Method and system for dynamic weighing of an article

Abstract

The application relates to an article dynamic weighing method, in particular to an article dynamic weighing method and system. The method comprises the following steps: placing an article on a weighing device using a spring, and configuring a force sensor below the spring; acquiring the size and direction of the deformation force of the spring at multiple time points and the weight measurement value of the article through the force sensor; inputting the size and direction of the deformation force into a swing feature recognition model, and the swing feature recognition model correspondingly outputs a recommended offset coefficient; the swing feature recognition model is pre-trained by using a swing database; and the weight measurement value is corrected by using the offset coefficient to obtain a new weight correction value. The application can dynamically distinguish the source of weighing error and adaptively switch the corresponding weighing compensation strategy, so that the influence on the original production line operation is reduced on the basis of improving the accuracy of the measurement value as much as possible.

Description

Technical Field

[0001] This invention relates to the field of weighing technology, specifically to a dynamic weighing method and system for articles. Background Technology

[0002] On modern production lines, high-precision and high-efficiency online weighing is a core element in achieving lean production and intelligent quality control.

[0003] Currently, traditional technologies have also attempted to propose some intelligent weighing solutions for items.

[0004] For example, patent application CN120930995A discloses a material weighing and conveying control method and system, relating to the field of adaptive control system technology. The method includes: setting visual positioning markers in the unloading area and configuring identification codes for the materials; establishing an information management database, associating the material identification codes with material attributes to generate a material association attribute table; collecting the material identification codes and retrieving the material association attribute table based on the identification codes; obtaining the production line status tables of each production line and the current warehouse status table based on the information management database, generating a candidate transportation list, determining the comprehensive priority score of each candidate transportation target point, and selecting the candidate transportation target point with the highest score as the final transportation target point.

[0005] For example, patent application CN120851362A proposes an IoT dynamic weighing and data traceability method and system, relating to the field of industrial internet data processing technology. The method includes: acquiring dynamic weighing data of a weighing event and at least one set of synchronized auxiliary data; generating a credibility weight value for the dynamic weighing data based on the auxiliary data using a preset credibility model; performing weighted statistical analysis on one or more dynamic weighing data points based on the weight value to obtain a weighted statistical analysis result; and integrating the weighted statistical analysis result with the changing trend of the credibility weight value to perform a two-dimensional fusion diagnosis to obtain a diagnostic result that can accurately decouple the root cause of the problem; and generating a high-credibility traceability record or issuing control commands based on the diagnostic result.

[0006] For example, patent application CN120106739A discloses an intelligent logistics warehouse cargo weighing management system and method, which relates to the field of cargo weighing management technology. The system includes: a starting image acquisition module, a contour midpoint acquisition module, a predicted weight acquisition module, a final image acquisition module, an observed weight acquisition module, and a final weight acquisition module. The contour midpoint acquisition module is used to acquire a starting contour map of the starting cargo image based on a cargo boundary contour acquisition method, and to acquire the starting contour midpoint based on the starting contour map. The final weight acquisition module is used to calculate the average of the predicted weight and the observed weight, mark it as the final weight, and use the final weight as the weight of the cargo.

[0007] However, the applicant noted that these weighing methods are unsuitable for complex production line scenarios. Therefore, there is an urgent need for a method to improve the efficiency and accuracy of weighing on production lines. Summary of the Invention

[0008] The purpose of this invention is to provide a dynamic weighing method and system for goods, which partially solves or alleviates the above-mentioned deficiencies in the prior art. It can dynamically distinguish the sources of weighing errors (such as spring swing, temperature drift, or environmental interference) and adaptively switch the corresponding weighing compensation strategy, thereby minimizing the impact on the operation of the original production line while maximizing the accuracy of the measurement values. To solve the aforementioned technical problems, the present invention specifically adopts the following technical solution: A first aspect of the present invention is to provide a method for dynamic weighing of an article, comprising: S101, placing an item on a weighing device using a spring, with a force sensor disposed below the spring; S102, the force sensor acquires the magnitude and direction of multiple deformation forces of the spring at multiple moments, as well as the weight measurement value of the item; S103, the magnitude and direction of the multiple deformation forces are input into the swing feature recognition model, and the swing feature recognition model outputs a recommended offset coefficient; wherein, the swing feature recognition model is pre-trained using a swing database, the swing database includes: multiple swing sample data, and the swing sample data includes: the offset coefficient between the measured weight value and the true value of the sample item, and the swing feature corresponding to the spring, the swing feature including at least one of: swing speed, swing amplitude, and swing frequency; Wherein, the swing speed refers to the instantaneous speed and direction of the spring's movement; the swing amplitude refers to the distance the spring travels from its rest position to its maximum deformation position; and the swing frequency refers to the total number of swings the spring makes within a certain time period. S104, The weight measurement value is corrected using the offset coefficient to obtain a new weight correction value.

[0009] In some embodiments, prior to S104, the method further includes: S105, determine whether the current swing degree is greater than or equal to the first swing degree and less than the second swing degree; wherein, the swing degree refers to the degree of intensity of the spring deviating from its equilibrium position and reciprocating, which is defined by at least one of the swing characteristics; If the result of S105 is yes, then execute: S106, determine whether the actual swing degree difference between the current swing degree and the historical swing degree is greater than or equal to the preset swing degree difference; S107. If the result of the judgment in S106 is yes, then return to S106.

[0010] In some embodiments, it also includes: S108. If the result of the judgment in S106 is negative, then proceed to S104.

[0011] In some embodiments, it also includes: S109, Determine whether the current swing degree is less than the first swing degree; If so, proceed to S104.

[0012] In some embodiments, it also includes: S1010, Determine whether the current swing degree is greater than or equal to the second swing degree; If so, a prompt signal will be issued.

[0013] In some embodiments, it also includes: S1011, Calculate the deviation of the current swing degree of the same batch of items; the deviation degree is used to define the numerical fluctuation of the current swing degree; S1012, determine whether the deviation degree is less than a preset deviation degree; If the judgment result of S1012 is yes, then the packaging parameters will be adjusted.

[0014] In some embodiments, it also includes: If the judgment result of S1012 is negative, then the environmental parameters will be adjusted.

[0015] In some embodiments, adjusting packaging parameters includes: Change the packaging or increase the number of items in a single package.

[0016] A second aspect of the present invention is that it also provides a dynamic weighing system for articles, comprising: A weighing device using a spring, with a force sensor disposed below the spring; the weighing device is used to weigh an item. The weight measurement module is used to acquire the magnitude and direction of multiple deformation forces of the spring at multiple moments, as well as the weight measurement value of the item, through the force sensor. A deformation force input module is used to input the magnitude and direction of multiple deformation forces into a swing feature recognition model, which outputs a recommended offset coefficient. The swing feature recognition model is pre-trained using a swing database, which includes multiple swing sample data, each including the offset coefficient between the measured weight of a sample item and its true value, and the swing feature corresponding to the spring. The swing feature includes at least one of swing speed, swing amplitude, and swing frequency. The swing speed refers to the instantaneous speed and direction of the spring's movement; the swing amplitude refers to the distance the spring travels from its rest position to its maximum deformation position; and the swing frequency refers to the total number of swings the spring makes within a certain time period. The weight correction module is used to correct the weight measurement value using the offset coefficient to obtain a new weight correction value.

