Weighing algorithm for electronic scale to automatically remove weight of spoon

Abstract

The invention discloses a weighing algorithm for an electronic scale to automatically remove the weight of a spoon, and the algorithm comprises the steps: extracting a decrement change feature through a decrement change analysis module when a weighing sensor measures decrement, and judging whether the decrement change feature accords with a food decrement change feature or not; if yes, the decrement value is the actual decrement value, and the calculation processing module calculates the actual decrement price of the food and displays the real-time total amount and the food decrement price; if not, the calculation processing module subtracts a spoon weight value obtained by the spoon weight model from the decrement value to obtain an actual decrement value, and the calculation processing module calculates the actual decrement price of the food and displays the real-time total amount and the food decrement price; the self-learning module analyzes the decrement data analyzed by the decrement change analysis module and the actual decrement data obtained by the calculation processing module, optimizes the spoon weight value in the spoon weight model, and solves the problem of extra expense of consumers caused by spoon misjudgment in a traditional self-service weighing system.

Description

[Technical field]

[0001] The invention relates to a weighing algorithm for automatically removing the weight of a spoon from an electronic scale. [Background Technology]

[0002] With the rise of smart restaurants, self-service weighing systems have been widely used in the catering industry. The system automatically weighs the food to calculate the customer's consumption amount, greatly improving the service efficiency of the restaurant. However, in actual applications, the weighing system often encounters some unavoidable errors. For example, when a customer uses a spoon to pick up food, but the spoon is not put back in place in time, or the network environment is unstable, resulting in settlement calculation errors, the system may mistakenly calculate the weight of the spoon as part of the food, resulting in the user's consumption amount being overestimated. [Summary of the invention]

[0003] The invention overcomes the shortcomings of the prior art and provides a weighing algorithm for an electronic scale which automatically removes the weight of a spoon.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] A weighing algorithm for an electronic scale that automatically removes the weight of a spoon, characterized in that it includes a weighing sensor disposed in the scale body that can measure the weight of a weighing plate and food, a data processing unit disposed in the scale body that can convert the weight of food measured by the weighing sensor into a weight value and automatically identify the weight removed from the spoon, a display screen connected to the scale body, and a background server for presetting food types and unit prices, the data processing unit includes a weight reduction change analysis module for determining whether the weight reduction of the weighing plate is a weight reduction of food or a weight reduction of the spoon, a self-learning module for establishing a spoon weight model and analyzing data through the weight reduction change analysis module to optimize the spoon weight accuracy in the model, and a calculation processing module for calculating the weight reduction and price of food, and the weighing algorithm includes the following steps:

[0006] S1, weighing sensor measures the weight of the weighing pan in the empty state;

[0007] S2, the weighing sensor measures the first total weight of the weighing plate when the food is transferred and the spoon is placed, and the calculation processing module subtracts the weight of the weighing plate from the first total weight to obtain a second total weight of the food weight and the spoon weight added together, and displays it on the display screen;

[0008] S3, when the weighing sensor measures the second total weight reduction, the reduction change analysis module extracts the reduction change characteristics and determines whether the reduction change characteristics conform to the food reduction change characteristics;

[0009] S4. If it is determined to be in compliance, the reduction value is the actual reduction value, and the calculation processing module connects with the background server to obtain the corresponding food unit price, and calculates the actual reduction price of the food according to the preset unit price, and displays the real-time second total weight and the price of the food reduction on the display screen;

[0010] If it is judged that it does not meet the requirements, the calculation and processing module subtracts the spoon weight value obtained by the spoon weight model in the self-learning module from the reduction value to obtain the actual reduction value. The calculation and processing module connects with the background server to obtain the corresponding food unit price, and calculates the actual reduction price of the food according to the preset unit price, and displays the real-time second total weight and the price of the food reduction through the display screen;

[0011] S5. The self-learning module analyzes the reduction data analyzed by the reduction change analysis module and the actual reduction data obtained by the calculation processing module, and optimizes the spoon weight value in the spoon weight model.

