Data processing method and system for multifunctional weight checking machine

Abstract

The invention discloses a data processing method and system for a multifunctional weight detector, relates to the technical field of measurement and detection, and aims to solve the problems of complex operation process and single interaction mode of an existing weight detector. The method specifically comprises the steps of collecting user gesture image data through an AI camera and extracting features, and collecting voice data in combination with a pickup unit; the method comprises the following steps: pre-constructing a simplified gesture set adaptive to an agricultural product transaction scene, and establishing a unique corresponding relation between gestures and weight checking operation; performing metering operation based on the agricultural product multi-dimensional classification information, the unit price data and the weighing data, and outputting a transaction result; and broadcasting a transaction result and an operation state based on gestures through multi-mode interaction feedback. According to the method, the operation process is simplified, a bimodal interaction mode is formed, and the method is adaptive to agricultural product transaction places.

Description

Technical Field

[0001] This invention belongs to the field of metrology and testing technology, specifically relating to a data processing method and system for a multifunctional weighing machine. Background Technology

[0002] Weighing machines, as core equipment for measuring the weight of goods, are widely used in agricultural product trading, logistics and transportation, and industrial production. However, current traditional weighing machines have the following problems: the operation process is complex, requiring multiple steps such as "selecting the product category - inputting parameters - confirming the weighing - settlement and printing," which is prone to operational errors and affects transaction efficiency; the interaction method is simple, mainly relying on touch screen or physical button operation, which can cause malfunctions when the user's hands are covered in mud or water or when wearing gloves.

[0003] In view of this, it is very necessary to provide a multifunctional weighing machine data processing method and system to solve the above-mentioned defects in the prior art. Summary of the Invention

[0004] To address the technical problems of complex operation procedures and limited interaction methods in existing technologies, this invention provides a multifunctional weighing machine data processing method and system to solve the aforementioned technical problems.

[0005] In a first aspect, the present invention provides a data processing method for a multifunctional weighing machine, comprising: Step S1: Acquiring and preprocessing user image data. The AI ​​camera acquires user gesture image data, extracts gesture features, and performs touch operations. At the same time, the microphone unit acquires user voice data. The user gesture image data collected includes image data containing the user's hand area; The image of the user's hand region includes the user's hand region and the background region; The user's hand area includes the palm, finger outline, and finger joint posture; The background area is the area where environmental objects are located.

[0006] The algorithm extracts the outlines of all objects in the user's gesture image data through edge detection, and then filters out the outline of the user's hand by combining the geometric features of the user's hand area. This achieves the segmentation of the hand area from the background area and outputs the pixel coordinates of the user's fingertips, supporting remote screen operation. The geometric features of the user's hand area include palm area, finger length ratio, and number of fingers; Collecting user gestures includes extracting key features of user gestures, namely geometric features and motion features; Movement characteristics include hand movement trajectory and posture change trends; The hand movement trajectory includes left and right swing trajectory and forward and backward swing trajectory, and the posture change trend includes the vertical downward movement of the palm pressing down and the static pointing angle of the fingers.

[0007] If the user agrees, collecting user images also includes collecting user facial images, locating facial regions, using the AgeNet network to extract age-related features, outputting age judgment results, and providing personalized operations for the user. Personalized operation for users includes providing an age-friendly mode for users aged 60 and above; The age-related characteristics include wrinkles, skin texture, and facial contour proportions; The age-friendly mode includes enlarging the font size, enhancing color contrast, and increasing button size on the touch interface; simplifying routine operation procedures; and strengthening voice prompts and animation guidance.