[0017] In some embodiments, before entering the weight correction module, the following is also included: The swing degree determination module is used to determine whether the current swing degree is greater than or equal to the first swing degree and less than the second swing degree; wherein, the swing degree refers to the degree of intensity of the spring deviating from its equilibrium position and reciprocating, which is defined by at least one of the swing characteristics; If the swing degree determination module determines the result as yes, then proceed to: The swing degree difference judgment module is used to determine whether the actual swing degree difference between the current swing degree and the historical swing degree is greater than or equal to the preset swing degree difference. Return to the judgment module, which is used to return to the swing degree difference judgment module if the judgment result of the swing degree difference judgment module is yes.

[0018] Beneficial technical effects: This invention proposes a dynamic compensation mechanism for weighing scenarios using springs. This dynamic compensation mechanism includes at least two aspects: first, numerical compensation, i.e., correcting the weight measurement value based on an offset coefficient; and second, the selection and switching of weighing intervention schemes, i.e., determining how to intervene in the weighing process based on the degree of sway. This invention can effectively overcome the inherent defects of traditional weighing methods in dynamic scenarios, such as poor adaptability and low accuracy, thereby improving the reliability and adaptability of weighing results for non-standard items. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. The elements or parts in the drawings are not necessarily drawn to scale. Obviously, the drawings described below are some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0020] Figure 1 A flowchart illustrating a dynamic weighing method for articles provided by the present invention; Figure 2 A schematic flowchart of the weighing correction method provided by the present invention; Figure 3 Another schematic diagram of the weighing correction method provided by the present invention; Figure 4 This is a schematic flowchart of the weighing compensation method for temperature changes provided by the present invention. Figure 5 This is a schematic diagram of the structure of a dynamic weighing system for articles provided by the present invention; Figure 6 This is a schematic diagram of the weighing correction system provided by the present invention; Figure 7 A schematic diagram of the weighing compensation system for temperature changes provided by the present invention; Figure 8 This is a schematic block diagram of the structure of a computer device provided by the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0022] In this document, suffixes such as "module," "part," or "unit" used to denote elements are used only for the purpose of illustrative purposes and have no specific meaning in themselves. Therefore, "module," "part," or "unit" may be used interchangeably.

[0023] In this document, the terms "upper," "lower," "inner," "outer," "front," "rear," "one end," and "the other end," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the present invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0024] In this document, unless otherwise explicitly specified and limited, the terms "installed," "equipped with," "connected," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, a direct connection, or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0025] In this document, "and / or" includes any and all combinations of one or more of the listed related items.

[0026] In this article, "multiple" means two or more, that is, it includes two, three, four, five, etc.

[0027] As used in this specification, the term "about" typically means + / -5% of the value, more typically + / -4% of the value, more typically + / -3% of the value, more typically + / -2% of the value, even more typically + / -1% of the value, and even more typically + / -0.5% of the value.

[0028] In this specification, certain embodiments may be disclosed in a range-bound format. It should be understood that this "range-bound" description is merely for convenience and brevity and should not be construed as a rigid limitation on the disclosed range. Therefore, the description of a range should be considered as having specifically disclosed all possible subranges and the individual numerical values ​​within those ranges. For example, a description of the range 1-6 should be considered as having specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6, etc., and the individual numbers within those ranges, such as 1, 2, 3, 4, 5, and 6. This rule applies regardless of the breadth of the range.

[0029] This invention provides an intelligent weighing solution for industrial batch weighing scenarios. This weighing solution can be applied to various weighing fields such as wafer manufacturing, food processing, pharmaceutical / health product bottling lines, small chemicals, and plastic parts sampling inspection.

[0030] For example, wafers are often weighed to determine if the quantity is accurate when they leave the factory. For instance, multiple wafers are placed in a standard wafer cassette, and the total weight of all the wafers in the cassette is quickly calculated by subtracting the known weight of the empty cassette, thus confirming that the quantity is correct.

[0031] For example, in a plastic parts processing plant, it is necessary to regularly conduct batch sampling and weighing of injection-molded plastic parts.

[0032] Example 1: In some embodiments, see Figure 1 This invention provides a dynamic weighing method for articles (or a weight correction method), comprising: S101, placing an item on a weighing device using a spring, with a force sensor disposed below the spring; In some embodiments, static calibration can be performed using standard weights before weighing.

[0033] S102, the force sensor acquires the magnitude and direction of multiple deformation forces of the spring at multiple moments, as well as the weight measurement value of the item; S103, the magnitude and direction of the multiple deformation forces are input into the swing feature recognition model, and the swing feature recognition model outputs a recommended offset coefficient accordingly; wherein, the swing feature recognition model is pre-trained using a swing database, the swing database includes: multiple swing sample data, and the swing sample data includes: the offset coefficient between the measured weight value and the true value of the sample item, and the swing feature corresponding to the spring, the swing feature including: at least one of swing speed, swing amplitude, and swing frequency; Wherein, the swing speed refers to the instantaneous speed and direction of the spring's movement (or, the swing speed refers to the instantaneous state of the spring's movement, which includes speed and direction); the swing amplitude refers to the distance the spring travels from its rest position to its maximum deformation position; and the swing frequency refers to the total number of swings the spring makes within a certain time period. S104, The weight measurement value is corrected using the offset coefficient to obtain a new weight correction value.

[0034] Alternatively, in some embodiments, the swing feature recognition model can directly output a weight correction value. The swing feature recognition model is pre-trained using a swing database, which includes multiple swing sample data sets, each containing: the measured weight of a sample item, its true weight, and the corresponding swing feature of the spring. The measured weight and swing feature serve as input data, and the true weight serves as output data.

[0035] In some embodiments, the force sensor used is a multi-dimensional force sensor, such as a three-dimensional (X / Y / Z axis) or six-dimensional (three-dimensional force + three-dimensional torque) force sensor, which can acquire the magnitude and direction of the deformation force during spring oscillation in real time. The oscillation frequency, amplitude, or speed of the spring can be calculated from the magnitude and direction of the spring's deformation force. On fully automated production lines for non-standard goods warehousing, item weighing is a core component for precise control and optimized management of material handling.

[0036] The applicant discovered that in the actual weighing process of items (especially non-standard items, i.e. items without strict uniform specifications, dimensions, and weight, as well as small objects), the weight measurement value is distorted due to the impact force generated when the item is placed, the mechanical vibration of the conveying equipment (such as the conveyor belt) itself, and the spring shaking caused by environmental factors (such as wind).

