realize adaptive filtering effect.

[0034] II. Weight locking After the sensor obtains the sampling value, the filtered weight FW is calculated according to the above formula, and then it is judged whether the displayed weight needs to be locked to a certain value. If it does not need to be locked, the filtered weight of the last process is displayed, and if it needs to be locked, the locked weight obtained in this state is displayed.

[0035] The weight locking core adopts a three-state machine control of data caching, condition judgment, and locking maintenance or release, and realizes accurate locking through ring array data storage and key index verification. The specific steps are as follows: First step: data caching preparation Continuously collect the real-time weight data W of the hoisted object, and store all the data in the preset ring array in order, complete the basic data accumulation before locking, and provide data support for subsequent index calculation.

[0036] Second step: data verification and locking judgment Index calculation: based on the weight data cached in the ring array, two core judgment indexes are calculated respectively: standard deviation (SD): the dispersion degree index of all weight data in the array; range: the difference between the maximum and minimum values of the weight data in the array.

[0037] Locking condition judgment: If the following two conditions are met simultaneously: ① SD < 2; ② Range < 4, set the lock value to the mean of all weight data in the ring array, and realize the lock maintenance state; If any of the above conditions is not met, clear the lock flag, keep the data buffer ready, and continue to buffer real-time weight data.

[0038] Step 3: Lock maintenance and release Continuous monitoring: After the system enters the lock state, real-time monitoring of two types of key signals: Mean deviation signal: the difference between the mean of the weight data in the current ring array and the locked value; Zero signal: the real-time weight data currently collected.

[0039] Lock state control: If any of the following conditions is detected: ① Mean deviation ≥ 1; ② Zero signal (real-time weight data ≤ 3g), immediately release the lock: clear the lock flag, store the current weight data in the ring array, and restart the data buffering and judgment process; If the above conditions are not detected (mean deviation < 1 and real-time weight data > 3g), the current lock value remains unchanged, and the lock state is maintained.

[0040] The weighing method of the hook scale of the present application adopts a dynamic weighted filtering and weight locking double logic cooperative working mechanism, which adaptively adjusts the filtering strategy and accurately locks the weight value by real-time sensing of weight change and motion state. The specific process is as follows: I. Dynamic weighted filtering process (filter value update) This process calculates dynamic weights by fusing weight differences and acceleration information to achieve adaptive filtering. The specific steps are as follows: Step 1: Data acquisition and core parameter calculation 1.1 Real-time acquisition of 3 types of core data: real-time weight W of the suspended object, historical filter value FWD of the last measurement period (as the previous filter value for current filter calculation), horizontal acceleration component X and vertical acceleration component Z of the hook scale; 1.2 Calculate 2 key intermediate parameters: Weight difference Di: represents the deviation of real-time weight from historical filter value, calculation formula: Di = |W - FWD|; Lateral acceleration La : represents the horizontal motion intensity of the hook scale, which is the acceleration vector length, calculation formula: .

[0041] Step 2: Basic weight calculation 2.1 Calculate the base weight A0 based on the weight difference Di, the calculation formula is: A0 = 0.7 - 0.7 x (Di / 100).

[0042] 2.2 Base weight threshold verification: if A0 < 0, reset A0 to 0.0; if A0 ≥ 0, keep the A0 calculation result unchanged.

[0043] Step 3: Acceleration weighted calculation (generate acceleration weight factor S) 3.1 Set the acceleration effective range: only for La Weighted adjustment is performed on the motion state in the range of 0.05g-0.5g (g is the acceleration of gravity); 3.2 Calculate the scaling factor S by scene: Scene 1: if La ≤0.05g (gentle motion), then S=0.0; Scene 2: if 0.05g < W < 0.5g (moderate motion), then S is calculated by a linear formula: La S=(La-0.05) / 0.45 x 0.7; Scene 3: if >0.5g (intense motion), then S=1.0. La La La

[0044] Step 4: Maximum weight synthesis 4.1 Fuse the base weight and the acceleration weight, the calculation formula is: A=A0+S x (1-A0); 4.2 Maximum weight threshold verification: if A>1.0, reset A to 1.0; if A≤1.0, keep the A calculation result unchanged.

[0045] Step 5: Filter value update Calculate the filter value FW of the current period based on the maximum weight A, the calculation formula is: FW=A x FWD + (1-A) x W; The updated FW will be used as the historical filter value FWD of the next measurement period for cyclic calculation.