[0012] The weighing algorithm of an electronic scale that automatically removes the weight of a spoon as described in 1 above is characterized in that the extracted reduction change characteristics include reduction change rate characteristics and reduction change amplitude characteristics.

[0013] The weighing algorithm for automatically removing the weight of the spoon from an electronic scale as described above is characterized in that: a preset range value of the spoon weight reduction change rate and a preset range value of the spoon weight reduction change amplitude are provided in the weight reduction change analysis module;

[0014] In S3, the reduction change analysis module determines whether the reduction change rate characteristic value and the reduction change amplitude characteristic value fall within the spoon reduction change rate preset range value and the spoon reduction change amplitude preset range value respectively;

[0015] In S4, when any characteristic value does not fall into the corresponding preset range value, it is judged that the reduction change characteristic meets the food reduction change characteristic; when the two characteristic values ​​fall into the corresponding preset range values ​​respectively, it is judged that the reduction change characteristic does not meet the food reduction change characteristic.

[0016] The weighing algorithm of the electronic scale that automatically removes the weight of the spoon as described above is characterized in that the self-learning module is based on a machine learning algorithm and establishes a spoon weight model by using a weighing sensor to measure the change pattern of food weight data and spoon weight data.

[0017] The weighing algorithm of the electronic scale that automatically removes the weight of the spoon as described above is characterized in that the spoon weight model analyzes the weighing data of the weighing sensor to learn the regular weight range, placement time and reduction fluctuation characteristic information of the spoon.

[0018] The weighing algorithm of the electronic scale that automatically removes the weight of the spoon as described above is characterized in that the machine learning algorithm includes a K-means clustering algorithm and a support vector machine algorithm.

[0019] The weighing algorithm of the electronic scale for automatically removing the weight of the spoon as described above is characterized in that: the data processing unit further comprises an error correction module for correcting errors caused by network fluctuations or hardware failures;

[0020] The error correction module in S4 monitors the network status and the reduction value. When the monitored network fluctuation exceeds the preset value or the reduction value differs from the historical record reduction value by more than the set value, the error correction module will smooth the real-time actual reduction value based on the historical record actual reduction value.

[0021] The weighing algorithm of an electronic scale that automatically removes the weight of a spoon as described above is characterized in that the data processing unit also includes a database for storing weighing data of each weighing by the weighing sensor, weight loss data, actual weight loss data, food weight increase and decrease pattern data, spoon weight characteristic data and weight change data of food adhered to the spoon.

[0022] The beneficial effects of the present invention are:

[0023] The present invention can solve the problem of extra charges for consumers caused by misjudgment of spoons in traditional self-service weighing systems; at the same time, the present invention does not require additional hardware settings, does not rely on external hardware identification equipment, and only uses weighing sensors and algorithms to remove the weight of the spoon, thereby reducing system costs; the present invention is efficient and accurate, and through self-learning and dynamic optimization algorithms, the system can accurately identify and remove the weight of the spoon, thereby improving calculation accuracy; the present invention provides real-time feedback and improves user experience: users can view the accurate food weight and consumption amount in real time, avoid unnecessary charges caused by misjudgment, and improve user satisfaction; the present invention is intelligent and adaptable, and the system can self-adjust according to different scenarios and user behavior habits, has strong adaptability, and the algorithm recognition ability continues to improve after long-term use. [Drawings]

[0024] Figure 1 Schematic diagram of the system of the present invention. [Specific implementation method]

[0025] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings.

[0026] It should be noted that all directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly. In addition, the descriptions of "preferred", "sub-preferred", etc. in the present invention are only used for descriptive purposes, and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "preferred" or "sub-preferred" may explicitly or implicitly include at least one such feature.