[0037] For example, some non-standard items often have irregular shapes, such as bulging packages in logistics or irregularly shaped parts in machinery manufacturing. These non-standard items often lack a regular shape. Therefore, when placed on a conventional weighing device, uneven contact points and a shift in the center of gravity can easily cause the weighing device to vibrate (especially causing the spring to vibrate or swing). This vibration will result in a certain degree of fluctuation in the measured weight value, making it inaccurate. For example, for flexible non-standard items such as soft-pack clothing and bulk fabrics, deformation may occur during weighing due to different placement methods, such as differences in the degree of folding and the tightness of stacking. This may also cause the weighing device to vibrate, thus introducing errors.

[0038] Therefore, this invention proposes a dynamic compensation mechanism for weighing scenarios using springs. This dynamic compensation mechanism includes at least two aspects: first, numerical compensation, that is, correcting the weight measurement value according to the offset coefficient; second, the selection and switching of weighing intervention schemes, that is, determining how to intervene in the weighing based on the degree of sway. This invention can effectively overcome the inherent defects of traditional weighing methods in dynamic scenarios, such as poor adaptability and low accuracy, thereby improving the reliability and adaptability of weighing results for non-standard items.

[0039] The following will explain this dynamic compensation mechanism in detail: In some embodiments, the weight of an item can be calculated based on the magnitude and direction of the spring deformation force obtained by the force sensor.

[0040] For example, at the first moment, the direction of the spring deformation force is downward to the right. After orthogonal decomposition of the deformation force, the corresponding weight measurement value at the first moment can be calculated based on the spring deformation force. At the second moment, the direction of the spring deformation force is downward to the left. After orthogonal decomposition of the deformation force, the corresponding weight measurement value at the second moment can be calculated based on the spring deformation force, and so on. Multiple weight measurement values ​​corresponding to multiple deformation forces can be calculated. Furthermore, the average of multiple weight measurement values ​​can be used as the weight measurement value of the item. Even further, the degree of spring oscillation can be used to determine whether the weight measurement value needs to be corrected, and how the correction factor should be selected.

[0041] In some embodiments, from the moment an item is placed on the weighing device, the spring begins to oscillate until the weight measurement stabilizes and the spring comes to rest (or is in equilibrium). The oscillation of the spring continues for a period of time, and the force sensor collects forces at multiple moments accordingly. However, the force measured by the sensor changes during the spring's oscillation, so the weight measurement can be compensated for by an oscillation feature recognition model based on the magnitude and direction of multiple deformation forces.

[0042] In some embodiments, the oscillation characteristic is used to characterize the dynamic properties of a spring during the entire weighing process, from the start of oscillation, continuous oscillation, to the gradual weakening of oscillation after being loaded with weight. It can be quantified from multiple dimensions such as oscillation speed, oscillation amplitude, and oscillation frequency.

[0043] For example, as an exemplary embodiment, the present invention employs a sequence-to-scalar regression model based on LSTM (Long Short-Term Memory) to learn the vibration modes of a spring. The model includes: The input layer receives a sequence (which consists of oscillation features, weight measurements, and true values ​​over a period of time); the LSTM layer learns vibration patterns from the sequence; the fully connected layer uses cellular mechanisms to learn dependencies and maps them to specific values; and the output layer contains a linear activation function to output the predicted offset coefficients.

[0044] For example, in some embodiments, the present invention can also directly output the corrected weight value through the model. Specifically, the oscillation characteristics of the spring (preferably the oscillation speed, amplitude, and frequency over a period of time) and the output value of the weighing device (i.e., the direct weight measurement value) are used as inputs, and the actual weight of the item (which can be measured under standard conditions, such as using a more precise weighing device, or under windless conditions) is used as the output. Thus, the model can automatically learn the complex mapping relationship between the spring vibration mode and the actual weight.

[0045] This embodiment provides an end-to-end model training scheme, wherein the model includes: an input layer for receiving a sequence (which consists of oscillation features and weight measurements over a period of time); an LSTM layer, which utilizes memory cell mechanisms to learn long-term dependencies in the sequence, such as recognizing the periodicity of vibrations; and this layer automatically extracts key temporal features related to vibrations from the sequence; and a fully connected layer that maps the output of the last time step of the LSTM layer (containing an understanding of the entire sequence) to the final weight prediction value. In other words, in this embodiment, the oscillation feature recognition model can directly output the corrected weight value.

[0046] Of course, in other embodiments, the swing feature recognition model can also be trained based on models such as linear regression, decision tree regression, random forest regression, gradient boosting regression tree, support vector regression, or neural network models, and the present invention does not limit this. For example, in some embodiments, those skilled in the art can select a suitable model for training according to actual weighing needs, such as non-standard items of different specifications and types, or different weighing environments.

[0047] In some embodiments, the true weight value in the swing sample data can be collected using a more accurate weighing device.

[0048] In some embodiments, when the swing feature recognition model outputs an offset coefficient, the weight measurement value is corrected using the offset coefficient to obtain a new corrected weight value. This can be achieved by: Weight Corrected Value = Weight Measurement Value × Offset Coefficient. Simultaneously, the offset coefficient directly quantifies the difference between the weight measurement value and the true value, which is helpful for subsequent training of the swing feature recognition model.

[0049] It should be understood that under different weighing scenarios (such as varying wind speeds), items of different weights may exhibit the same swaying characteristics, or items of the same weight may exhibit different swaying characteristics. This invention inputs the offset coefficient between the measured weight of the sample item and its true value, along with the swaying characteristics of the spring, into a swaying feature recognition model. The model can establish a correspondence between the offset coefficient and the swaying characteristics for items of different weights, thus providing a reliable basis for the model to recognize swaying characteristics and enhancing the model's generalization ability.

[0050] In some embodiments, in addition to swing speed, swing amplitude, and swing frequency, the asymmetry of the swing can also be used as a swing characteristic, wherein the asymmetry of the swing refers to the difference between the amplitude of the spring's forward swing and reverse swing.

[0051] In some embodiments, if the oscillation asymmetry is very high, it is likely that the spring is aging or has a quality problem, and the spring should be replaced in time.

[0052] In some embodiments, the degree of oscillation can be quantified using at least one of the oscillation features. For example, if one of the oscillation features exceeds a preset threshold, the degree of oscillation is level one; if two of the oscillation features exceed the preset threshold, the degree of oscillation is level two, and so on. The more oscillation features that exceed the preset threshold, the greater the degree of oscillation.

[0053] Alternatively, in other embodiments, multiple sets of oscillation feature data can be collected, and the degree of oscillation corresponding to each set of oscillation feature data can be scored. The scoring criteria can be preset by the user, such as giving a score of 0-10 for different degrees of oscillation. Subsequently, the oscillation degree recognition model is trained to learn the mapping relationship between oscillation feature data and oscillation degree. It is understood that the learning mode between oscillation features and oscillation degree in this embodiment can still draw on the above-mentioned long short-term memory network mode, which will not be repeated here.