[0046] II. Weight locking process (three-state machine control) This process realizes accurate weight locking and dynamic release through data caching and index verification, the specific steps are as follows: Step 1: Data caching preparation (before locking preparation) Continuously collect the real-time weight data W of the suspended object and store it in the preset ring array in chronological order, complete the basic data accumulation, and provide data support for subsequent index calculation. ​

[0047] Step 2: Data verification and lock judgment 2.1 Calculate core indicators: based on all the weight data cached in the ring array, calculate: Standard deviation SD: representing the dispersion of weight data; Range: representing the maximum fluctuation range of weight data (the difference between the maximum and minimum values in the array); 2.2 Lock condition judgment: If the following two conditions are met at the same time: ① Standard deviation SD < 2; ② Range < 4, enter the lock maintenance state, set the lock value to the mean of all weight data in the ring array, and update the lock flag Lf; If any of the above conditions is not met, clear the lock flag Lf, keep the data caching state, and continue to collect and store real-time weight data.

[0048] Step 3: Lock maintenance and release 3.1 Real-time monitoring of key signals: after the system enters the lock state, continuously monitor two types of signals: Mean deviation signal: the difference between the mean of weight data in the current ring array and the locked value; Zero signal: the real-time weight data W currently collected; 3.2 Lock state control: Unlock condition: if any of the following conditions is monitored: ① Mean deviation ≥ 1; ② Zero signal triggered (W ≤ 3g), then immediately execute: a. Lock flag is cleared; b. Store the current weight data in the ring array; c. Return to the data caching preparation step and restart the lock judgment process; Lock maintenance condition: if the above release conditions are not monitored (mean deviation < 1 and W > 3g), the current lock value remains unchanged, and the lock state is continuously maintained.

[0049] III. Process coordination logic Dynamic weighted filtering process and weight locking process are executed in parallel: the filtering process outputs stable weight filtering values in real time, providing reliable basic data for the locking process; the locking process verifies the stability of the filtered data to achieve precise weight locking, while dynamically releasing the lock when the object's motion state changes or the weighing ends, ensuring the real-time and accuracy of the weighing process.

[0050] The above method is illustrated by an example as follows.

[0051] First, define the following parameters: W1: sampled weight, weight calculated directly by the chip, i.e. how much is hung under ideal conditions (without any interference), W1 equals how much W2: filtered weight, the value calculated by filtering algorithm on W1 W3: Locking weight, after meeting the locking condition (SD < 2 and extreme value < 4), the locking value is obtained by averaging n W2 in the cache W: Final output Known conditions: The chip is continuously reading the voltage to calculate W1, and each time W1 is calculated, the entire method flow is run to obtain a final output value.

[0052] Detailed weighing process: 1. The hook scale is vertically stationary, turned on, peeled (i.e. subtract the current weight), no weight is hung, W1≡0 (actual may have jitter, but the zero detection will change the weight near 0 to 0), W2≡0, meet the locking condition, calculate W3≡0, output W=W3.

[0053] 2. Hang the weight (assume 1000kg), but do not move the crane, for ease of understanding, we consider it stationary, because the chip is continuously reading and calculating W1, so W1 should quickly change from 0 to 1000, which is divided into several parts, the first part is the large-scale change from 0 to 1000, after calculating the nth W1, first filter, calculate Di: the difference between the nth W1 and the nth-1 W2, which should be relatively large (assume 100-200). La (can be understood as the degree of inclination from 0 to 1), keep stationary and equal to 0.

[0054] Calculate A0 should be <0, and after two judgments, A0=0, continue to walk down, calculate the scaling factor S. La=0, S=0, A0=0, then calculate the nth W2=the nth W1, since it is a large change, it obviously does not meet the locking condition, Lf=0, output W=the nth W2.

[0055] The second part, W1 changes to the vicinity of 1000kg, due to slight shaking, W1 will fluctuate around 1000, this fluctuation will change and become smaller as the scale gradually becomes stationary, during this process Di will not be very large (assume from 100 to 0), La=0 (inclination is small, less than 0.05 is considered equal to 0, for ease of understanding, La=0), A0 will gradually change from 0 to 0.7, A=A0 (0->0.7), the filtering strength during this period will gradually increase, W2 becomes more and more stable, when it is stable enough (i.e. meets the locking condition), Lf=1, take the average of the numbers in the cache to get W3, output W=W3 (before meeting the condition, it is the same as the first part, W=W2).

[0056] Third part, mobile hook scale when the roll, this case is more complex, but can be seen alone only the impact of roll, roll after La larger, more than 0.05, will calculate S, S can be understood as normalized to 0 ~ 0.7 La, that is, and A0 same scale, to calculate the combined A, roll the larger, A should be larger, W2 more stable, to strengthen the filter effect of the period of shaking, to ensure that the movement remains stable even locked, and continue to increase the roll, La more than 0.5, A will = 1, W2 will remain unchanged, this is an extreme case, forced stability.