[0027] like Figure 1 As shown, a weighing algorithm for automatically removing the weight of a spoon from an electronic scale includes a weighing sensor 1 disposed in the scale body and capable of measuring the weight of a weighing plate and food, a data processing unit 2 disposed in the scale body and capable of converting the weight of food measured by the weighing sensor 1 into a weight value and automatically identifying the weight removed from the spoon, a display screen 3 connected to the scale body, and a background server 4 for presetting food types and unit prices, the data processing unit 2 includes a weight reduction change analysis module 21 for determining whether the weight reduction of the weighing plate is a weight reduction of food or a weight reduction of the spoon, a self-learning module 22 for establishing a spoon weight model and optimizing the spoon weight accuracy in the model by analyzing data through the weight reduction change analysis module 21, and a calculation processing module 23 for calculating the weight reduction and price of food, and the weighing algorithm includes the following steps:

[0028] S1, weighing sensor 1 measures the weight of the weighing pan in an empty pan state;

[0029] S2, the weighing sensor 1 measures the first total weight of the weighing plate when the food is transferred and the spoon is placed, and the calculation processing module 23 subtracts the weight of the weighing plate from the total weight to obtain the second total weight of the food weight and the spoon weight added together, and displays it on the display screen 3;

[0030] S3, when the weighing sensor 1 measures the second total weight reduction, the reduction change analysis module 21 extracts the reduction change characteristics and determines whether the reduction change characteristics are consistent with the food reduction change characteristics;

[0031] S4, if it is determined to be in compliance, the reduction value is the actual reduction value, the calculation processing module 23 connects with the backend server 4 to obtain the corresponding food unit price, and calculates the actual reduction price of the food according to the preset unit price, and displays the real-time second total weight and the price of the food reduction through the display screen 3;

[0032] If it is judged that it does not meet the requirements, the calculation processing module 23 subtracts the spoon weight value obtained by the spoon weight model in the self-learning module 22 from the reduction value to obtain the actual reduction value. The calculation processing module 23 connects with the backend server 4 to obtain the corresponding food unit price, and calculates the actual reduction price of the food according to the preset unit price, and displays the real-time second total weight and the price of the food reduction through the display screen 3;

[0033] S5. The self-learning module 22 analyzes the reduction data analyzed by the reduction change analysis module 21 and the actual reduction data obtained by the calculation processing module 23, and optimizes the spoon weight quantity in the spoon weight model.

[0034] Specifically, the reduction change analysis module in S3 is provided with a preset range value of the spoon reduction change rate and a preset range value of the spoon reduction change amplitude; at the same time, the reduction change analysis module in S3 determines whether the reduction change rate characteristic value and the reduction change amplitude characteristic value fall within the preset range value of the spoon reduction change rate and the preset range value of the spoon reduction change amplitude respectively; therefore, in S4, when any characteristic value does not fall within the corresponding preset range value, it is judged that the reduction change characteristic meets the food reduction change characteristic; when the two characteristic values ​​fall into the corresponding preset range values ​​respectively, it is judged that the reduction change characteristic does not meet the food reduction change characteristic.

[0035] In this case, the data processing unit also includes an error correction module 24 for correcting errors caused by network fluctuations or hardware failures;

[0036] The error correction module in S4 monitors the network status and the reduction value. When the monitored network fluctuation exceeds the preset value or the reduction value differs from the historical record reduction value by more than the set value, the error correction module will smooth the real-time actual reduction value based on the historical record actual reduction value.

[0037] The food weight change pattern analysis in this case includes the change characteristics of food weight and the change characteristics of spoon weight.

[0038] Characteristics of food weight changes: Food weight usually increases steadily and continuously, and changes greatly in a short period of time.

[0039] The weight of the spoon is usually relatively fixed, and may fluctuate briefly during placement, but it does not increase in the same way as the weight of the food. The spoon and the food change at different rates and magnitudes, and the system distinguishes them by analyzing the difference between the two.