[0054] The magnitude and direction of multiple deformation forces can be input into a swing degree recognition model, which outputs a corresponding assessment of the swing degree. This swing degree recognition model is pre-trained using a swing feature database, which includes multiple swing feature data sets. These data sets include the swing features corresponding to the spring, and a swing degree score or grade assigned by an expert (such as a technical engineer) for each set of swing feature data. The swing degree recognition model learns the mapping relationship between swing features and swing degree using machine learning methods.

[0055] In some embodiments, prior to S104, the method further includes: S105, determine whether the current swing degree is greater than or equal to the first swing degree and less than the second swing degree; wherein, the swing degree refers to the degree of intensity of the spring deviating from its equilibrium position and reciprocating, which is defined by at least one of the swing characteristics; If the result of S105 is yes, then execute: S106, determine whether the actual swing degree difference between the current swing degree and the historical swing degree is greater than or equal to the preset swing degree difference; S107. If the result of the judgment in S106 is yes, then return to S106.

[0056] In some embodiments, it also includes: S108. If the result of the judgment in S106 is negative, then proceed to S104. In some embodiments, it also includes: S109, Determine whether the current swing degree is less than the first swing degree; If so, proceed to S104. In some embodiments, it also includes: S1010, Determine whether the current swing degree is greater than or equal to the second swing degree; If so, a prompt signal will be issued. In some embodiments, it also includes: S1011, Calculate the deviation of the current swing degree of the same batch of items; the deviation degree is used to define the numerical fluctuation of the current swing degree; S1012, determine whether the deviation degree is less than a preset deviation degree; If the judgment result of S1012 is yes, then the packaging parameters will be adjusted.

[0057] In some embodiments, it also includes: If the judgment result of S1012 is negative, then the environmental parameters will be adjusted.

[0058] In some embodiments, adjusting packaging parameters includes: Change the packaging or increase the number of items in a single package.

[0059] In some embodiments, see Figure 5 The present invention also provides a dynamic weighing system for articles, comprising: A weighing device using a spring, with a force sensor disposed below the spring; the weighing device is used to weigh an item. The weight measurement module is used to acquire the magnitude and direction of multiple deformation forces of the spring at multiple moments, as well as the weight measurement value of the item, through the force sensor. A deformation force input module is used to input the magnitude and direction of multiple deformation forces into a swing feature recognition model, which outputs a recommended offset coefficient. The swing feature recognition model is pre-trained using a swing database, which includes multiple swing sample data, each including the offset coefficient between the measured weight of a sample item and its true value, and the swing feature corresponding to the spring. The swing feature includes at least one of swing speed, swing amplitude, and swing frequency. The swing speed refers to the instantaneous speed and direction of the spring's movement; the swing amplitude refers to the distance the spring travels from its rest position to its maximum deformation position; and the swing frequency refers to the total number of swings the spring makes within a certain time period. The weight correction module is used to correct the weight measurement value using the offset coefficient to obtain a new weight correction value.

[0060] Preferably, in some embodiments, the system includes: An item placement module (such as a robot, robotic arm, or other automated device) is used to place items on a weighing device that uses a spring, with a force sensor positioned below the spring. In some embodiments, before entering the weight correction module, the following is also included: The swing degree determination module is used to determine whether the current swing degree is greater than or equal to the first swing degree and less than the second swing degree; wherein, the swing degree refers to the degree of intensity of the spring deviating from its equilibrium position and reciprocating, which is defined by at least one of the swing characteristics; If the swing degree determination module determines the result as yes, then proceed to: The swing degree difference judgment module is used to determine whether the actual swing degree difference between the current swing degree and the historical swing degree is greater than or equal to the preset swing degree difference. Return to the judgment module, which is used to return to the swing degree difference judgment module if the judgment result of the swing degree difference judgment module is yes.

[0061] It should be understood that the aforementioned dynamic weighing system for articles can be used to implement the steps described in any embodiment of the present invention.

[0062] Example 2: In some embodiments, see Figures 2-3 The present invention also proposes a weighing correction method, including: S201, placing an item on a weighing device using a spring, with a force sensor disposed below the spring; S202, the force sensor acquires the magnitude and direction of multiple deformation forces of the spring at multiple moments, as well as the weight measurement value of the item; S203, determine whether the current swing degree of the spring is greater than or equal to the first swing degree and less than the second swing degree; Wherein, the current degree of oscillation refers to the degree of intensity of the spring's deviation from its equilibrium position and its reciprocating motion, which is defined by at least one of oscillation speed, oscillation amplitude, and oscillation frequency; wherein, the oscillation speed refers to the instantaneous speed and direction of the spring's motion; the oscillation amplitude refers to the distance the spring travels from its rest position to its maximum deformation position; and the oscillation frequency refers to the total number of times the spring oscillates within a certain period of time; If the result of S203 is yes, then execute: S204, determine whether the actual swing degree difference between the current swing degree and the historical swing degree is greater than or equal to the preset swing degree difference; wherein, the historical swing degree is a statistical value calculated based on the swing degree dataset of the same batch of items before the current moment; If the result of S204 is yes, then execute: S205, return to S204; If the result of S204 is negative, then execute: S206, The weight measurement value is corrected according to the current degree of sway, and a new weight correction value is output.

[0063] In some embodiments, if the current degree of oscillation falls within the range of being greater than or equal to the first degree of oscillation and less than the second degree of oscillation, it may indicate that the current degree of oscillation has exceeded the range of slight oscillation, but has not yet reached the level of violent or out-of-control oscillation.

[0064] In some embodiments, it can be inferred whether the current swing difference is long-term or intermittent by determining whether the actual swing difference exceeds a preset swing difference, and thus infer whether the current swing is normal or abnormal. That is, if the actual swing difference is greater than or equal to the preset swing difference, it may indicate that the current swing exceeds the acceptable level and is an intermittent abnormality; conversely, if the actual swing difference is less than the preset swing difference, it indicates that the current swing is still within the acceptable level and is not an intermittent abnormality.

[0065] The historical oscillation degree can be the average oscillation degree of the current batch of items, or other representative statistical values ​​calculated based on the oscillation degree dataset of the same batch of items before the current moment, such as the median.

[0066] It should be understood that if the actual difference in sway degree is greater than or equal to the preset difference in sway degree, it indicates that the sway degree of the current item during weighing differs significantly from the sway degree of historical items during weighing within the same batch. This may indicate that the weighing environment of the current item fluctuates due to some occasional anomalies, leading to measurement errors. For example, occasional anomalies may be caused by wind due to opening or closing doors. Especially for some small, non-standard items, a gust of wind can cause their center of gravity to become unstable and shift. Another example is that occasional anomalies may be caused by equipment failure on the production line, such as a malfunction in the weighing device (e.g., loose mounting screws) or a potential problem with the conveyor belt (e.g., damaged conveyor belt bearings causing periodic jamming).