[0008] Step S2: The step of constructing the gesture set corresponding to the weighing operation. A simplified gesture set adapted to the agricultural product trading scenario is constructed in advance, and a unique correspondence is established between each gesture in the gesture set and the weighing operation. The simplified gesture set includes number gestures, command gestures, and region selection gestures; Numeric gestures include standard digit gestures from 1 to 10; Instruction gestures include giving a thumbs up, waving, beckoning, and pressing down with the palm; Region selection gesture: a static gesture based on finger pointing posture; Establish a unique correspondence between each gesture in the gesture set and the weighing operation, including standard numerical gestures from 1 to 10 for quickly inputting the quantity or estimated weight of agricultural products to be weighed; Command gesture correspondence: a thumbs up corresponds to the "confirm" operation, a hand wave corresponds to the "cancel" operation, a hand beckon corresponds to the "return" operation, and pressing down with the palm corresponds to the "print transaction voucher" operation; Area selection gestures: By combining the pixel coordinates of the fingertip with the coordinates of the touch screen, it enables screen operation from a distance without the user having to bend over or get close to the device.

[0009] Step S3: The steps for executing the weighing transaction are as follows: Based on the preset multi-dimensional classification information and unit price data of agricultural products, combined with the actual weight data collected by the weighing unit, the weighing calculation is performed, and the weighing transaction operation result is output. Agricultural product classification information includes classification by season, category, and region; The measurement units supported by the measurement calculation include traditional agricultural measurement units and standard measurement units.

[0010] Step S4: The multimodal interactive feedback step involves providing voice broadcasts of the weighing operation results and operation status based on user gestures.

[0011] The multimodal interactive feedback steps support an independent voice operation mode: if the user cannot perform touch operation, the weighing operation can be triggered directly through voice command; The voice interaction system includes a preset dialect database, an agricultural terminology database, and a colloquial command database.

[0012] Secondly, the technical solution of the present invention also provides a multi-functional weighing machine data processing system, including an image data acquisition and preprocessing module, a gesture set construction module, a weighing and measurement module, and a multimodal interaction module; The image data acquisition and preprocessing module, including an AI camera and a microphone array, is used to acquire user gesture image data, extract gesture features, perform touch operations, and acquire user voice data at the same time. The AI ​​camera includes a high-definition image acquisition unit, an infrared illumination unit, and an edge computing unit; The high-definition image acquisition unit is used to acquire user image data; Infrared supplementary lighting units are used for automatic supplementary lighting in low-light environments; The edge computing unit is equipped with an AI processing chip to process user image data.

[0013] The gesture set construction module pre-builds a simplified gesture set adapted to agricultural product trading scenarios and establishes a unique correspondence between each gesture in the gesture set and the weighing operation; The weighing and measurement module performs measurement calculations based on preset multi-dimensional classification information and unit price data of agricultural products, combined with the actual weight data collected by the weighing and measurement unit, and outputs the weighing and measurement transaction results. The multimodal interaction module provides voice broadcasts of the weighing operation results and operation status based on user gestures.

[0014] The beneficial effects of this invention are as follows: the multifunctional weighing machine data processing method and system provided by this invention, by constructing a simplified gesture set adapted to agricultural product trading scenarios, binds the core weighing operation with intuitive gestures, simplifying the operation process; realizes contactless gesture operation through an AI camera, and supports voice interaction with a sound pickup unit, forming a dual-modal interaction mode; if the user agrees to collect facial features, it provides an age-friendly mode for the user; and preset multi-dimensional classification information of agricultural products is adapted to agricultural product trading venues.

[0015] Furthermore, the design principle of this invention is reliable, the structure is simple, and it has a very wide range of application prospects. Attached Figure Description

[0016] 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. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a data processing method for a multifunctional weighing machine provided by the present invention.

[0018] Figure 2 This is a schematic diagram of a multifunctional weighing machine data processing system provided by the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0021] Example 1: like Figure 1 As shown, this embodiment of the invention provides a data processing method for a multi-functional weighing machine, including the following steps: Step S1: Acquiring and preprocessing user image data. The AI ​​camera acquires user gesture image data, extracts gesture features, and performs touch operations. At the same time, the microphone unit acquires user voice data. Step S2: The step of constructing the gesture set corresponding to the weighing operation. A simplified gesture set adapted to the agricultural product trading scenario is constructed in advance, and a unique correspondence is established between each gesture in the gesture set and the weighing operation. Step S3: The steps for executing the weighing transaction are as follows: Based on the preset multi-dimensional classification information and unit price data of agricultural products, combined with the actual weight data collected by the weighing unit, the weighing calculation is performed, and the weighing transaction operation result is output. Step S4: The multimodal interactive feedback step involves providing voice broadcasts of the weighing operation results and operation status based on user gestures.