[0057] Obviously, the above examples are merely illustrative for clarity, and not limited to the embodiments. For those of ordinary skill in the art, other different forms of changes or variations can be made on the basis of the above description. Here, it is not necessary and impossible to exhaust all the embodiments. The obvious changes or variations derived therefrom are still within the protection scope of the present invention.

Claims

1. A weighing method for a crane scale, characterized in that, The dynamic weighted filtering process and the weight locking process work together in a collaborative mechanism, and the two processes are executed in parallel. Specifically, the following steps are included: (1) Dynamic weighted filtering process: data acquisition and core parameter calculation, basic weight calculation, acceleration weight calculation, final weight synthesis, and filter value update are executed in sequence to output a stable weight filter value; (2) Weight locking process: through three-state machine control, data cache preparation, data verification and lock judgment, lock maintenance and release are executed in sequence to achieve accurate weight locking and dynamic release; (3) Collaborative logic: the filter value output by the dynamic weighted filtering process provides basic data for the weight locking process. The weight locking process locks the data by verifying the stability of the filtered data and dynamically releases the lock when the object's motion state changes or when the weighing ends.

2. The weighing method of the hook scale according to claim 1, characterized in that, The data acquisition and core parameter calculation in the dynamic weighted filtering process specifically include: (1) real-time acquisition of core data: the real-time weight W of the suspended object, the historical filter value FWD of the previous metering cycle, and the horizontal acceleration component X and vertical acceleration component Z of the hook scale; (2) calculation of weight difference Di=|W-FWD|, which characterizes the degree of deviation between the real-time weight and the historical filter value; lateral acceleration .

3. The weighing method of the hook scale according to claim 2, characterized in that, The calculation of the basic weight in the dynamic weighted filtering process is as follows: (1) Calculate the basic weight A0 based on the weight difference Di, A0 = 0.7 - 0.7 × (Di / 100); (2) Basic weight threshold verification: if A0 < 0, then reset A0 to 0.0; if A0 ≥ 0, then keep the calculation result of A0 unchanged.

4. The weighing method of the hook scale according to claim 3, characterized in that, The acceleration weighting calculation in the dynamic weighted filtering process is specifically as follows: (1) setting the effective acceleration range to 0.05g~0.5g; (2) based on lateral acceleration. La Calculate the scaling factor S for each scene: If La If ≤0.05g, then S=0.0; if 0.05g < La If the weight is ≤0.5g, then S=( La -0.05) / 0.45×0.7; if La If the weight is greater than 0.5g, then S = 1.

0.

5. The weighing method of the hook scale according to claim 4, characterized in that, The final weight synthesis in the dynamic weighted filtering process is as follows: (1) The basic weight A0 and the acceleration weight factor S are fused together, and the calculation formula is A=A0+S×(1-A0); (2) Final weight threshold verification: if A>1.0, then A is reset to 1.0; if A≤1.0, then the calculation result of A remains unchanged.

6. The weighing method of the hook scale according to claim 5, characterized in that, The filter value update in the dynamic weighted filtering process is as follows: the filter value FW for the current period is calculated based on the final weight A, and the calculation formula is FW=A×FWD+(1-A)×W; the updated FW is used as the historical filter value FWD for the next measurement period for cyclic calculation.

7. The weighing method of the hook scale according to claim 1, characterized in that, The data cache preparation in the weight locking process specifically involves: continuously collecting real-time weight data W of the suspended object and storing it in a preset circular array in chronological order to complete the accumulation of basic data.

8. The weighing method of the hook scale according to claim 7, characterized in that, The data verification and locking judgment in the weight locking process specifically includes: (1) calculating the standard deviation and range based on the weight data cached in the circular array; (2) locking condition judgment: if the standard deviation < 2 and the range < 4 are satisfied at the same time, the locking maintenance state is entered and the locking value is set to the mean of all weight data in the circular array; if any of the above conditions are not satisfied, the locking flag is cleared and real-time weight data is collected and stored.

9. The weighing method of the hook scale according to claim 8, characterized in that, The locking and unlocking process in the weight locking procedure specifically includes: (1) real-time monitoring of mean offset signal and zeroing signal; (2) Locking status control: if the mean offset is detected to be ≥1 or W≤3g, the lock is immediately unlocked, the lock flag is cleared, the current weight data is stored in the circular array and the data cache preparation step is returned; if the mean offset is <1 and W>3g, the current lock value is maintained and the lock status is maintained; the mean offset signal is the difference between the mean of the weight data in the current circular array and the locked value, and the zeroing signal is the real-time weight data W collected at the moment.

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