[0067] To address these unforeseen factors, it is preferable for engineers to maintain environmental conditions, such as by closing doors or windows to ensure a more stable gas flow, thus reducing the difficulty of correcting errors caused by airflow fluctuations. Another example is to conduct hazard inspections on production line equipment to prevent potential problems from escalating and causing greater impact.

[0068] In other words, by analyzing abnormalities in the degree of oscillation, this invention can provide more accurate and reliable measurement results for the product through an intelligent correction mechanism. Furthermore, it can reasonably retain abnormal situations (such as accidental factors), providing engineers with diagnostic information and preventing the intelligent correction mechanism from masking potential problems in production line equipment or the operating environment.

[0069] As mentioned earlier, the judgment mechanism based on the degree of oscillation in this embodiment can avoid hiding early signs of major problems (such as equipment failure, raw material quality defects, improper operation, etc.) through correction, and instead can investigate occasional problems in advance (such as suspending the production line and conducting rapid equipment screening).

[0070] In some embodiments, if the actual swing difference is less than a preset swing difference, it indicates that the swing of items in the same batch is relatively consistent, meaning that the actual swing of the object may be within a tolerable and reasonable range. In this case, it is preferable to make synchronous corrections. That is, this embodiment, by making an overall comparison of the actual swing of a batch of items, can improve the tolerance for errors to a certain extent, while providing timely warnings for possible occasional anomalies.

[0071] In other words, in this embodiment, the ultimate goal of weighing is to improve the production pass rate. As long as the weight error can be controlled within a reasonable and controllable range, the weighing error problem can be solved in other ways that have the least impact on the production line (such as uniform error compensation). That is, there is no need to interrupt production in order to pursue the absolute accuracy of the weighing data.

[0072] In summary, unlike weighing methods that pursue absolute accuracy, this invention prefers to maintain optimal overall efficiency amidst dynamic changes in weighing data, that is, to ensure the overall quality and continuity of weighing results output at the lowest cost, thereby achieving an optimal balance between accuracy and efficiency.

[0073] In some embodiments, if the actual oscillation difference is greater than or equal to the preset oscillation difference, the production line can be suspended until an occasional abnormality (such as a sudden gust of wind) occurs and the production stops.

[0074] Alternatively, an anomaly alert can be sent to production line personnel, who can then investigate the problem until the actual oscillation difference is less than the preset oscillation difference.

[0075] In some embodiments, if the actual swing difference is less than a preset swing difference, a correction value for the weight measurement can be directly output.

[0076] In some embodiments, it also includes: S207, Determine whether the current swing degree is less than the first swing degree; If so, proceed to S206.

[0077] In other words, if the current degree of oscillation is less than the first degree of oscillation, the correction value for the weight measurement can be directly output.

[0078] The weight measurement value can be corrected based on the offset coefficient output by the swing feature identification model.

[0079] In some embodiments, it also includes: S208, determine whether the current swing degree is greater than or equal to the second swing degree; If so, a prompt signal will be issued.

[0080] In other words, if the current degree of oscillation is greater than or equal to the second degree of oscillation, a warning signal can be issued to remind production line personnel to investigate the problem of this weighing.

[0081] In some embodiments, it also includes: S209, calculate the deviation of the current swing degree of the items in the same batch; the deviation degree is used to define the numerical fluctuation of the current swing degree; S210, determine whether the deviation is less than a preset deviation; If the judgment result of S210 is yes, then the packaging parameters are adjusted; including: changing the packaging or increasing the number of items in a unit package.

[0082] In some embodiments, the deviation of the current swing degree can be calculated by obtaining the current swing degree sequence of items in the same batch (e.g., the swing degree scores of five products are 4, 2, 7, 9, and 1 respectively), and the deviation degree can be calculated by the standard deviation, variance, range, etc. of the swing degree sequence.

[0083] In some embodiments, the preset deviation level is a quantitative threshold set based on statistical analysis of historical weighing data. It is used to measure the fluctuation or regularity of the swaying characteristics of the current batch of items. If the deviation level of the current batch of items exceeds the preset deviation level, it can be determined that the swaying fluctuation of the current batch of items is large, and different weighing correction schemes can be selected accordingly. Specifically, users can set different preset deviation levels for different weighing scenarios.

[0084] In some embodiments, if the deviation is less than a preset deviation, the packaging parameters can be adjusted; this includes changing the packaging or increasing the number of items in a unit package. This allows for effective improvement of overall weighing stability and consistency by optimizing the packaging parameters, an external variable, without altering the items themselves.

[0085] For example, by increasing the weight of the packaging or by increasing the number of items to increase the weight, the interference of airflow on non-standard weighed items can be reduced to some extent, thus preventing items from being too light and swaying in the wind.

[0086] In some embodiments, if the deviation is less than a preset deviation, it may indicate that the spring's oscillation is relatively regular when weighing the batch of items, or that the fluctuation in the oscillation of the items is small. In this case, the spring's oscillation amplitude can be relatively reduced by changing the packaging material or increasing the number of items in a unit package.

[0087] In some embodiments, it also includes: If the judgment result of S210 is negative, then the environmental parameters will be adjusted.

[0088] In some embodiments, if the deviation is greater than or equal to a preset deviation, it may indicate that the spring's oscillation is irregular when weighing the batch of items, or that the oscillation of the items fluctuates significantly. This usually indicates that external environmental disturbances are the dominant factor causing measurement distortion, rather than problems with the items themselves or their packaging. In this case, environmental parameters can be adjusted, such as temporarily closing doors and windows, to quickly eliminate or reduce random and irregular external environmental interference, providing a relatively stable measurement environment for weighing. This fundamentally reduces the fluctuation of the oscillation and ensures the reliability of subsequent measurement data.

[0089] In some embodiments, see Figure 6 The present invention also provides a weighing correction system, comprising: An item placement module for placing items on a weighing device using a spring, with a force sensor disposed below the spring; The weight measurement module is used to acquire the magnitude and direction of multiple deformation forces of the spring at multiple moments, as well as the weight measurement value of the item, through the force sensor. The first swing degree determination module is used to determine whether the current swing degree of the spring is greater than or equal to the first swing degree and less than the second swing degree; wherein, the current swing degree refers to the degree of intensity of the spring's deviation from its equilibrium position and its reciprocating motion, which is defined by at least one of swing speed, swing amplitude, and swing frequency; wherein, the swing speed refers to the instantaneous speed and direction of the spring's motion; the swing amplitude refers to the distance the spring travels from its rest position to its maximum deformation position; and the swing frequency refers to the total number of swings the spring makes within a certain time period. If the first swing degree judgment module determines the result as yes, then proceed to: The swing degree difference judgment module is used to determine whether the actual swing degree difference between the current swing degree and the historical swing degree is greater than or equal to a preset swing degree difference; wherein, the historical swing degree is a statistical value calculated based on the swing degree dataset of the same batch of items before the current moment; If the judgment result of the swing degree difference judgment module is yes, then proceed to: The return judgment module is used to return S204; If the judgment result of the swing degree difference judgment module is negative, then proceed to: The weight correction module is used to correct the weight measurement value according to the current degree of sway and output a new weight correction value.