[0022] Example 2: like Figure 1 As shown, this embodiment of the invention provides a data processing method for a multi-functional weighing machine, including the following steps: Step S1: Acquiring and preprocessing user image data. The AI ​​camera acquires user gesture image data, extracts gesture features, and performs touch operations. At the same time, the microphone unit acquires user voice data. Step S2: The step of constructing the gesture set corresponding to the weighing operation. A simplified gesture set adapted to the agricultural product trading scenario is constructed in advance, and a unique correspondence is established between each gesture in the gesture set and the weighing operation. Step S3: The steps for executing the weighing transaction are as follows: Based on the preset multi-dimensional classification information and unit price data of agricultural products, combined with the actual weight data collected by the weighing unit, the weighing calculation is performed, and the weighing transaction operation result is output. Step S4: The multimodal interactive feedback step involves providing voice broadcasts of the weighing operation results and operation status based on user gestures.

[0023] In step S1, the user gesture image data collected is image data containing the user's hand area; The image of the user's hand region includes the user's hand region and the background region; The user's hand area includes the palm, finger outline, and finger joint posture; The background area is the area where environmental objects are located.

[0024] The algorithm extracts the outlines of all objects in the user's gesture image data through edge detection, and then filters out the outline of the user's hand by combining the geometric features of the user's hand area. This achieves the segmentation of the hand area from the background area and outputs the pixel coordinates of the user's fingertips, supporting remote screen operation. The geometric features of the user's hand area include palm area, finger length ratio, and number of fingers; Collecting user gestures includes extracting key features of user gestures, namely geometric features and motion features; Movement characteristics include hand movement trajectory and posture change trends; The hand movement trajectory includes left and right swing trajectory and forward and backward swing trajectory, and the posture change trend includes the vertical downward movement of the palm pressing down and the static pointing angle of the fingers.

[0025] If the user agrees, collecting user images also includes collecting user facial images, locating facial regions, using the AgeNet network to extract age-related features, outputting age judgment results, and providing personalized operations for the user. Personalized operation for users includes providing an age-friendly mode for users aged 60 and above; Age-related characteristics include wrinkles, skin texture, and facial contour proportions; The age-friendly mode includes enlarging the font size, enhancing color contrast, and increasing button size on the touch interface; simplifying routine operation processes; and strengthening voice prompts and animation guidance.

[0026] Preferably, the font size is enlarged to 1.5-2 times the normal size, the color contrast is increased to 8:1 or higher, and the size of important buttons is increased; routine operations are simplified to within 3 steps; the intensity and frequency of voice prompts are increased, and animation guidance is added to key steps.

[0027] Example 3: like Figure 1 As shown, this embodiment of the invention provides a data processing method for a multi-functional weighing machine, including the following steps: Step S1: Acquiring and preprocessing user image data. The AI ​​camera acquires user gesture image data, extracts gesture features, and performs touch operations. At the same time, the microphone unit acquires user voice data. Step S2: The step of constructing the gesture set corresponding to the weighing operation. A simplified gesture set adapted to the agricultural product trading scenario is constructed in advance, and a unique correspondence is established between each gesture in the gesture set and the weighing operation. Step S3: The steps for executing the weighing transaction are as follows: Based on the preset multi-dimensional classification information and unit price data of agricultural products, combined with the actual weight data collected by the weighing unit, the weighing calculation is performed, and the weighing transaction operation result is output. Step S4: The multimodal interactive feedback step involves providing voice broadcasts of the weighing operation results and operation status based on user gestures.