[0040] Therefore, the algorithm steps for removing the spoon weight include weight increment comparison and increment feature analysis.

[0041] in,

[0042] Weight increment comparison: The system will monitor the weight increment of the weighing plate in real time. If the system detects a sudden increase in weight in a short period of time, but the increase is relatively small and the change is stable, the system will determine that part of the weight is the weight of the spoon.

[0043] Incremental feature analysis: By comparing the typical pattern of food weight gain, such as continuous and gradual changes in food weight, the system determines that the weight fluctuations that do not conform to the food weight gain characteristics in the short term are caused by the spoon, and then deducts this part from the second total weight.

[0044] The self-learning mechanism in this case includes dynamic adjustment of removal strategy and model training and optimization.

[0045] Dynamically adjust the removal strategy: The system uses a self-learning module to gradually optimize the spoon weight model based on historical data. The data after each weighing will be stored, and the spoon weight model will be adjusted based on the weighing results. The system will analyze the change pattern of the spoon weight in each weighing data and learn the regular weight range, placement time, fluctuation characteristics and other information of the spoon.

[0046] Model training and optimization: Through machine learning algorithms such as K-means clustering and support vector machines, the system can continuously adjust and optimize the recognition accuracy of the spoon and food weight changes, and gradually improve the accuracy of spoon weight recognition and removal.

[0047] The error correction in this case is mainly for network fluctuation and data stability detection: the system will monitor the network status. If weight instability caused by network fluctuation is detected during data transmission, the system will perform smoothing correction based on historical data to prevent erroneous calculations due to network fluctuations.

[0048] Finally, remove the spoon weight: When the system confirms that the spoon weight has been correctly identified, it will deduct the spoon weight from the total weight, leaving only the net weight of the food. Finally, the weight of the food that the user should pay is calculated and the accurate consumption amount is generated.

[0049] In this case, a self-learning optimization mechanism is set up. In order to ensure that the system can effectively adapt to different restaurant environments and different user scenarios, the system's self-learning mechanism continuously optimizes the accuracy of the spoon weight, including:

[0050] Historical data accumulation: The system will accumulate a large amount of historical weighing data, including the weight increase and decrease pattern of food, the weight characteristics of the spoon, and the weight change when the food and spoon are combined. By analyzing this data, the system can build a database of the weight change of food and spoon.

[0051] Model adaptive adjustment: After each weighing, the system will compare the current data with the historical data and automatically adjust the algorithm parameters for removing the spoon weight. As the number of uses increases, the system will gradually optimize the removal strategy, so that the accuracy of spoon weight removal continues to improve.

[0052] Error correction: In certain abnormal situations such as misoperation, network failure, etc., the system detects abnormalities through error detection mechanisms such as comparing the set error range and fluctuation range, and automatically corrects the weight data to ensure the accuracy of the removal process.

[0053] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. All equivalent structural changes made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or directly or indirectly applied in other related technical fields, are included in the patent protection scope of the present invention.