[0090] In some embodiments, it also includes: The second swing degree judgment module is used to determine whether the current swing degree is less than the first swing degree; If so, proceed to the weight correction module.

[0091] In some embodiments, it also includes: The third swing degree judgment module is used to determine whether the current swing degree is greater than or equal to the second swing degree; If so, a prompt signal will be issued.

[0092] It should be understood that the weighing correction system can be used to implement the steps described in any embodiment of the present invention.

[0093] In some embodiments, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the weighing correction method as described in any one of the present invention.

[0094] In some embodiments, the present invention also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the weighing correction method as described in any one of the present invention.

[0095] Example 3: The applicant noted that in a weighing system that includes springs and force sensors, temperature changes can also cause significant measurement errors: firstly, the spring constant changes with temperature fluctuations, resulting in different deformations under the same load; secondly, the force sensor itself has zero point and sensitivity temperature drift, causing the output signal to deviate from the true value; and thirdly, the thermal expansion and contraction of the mechanical structure introduces slight deformations, interfering with the normal transmission of force.

[0096] In scenarios with multiple intersecting error factors, the difficulty of accurate weighing increases significantly. To address this, this embodiment preferably provides a restrictive temperature correction scheme to reduce or avoid introducing excessive errors during correction, while simultaneously improving weighing accuracy.

[0097] Therefore, please see Figure 4 The present invention also provides a weighing compensation method for temperature changes, including: S301, placing an item on a weighing device using a spring, with a force sensor disposed below the spring; S302, the weight measurement value of the item is obtained through the force sensor; S303, Obtain the current rate of temperature change; the rate of temperature change is calculated based on the amount of temperature change per unit time. S304, determine whether the temperature change rate is less than the temperature change rate threshold; If the result of S304 is yes, then execute: S305, The weight measurement value and the current temperature are input into the temperature compensation model, and the temperature compensation model outputs the corrected weight value accordingly; In some embodiments, the temperature can be the measured ambient temperature.

[0098] In some embodiments, it also includes: If the judgment result of S304 is negative, the weight measurement value will be output directly.

[0099] The applicant noted that the spring constant (or elastic deformation state) varies under different rates of temperature change.

[0100] To address this, the present invention proposes a restrictive weighing compensation scheme based on different rates of temperature change. Specifically, when the rate of temperature change is high (or greater than or equal to a temperature change rate threshold), no correction is made to the weight measurement value to avoid the difficulty in effectively predicting the elastic coefficient when the temperature change rate is high. Meanwhile, when the rate of temperature change is low (or less than the temperature change rate threshold), the elastic coefficient or elastic change state of the spring is stable (or predictable), and compensation can preferably be made to the weight measurement value.

[0101] In other words, the present invention preferably enables compensation under operating conditions where the temperature changes slowly and the change in the spring's elastic coefficient is predictable, thereby effectively ensuring the applicability of the temperature compensation model.

[0102] In some embodiments, it also includes: S306, Get the current temperature value; S307, determine whether the current temperature value is greater than a first temperature threshold or less than a second temperature threshold; If the result of S307 is yes, then S305 is executed.

[0103] In some embodiments, if the determination result of S307 is yes, then S305 is allowed to be executed.

[0104] In some embodiments, it also includes: If the judgment result of S307 is negative, the weight measurement value is directly output.

[0105] It should be understood that this invention employs a restrictive compensation mechanism based on temperature thresholds (a first temperature threshold and a second temperature threshold). Specifically, compensation is only performed on the weight measurement value when the temperature is within a high-temperature range (greater than the first temperature threshold) or a low-temperature range (less than the second temperature threshold). When the temperature is within the normal temperature range between the first and second temperature thresholds, the compensation process is not initiated. This restrictive compensation mechanism effectively solves the problems of logical complexity and accuracy interference caused by uniform compensation across the entire temperature range. By focusing on core compensation scenarios and eliminating invalid compensation requirements, the design difficulty and calibration complexity of the compensation algorithm are significantly reduced, improving the efficiency and accuracy of compensation execution.

[0106] In some embodiments, it also includes: S308, Calculate the degree of deviation of the weight measurement values ​​of the same batch of items; the degree of deviation is used to define the numerical fluctuation of the weight measurement values; S309, determine whether the degree of deviation is greater than or equal to a preset degree of deviation; If the result of S309 is yes, then execute: S310 adjusts environmental parameters.

[0107] The degree of deviation of the weight measurement value can be calculated by referring to the deviation of the swing degree. For example, if the weight measurement value sequence of the same batch of items is recorded (such as the weight measurement values ​​(in g) of five products are 400, 420, 398, 409, and 401 respectively), the degree of deviation can be calculated by the standard deviation, variance, range, etc. of the weight measurement value sequence.

[0108] In some embodiments, if the deviation of the weight measurement value is greater than or equal to the preset deviation, it may indicate that the weighing results of the batch of items are irregular or fluctuate greatly. In this case, adjusting the environmental parameters (such as closing doors and windows) can effectively eliminate environmental interference, provide a stable environment for weighing, and improve the accuracy of the weighing results.

[0109] In some embodiments, it also includes: If, after executing S310, the deviation degree is still greater than or equal to the preset deviation degree, then execute: S311, replace the spring.

[0110] In some embodiments, if the deviation of the weight measurement value is still greater than or equal to the preset deviation after adjusting the environmental parameters, it indicates that the inaccuracy of the weighing result is not due to interference from external environmental factors, but rather to the aging of the spring itself or other defects. In this case, replacing the spring can fundamentally eliminate the systematic error caused by spring fatigue or internal structural damage, thereby ensuring the basic accuracy of the weighing measurement, avoiding the compensation algorithm from continuously making ineffective adjustments based on the failed hardware, and ensuring the long-term stability and measurement accuracy of the weighing result from the source.

[0111] It should be understood that this invention proposes a weighing compensation method based on the principle of local minimum intervention, specifically in the following aspects: 1) The weighing intervention scheme is switched according to the real-time swing degree of the spring (such as replacing the spring, adjusting environmental parameters, adjusting packaging parameters, etc.), which can make targeted adjustments based on the underlying causes of the spring swing, creating a relatively stable weighing environment at the lowest cost, rather than stopping the entire line to investigate the problem, which can greatly reduce the impact on the normal operation of the production line; 2) When the swing degree is small, or the swing degree is in the medium range but is judged not to be an occasional abnormality, it is preferable to directly output the weight correction value, which can avoid excessive intervention in the production line, that is, selective intervention under specific circumstances to adapt to the high-speed production rhythm of the production line; 3) Selective compensation or no compensation is performed according to the rate of temperature change and the temperature range, which can improve the relative stability and relative accuracy of weighing results under different ambient temperatures.