[0028] In step S1, the user gesture image data collected is image data containing the user's hand area; The image of the user's hand region includes the user's hand region and the background region; The user's hand area includes the palm, finger outline, and finger joint posture; The background area is the area where environmental objects are located.

[0029] The algorithm extracts the outlines of all objects in the user's gesture image data through edge detection, and then filters out the outline of the user's hand by combining the geometric features of the user's hand area. This achieves the segmentation of the hand area from the background area and outputs the pixel coordinates of the user's fingertips, supporting remote screen operation. The geometric features of the user's hand area include palm area, finger length ratio, and number of fingers; Collecting user gestures includes extracting key features of user gestures, namely geometric features and motion features; Movement characteristics include hand movement trajectory and posture change trends; The hand movement trajectory includes left and right swing trajectory and forward and backward swing trajectory, and the posture change trend includes the vertical downward movement of the palm pressing down and the static pointing angle of the fingers.

[0030] If the user agrees, collecting user images also includes collecting user facial images, locating facial regions, using the AgeNet network to extract age-related features, outputting age judgment results, and providing personalized operations for the user. Personalized operation for users includes providing an age-friendly mode for users aged 60 and above; Age-related characteristics include wrinkles, skin texture, and facial contour proportions; The age-friendly mode includes enlarging the font size, enhancing color contrast, and increasing button size on the touch interface; simplifying routine operation processes; and strengthening voice prompts and animation guidance.

[0031] Preferably, the font size is enlarged to 1.5-2 times the normal size, the color contrast is increased to 8:1 or higher, and the size of important buttons is increased; routine operations are simplified to within 3 steps; the intensity and frequency of voice prompts are increased, and animation guidance is added to key steps.

[0032] In step S2, the simplified gesture set includes digital gestures, command gestures, and region selection gestures. Numeric gestures include standard digit gestures from 1 to 10; Instruction gestures include giving a thumbs up, waving, beckoning, and pressing down with the palm; Region selection gesture: a static gesture based on finger pointing posture; Establish a unique correspondence between each gesture in the gesture set and the weighing operation, including standard numerical gestures from 1 to 10 for quickly inputting the quantity or estimated weight of agricultural products to be weighed; Command gesture correspondence: a thumbs up corresponds to the "confirm" operation, a hand wave corresponds to the "cancel" operation, a hand beckon corresponds to the "return" operation, and pressing down with the palm corresponds to the "print transaction voucher" operation; Area selection gestures: By combining the pixel coordinates of the fingertip with the coordinates of the touch screen, it enables screen operation from a distance without the user having to bend over or get close to the device.

[0033] Example 4: like Figure 1 As shown, this embodiment of the invention provides a data processing method for a multi-functional weighing machine, including the following steps: Step S1: Acquiring and preprocessing user image data. The AI ​​camera acquires user gesture image data, extracts gesture features, and performs touch operations. At the same time, the microphone unit acquires user voice data. Step S2: The step of constructing the gesture set corresponding to the weighing operation. A simplified gesture set adapted to the agricultural product trading scenario is constructed in advance, and a unique correspondence is established between each gesture in the gesture set and the weighing operation. Step S3: The steps for executing the weighing transaction are as follows: Based on the preset multi-dimensional classification information and unit price data of agricultural products, combined with the actual weight data collected by the weighing unit, the weighing calculation is performed, and the weighing transaction operation result is output. Step S4: The multimodal interactive feedback step involves providing voice broadcasts of the weighing operation results and operation status based on user gestures.

[0034] In step S1, the user gesture image data collected is image data containing the user's hand area; The image of the user's hand region includes the user's hand region and the background region; The user's hand area includes the palm, finger outline, and finger joint posture; The background area is the area where environmental objects are located.

[0035] The algorithm extracts the outlines of all objects in the user's gesture image data through edge detection, and then filters out the outline of the user's hand by combining the geometric features of the user's hand area. This achieves the segmentation of the hand area from the background area and outputs the pixel coordinates of the user's fingertips, supporting remote screen operation. The geometric features of the user's hand area include palm area, finger length ratio, and number of fingers; Collecting user gestures includes extracting key features of user gestures, namely geometric features and motion features; Movement characteristics include hand movement trajectory and posture change trends; The hand movement trajectory includes left and right swing trajectory and forward and backward swing trajectory, and the posture change trend includes the vertical downward movement of the palm pressing down and the static pointing angle of the fingers.