Claims

1. A weighing algorithm for an electronic scale that automatically removes the weight of a spoon, characterized in that: The invention comprises a weighing sensor (1) arranged in a weighing body and capable of measuring the weight of a weighing plate and food, a data processing unit (2) arranged in the weighing body and capable of converting the food weight measured by the weighing sensor (1) into a weight value and automatically identifying the weight after the spoon is removed, a display screen (3) connected to the weighing body, and a background server (4) for presetting the food type and unit price. The data processing unit (2) comprises a weight reduction change analysis module (21) for determining whether the weight reduction of the weighing plate is the weight reduction of food or the weight reduction of the spoon, a self-learning module (22) for establishing a spoon weight model and optimizing the spoon weight accuracy in the model by analyzing data through the weight reduction change analysis module (21), and a calculation processing module (23) for calculating the weight reduction and price of food. The weighing algorithm comprises the following steps: S1, a weighing sensor (1) measures the weight of the weighing pan in an empty state; S2, the weighing sensor (1) measures a first total weight when the weighing plate is loaded with food and the spoon is placed thereon, the calculation processing module (23) subtracts the weight of the weighing plate from the first total weight to obtain a second total weight which is the sum of the weight of the food and the weight of the spoon, and displays it on the display screen (3); S3, when the weighing sensor (1) measures the second total weight reduction, the reduction change analysis module (21) extracts the reduction change characteristics and determines whether the reduction change characteristics conform to the food reduction change characteristics; S4, if it is determined that the reduction value is an actual reduction value, the calculation processing module (23) connects with the backend server (4) to obtain the corresponding food unit price, and calculates the actual reduction price of the food according to the preset unit price, and displays the real-time second total weight and the price of the food reduction through the display screen (3); If it is determined that the weight of the spoon obtained by the spoon weight model in the self-learning module (22) is not met, the calculation processing module (23) subtracts the weight reduction value from the weight reduction value to obtain the actual weight reduction value, the calculation processing module (23) connects with the backend server (4) to obtain the corresponding food unit price, and calculates the actual weight reduction price of the food according to the preset unit price, and displays the real-time second total weight and the weight reduction price of the food on the display screen (3); S5. The self-learning module (22) analyzes the weight reduction data analyzed by the weight reduction change analysis module (21) and the actual weight reduction data obtained by the calculation processing module (23), and optimizes the spoon weight value in the spoon weight model.

2. The weighing algorithm of the electronic scale that automatically removes the weight of the spoon according to claim 1, characterized in that: The extracted reduction change features include reduction change rate features and reduction change amplitude features.

3. The weighing algorithm of the electronic scale that automatically removes the weight of the spoon according to claim 2, characterized in that: The reduction change analysis module (21) is provided with a preset range value of the spoon reduction change rate and a preset range value of the spoon reduction change amplitude; In S3, the reduction change analysis module (21) determines whether the reduction change rate characteristic value and the reduction change amplitude characteristic value fall within the spoon reduction change rate preset range value and the spoon reduction change amplitude preset range value respectively; In S4, when any characteristic value does not fall within the corresponding preset range value, it is determined that the reduction change characteristic meets the food reduction change characteristic; When the two characteristic values ​​fall into the corresponding preset range values ​​respectively, it is judged that the reduction change characteristic does not conform to the food reduction change characteristic.

4. The weighing algorithm of the electronic scale that automatically removes the weight of the spoon according to claim 1, characterized in that: The self-learning module (22) establishes a spoon weight model based on a machine learning algorithm and a weighing sensor (1) based on the change pattern of food weight data and spoon weight data.

5. The weighing algorithm of the electronic scale that automatically removes the weight of the spoon according to claim 4, characterized in that: The spoon weight model analyzes the weighing data of the weighing sensor (1) to learn the regular weight range, placement time and weight loss fluctuation characteristic information of the spoon.

6. The weighing algorithm of the electronic scale that automatically removes the weight of the spoon according to claim 4, characterized in that: Machine learning algorithms include K-means clustering algorithm and support vector machine algorithm.

7. The weighing algorithm of the electronic scale that automatically removes the weight of the spoon according to claim 1, characterized in that: The data processing unit (2) also includes an error correction module (24) for correcting errors caused by network fluctuations or hardware failures; The error correction module (24) in S4 monitors the network status and the decrement value. When the monitored network fluctuation exceeds a preset value or the decrement value differs from the historical record decrement value by more than a set value, the error correction module (24) will smooth the actual decrement value in real time according to the historical record actual decrement value.

8. The weighing algorithm of the electronic scale that automatically removes the weight of the spoon according to claim 1, characterized in that: The data processing unit (2) also includes a database (25) for storing weighing data, weight loss data, actual weight loss data, food weight increase and decrease pattern data, spoon weight characteristic data and spoon adhered food weight change data of each weighing of the weighing sensor (1).

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