[0112] In summary, this invention does not stop the production line whenever an anomaly occurs, nor does it indiscriminately compensate for all weight measurement results. Instead, it adopts the principle of local minimum intervention and classifies and handles abnormal situations in weighing scenarios in a hierarchical manner (such as setting corresponding optimal response plans for different degrees of oscillation, different rates of temperature change, and different temperatures). This can ensure the accuracy of weighing results while maintaining the operation of the production line to the maximum extent.

[0113] In some embodiments, considering that temperature affects the spring constant and that the force sensor also experiences temperature drift due to temperature changes, a temperature compensation model can be trained using the following steps: Step 1: Data Acquisition and Calibration Place the entire weighing device (including springs, force sensors, etc.) into a controlled temperature chamber. Install a high-precision temperature sensor to measure the ambient temperature.

[0114] Under both no-load and standard load (e.g., weights), the weighing chamber was used to traverse multiple temperature points (e.g., -10℃, 0℃, 20℃, 40℃, 60℃). After stabilization at each temperature point, the weight measurement and current temperature of the weighing device were recorded.

[0115] Step Two: Calculate multiple correction coefficients at different temperature points based on multiple sets of weight measurements and actual weight values, and fit the functional relationship between temperature and spring elasticity coefficient based on the multiple correction coefficients at different temperature points (multinomial or machine learning algorithms can be used for modeling).

[0116] It should be understood that the correction coefficient will be dynamically adjusted as the elastic coefficient changes. When the temperature change causes the elastic coefficient to deviate from the standard value (such as k0 at room temperature), a weight measurement deviation will occur. The present invention preferably learns the mapping relationship between temperature and elastic coefficient change through a temperature compensation model. With the help of the temperature compensation model, the elastic coefficient at the corresponding temperature can be determined, thereby calculating a correction coefficient that is more in line with the actual weighing scenario.

[0117] In some embodiments, the weight measurements of the sample item at multiple temperature points can be repeatedly collected, and the average of the multiple weight measurements can be taken as the weight measurement of the sample item.

[0118] In some embodiments, the present invention can also be applied to an intelligent conveyor belt system, wherein the intelligent conveyor belt system can construct a fully automated production line for non-standard items entering the warehouse by integrating weighing and labeling functions (the items enter the labeling area at a constant speed through the conveyor belt, the labeling wheel pastes the label onto the fixed position surface of the item, and the item is automatically weighed and labeled on the conveyor belt), realizing real-time synchronization of weight data and label information, controlling the error within 0.1kg, and realizing precise control and optimized management of material conveying.

[0119] In some embodiments, the weighing posture recognition can be achieved through the perception and positioning functions of an intelligent visual positioning system (such as a camera), thereby reducing measurement errors caused by improper placement.

[0120] In some embodiments, before placing the materials to be conveyed into the intelligent conveyor system in accordance with a prescribed method, it is necessary to ensure that the materials are evenly distributed, without accumulation or cross-contamination.

[0121] In some embodiments, the weight measurement value or weight correction value can be written into the RFID (Radio Frequency Identification) chip and uploaded to the WMS (Warehouse Management System) via the MODBUS protocol. When the item enters the labeling area via the conveyor belt, the labeling wheel will stick the label to the fixed position surface of the item.

[0122] In some embodiments, before the label is generated, the RFID label paper can be connected to the printer, the printer automatically generates the label, and the label is printed using a thermal transfer method.

[0123] In some embodiments, RFID tag position calibration can be performed before the tag is generated, for example, by using a printer panel to calibrate the distance parameter from the RFID chip to the edge of the tag to ensure uniform signal distribution.

[0124] In some embodiments, the Modbus protocol is a serial communication protocol, an industry standard for communication protocols in the industrial field, and is now a commonly used connection method between industrial electronic devices.

[0125] In some embodiments, a WMS (warehouse management system) is a real-time computer software system that can efficiently manage information, resources, behaviors, inventory, and distribution operations according to the business rules and algorithms in operation.

[0126] In some embodiments, the WMS system can allocate the optimal storage location for materials based on label information and historical storage location, and complete the warehousing and shelving operation.

[0127] In some embodiments, a PLC (Programmable Logic Controller) can be used to control and manage the start-up, stop, and speed adjustment of the conveyor belt.

[0128] In some embodiments, a thermal imager can be used to detect the temperature of the conveyor belt in real time, and power can be cut off in case of abnormality.

[0129] In some embodiments, RFID (Radio Frequency Identification) is a type of automatic identification technology that uses wireless radio frequency for non-contact two-way data communication and reads and writes recording media (electronic tags or RFID cards) to achieve the purpose of identifying targets and exchanging data.

[0130] It should be understood that, with the help of this invention, the accuracy and efficiency of weighing can be improved in two ways: 1) High-precision weighing: Using digital sensors and calibration technology, the error is controlled within ±0.1%, far exceeding the accuracy of traditional mechanical scales. 2) Automated process: Through RFID (Radio Frequency Identification) technology, automatic weighing and data recording of goods are achieved. Through sensors and intelligent control systems, the entire process of label conveying, positioning, and affixing is automated, reducing manual intervention, improving efficiency, and lowering labor management costs. The intelligent conveyor belt can operate 24 hours a day, increasing the turnover speed by more than 50%. 3) Precise sorting and reduced error rate: Integrating RFID and visual recognition systems, label information can be read in batches, with a misread rate of less than 0.1%, far superior to manual sorting. On the other hand, the scalability of this invention (i.e., applicable to multiple industries such as food, daily chemicals, and electronics; and can be integrated with management systems such as ERP and WMS) can also be used to achieve collaborative operations, real-time inventory tracking, and operational visualization.

[0131] In some embodiments, the intelligent conveyor belt system can use RFID technology to read tags in batches (e.g., dozens per second) and automatically synchronize weighing data in real time; during logistics sorting, it can automatically guide packages to the corresponding areas, and complete verification simultaneously upon outbound shipment. Meanwhile, the RFID tag misread rate is less than 0.1%, and the weighing data can be largely accurate through high-precision sensors and algorithms; furthermore, monitoring can be integrated to prevent tampering.

[0132] Compared with ordinary tags, RFID tags have the following technological advantages: Table 1: Comparison of RFID Tags and Ordinary Tags In some embodiments, see Figure 7 The present invention also provides a weighing compensation system for temperature changes, comprising: An item placement module for placing items on a weighing device using a spring, with a force sensor disposed below the spring; A weight acquisition module is used to acquire the weight measurement value of the item through the force sensor; A temperature change rate acquisition module is used to acquire the current temperature change rate; the temperature change rate is calculated based on the amount of temperature change per unit time. A temperature change rate determination module is used to determine whether the temperature change rate is less than a temperature change rate threshold. If the temperature change rate determination module determines the result as yes, then proceed to: The weight correction value output module is used to input the weight measurement value and the current temperature into the temperature compensation model, and the temperature compensation model outputs the corrected weight correction value accordingly. The temperature compensation model is obtained by pre-fitting and calculating using a temperature change database, which includes: weight values ​​of multiple sets of sample items with different weights. Each set of sample item weight values ​​includes: the temperature under measurement conditions, the actual weight of the item, and the weight measurement value obtained through testing.

[0133] In some embodiments, it also includes: Temperature value acquisition module, used to acquire the current temperature value; The temperature value determination module is used to determine whether the current temperature value is greater than a first temperature threshold or less than a second temperature threshold; If the temperature value judgment module determines the result as yes, then proceed to the weight correction value output module.

[0134] It should be understood that the weighing compensation system for temperature changes can be used to implement the steps described in any embodiment of the present invention.

[0135] In some embodiments, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the weighing compensation method for temperature changes as described in any one of the present invention.

[0136] In some embodiments, the present invention also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the weighing compensation method for temperature changes as described in any one of the present invention.

[0137] Embodiments of this application also provide a computer device; please refer to [link to previous document]. Figure 8 Computer programs can be used in situations such as Figure 8 It runs on the computer device shown. Figure 8 As shown, the computer device includes a processor, memory, and network interface connected via a system bus, wherein the memory may include non-volatile storage media and internal memory.

[0138] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions that, when executed, cause a processor to perform the steps described in any embodiment of the present invention.

[0139] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0140] The internal memory provides an environment for the execution of a computer program in a non-volatile storage medium, which, when executed by a processor, enables the processor to perform the steps described in any embodiment of the present invention.

[0141] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0142] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0143] The processor is used to run a computer program stored in a memory to perform the steps described in any embodiment of the present invention.

[0144] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0145] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product (or it can be called a computer program product) is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a computer terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0146] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for dynamic weighing of an item, characterized in that, include: S101, placing an item on a weighing device using a spring, with a force sensor disposed below the spring; S102, the magnitude and direction of multiple deformation forces of the spring at multiple moments are obtained through the force sensor, and the multiple deformation forces are orthogonally decomposed to calculate the corresponding multiple weight measurement values. The average of the multiple weight measurements is taken as the weight measurement of the item. S103, the magnitude and direction of the multiple deformation forces are input into the swing feature recognition model, and the swing feature recognition model outputs a recommended offset coefficient accordingly; wherein, the swing feature recognition model is pre-trained using a swing database, the swing database includes: multiple swing sample data, and the swing sample data includes: the offset coefficient between the measured weight value and the true value of the sample item, and the swing feature corresponding to the spring, the swing feature including: at least one of swing speed, swing amplitude, and swing frequency; Wherein, the swing speed refers to the instantaneous speed and direction of the spring's movement; the swing amplitude refers to the distance the spring travels from its rest position to its maximum deformation position; and the swing frequency refers to the total number of swings the spring makes within a certain time period. S104, The weight measurement of the item is corrected using the offset coefficient to obtain a new weight correction value.

2. The method for dynamic weighing of articles according to claim 1, characterized in that, Before S104, it also includes: S105, determine whether the current swing degree is greater than or equal to the first swing degree and less than the second swing degree; wherein, the swing degree refers to the degree of intensity of the spring deviating from its equilibrium position and reciprocating, which is defined by at least one of the swing characteristics; If the result of S105 is yes, then execute: S106, determine whether the actual swing degree difference between the current swing degree and the historical swing degree is greater than or equal to the preset swing degree difference; S107. If the result of the judgment in S106 is yes, then return to S106.

3. The dynamic weighing method for articles according to claim 2, characterized in that, Also includes: S108. If the result of the judgment in S106 is negative, then proceed to S104.

4. The dynamic weighing method for articles according to claim 1, characterized in that, Also includes: S109, Determine whether the current swing degree is less than the first swing degree; If so, proceed to S104.

5. The method for dynamic weighing of articles according to claim 1, characterized in that, Also includes: S1010, Determine whether the current swing degree is greater than or equal to the second swing degree; If so, a prompt signal will be issued.

6. The method for dynamic weighing of articles according to claim 5, characterized in that, Also includes: S1011, Calculate the degree of deviation of the current swing degree of the same batch of items; The degree of deviation is used to define the numerical fluctuation of the current degree of oscillation; S1012, determine whether the deviation degree is less than a preset deviation degree; If the judgment result of S1012 is yes, then the packaging parameters will be adjusted.

7. The dynamic weighing method for articles according to claim 6, characterized in that, Also includes: If the judgment result of S1012 is negative, then the environmental parameters will be adjusted.

8. The method for dynamic weighing of articles according to claim 6, characterized in that, Adjustments were made to the packaging parameters, including: Change the packaging or increase the number of items in a single package.

9. A dynamic weighing system for goods, characterized in that, include: A weighing device using a spring, with a force sensor disposed below the spring; the weighing device is used to weigh an item. The weight measurement module is used to acquire the magnitude and direction of multiple deformation forces of the spring at multiple moments through the force sensor, and to perform orthogonal decomposition on the multiple deformation forces to calculate the corresponding multiple weight measurement values. The average of the multiple weight measurements is taken as the weight measurement of the item. A deformation force input module is used to input the magnitude and direction of multiple deformation forces into a swing feature recognition model, which outputs a recommended offset coefficient. The swing feature recognition model is pre-trained using a swing database, which includes multiple swing sample data, each including the offset coefficient between the measured weight of a sample item and its true value, and the swing feature corresponding to the spring. The swing feature includes at least one of swing speed, swing amplitude, and swing frequency. The swing speed refers to the instantaneous speed and direction of the spring's movement; the swing amplitude refers to the distance the spring travels from its rest position to its maximum deformation position; and the swing frequency refers to the total number of swings the spring makes within a certain time period. The weight correction module is used to correct the measured weight of the item using the offset coefficient to obtain a new weight correction value.

10. A dynamic weighing system for articles according to claim 9, characterized in that, Before entering the weight correction module, it also includes: The swing degree determination module is used to determine whether the current swing degree is greater than or equal to the first swing degree and less than the second swing degree; wherein, the swing degree refers to the degree of intensity of the spring deviating from its equilibrium position and reciprocating, which is defined by at least one of the swing characteristics; If the swing degree determination module determines the result as yes, then proceed to: The swing degree difference judgment module is used to determine whether the actual swing degree difference between the current swing degree and the historical swing degree is greater than or equal to the preset swing degree difference; Return to the judgment module, which is used to return to the swing degree difference judgment module if the judgment result of the swing degree difference judgment module is yes.

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