[0036] If the user agrees, collecting user images also includes collecting user facial images, locating facial regions, using the AgeNet network to extract age-related features, outputting age judgment results, and providing personalized operations for the user. Personalized operation for users includes providing an age-friendly mode for users aged 60 and above; Age-related characteristics include wrinkles, skin texture, and facial contour proportions; The age-friendly mode includes enlarging the font size, enhancing color contrast, and increasing button size on the touch interface; simplifying routine operation processes; and strengthening voice prompts and animation guidance.

[0037] Preferably, the font size is enlarged to 1.5-2 times the normal size, the color contrast is increased to 8:1 or higher, and the size of important buttons is increased; routine operations are simplified to within 3 steps; the intensity and frequency of voice prompts are increased, and animation guidance is added to key steps.

[0038] In step S2, the simplified gesture set includes digital gestures, command gestures, and region selection gestures. Numeric gestures include standard digit gestures from 1 to 10; Instruction gestures include giving a thumbs up, waving, beckoning, and pressing down with the palm; Region selection gesture: a static gesture based on finger pointing posture; Establish a unique correspondence between each gesture in the gesture set and the weighing operation, including standard numerical gestures from 1 to 10 for quickly inputting the quantity or estimated weight of agricultural products to be weighed; Command gesture correspondence: a thumbs up corresponds to the "confirm" operation, a hand wave corresponds to the "cancel" operation, a hand beckon corresponds to the "return" operation, and pressing down with the palm corresponds to the "print transaction voucher" operation; Area selection gestures: By combining the pixel coordinates of the fingertip with the coordinates of the touch screen, it enables screen operation from a distance without the user having to bend over or get close to the device.

[0039] Among them, the multi-dimensional classification information of agricultural products in step S3 includes classification by season, category, and region; The measurement units supported by the measurement calculation include traditional agricultural measurement units and standard measurement units.

[0040] Preferred traditional agricultural units of measurement include "jin" and "dan"; Standard units of measurement include kilograms and tons.

[0041] Example 5: like Figure 1 As shown, this embodiment of the invention provides a data processing method for a multi-functional weighing machine, including the following steps: Step S1: Acquiring and preprocessing user image data. The AI ​​camera acquires user gesture image data, extracts gesture features, and performs touch operations. At the same time, the microphone unit acquires user voice data. Step S2: The step of constructing the gesture set corresponding to the weighing operation. A simplified gesture set adapted to the agricultural product trading scenario is constructed in advance, and a unique correspondence is established between each gesture in the gesture set and the weighing operation. Step S3: The steps for executing the weighing transaction are as follows: Based on the preset multi-dimensional classification information and unit price data of agricultural products, combined with the actual weight data collected by the weighing unit, the weighing calculation is performed, and the weighing transaction operation result is output. Step S4: The multimodal interactive feedback step involves providing voice broadcasts of the weighing operation results and operation status based on user gestures.

[0042] In step S1, the user gesture image data collected is image data containing the user's hand area; The image of the user's hand region includes the user's hand region and the background region; The user's hand area includes the palm, finger outline, and finger joint posture; The background area is the area where environmental objects are located.

[0043] The algorithm extracts the outlines of all objects in the user's gesture image data through edge detection, and then filters out the outline of the user's hand by combining the geometric features of the user's hand area. This achieves the segmentation of the hand area from the background area and outputs the pixel coordinates of the user's fingertips, supporting remote screen operation. The geometric features of the user's hand area include palm area, finger length ratio, and number of fingers; Collecting user gestures includes extracting key features of user gestures, namely geometric features and motion features; Movement characteristics include hand movement trajectory and posture change trends; The hand movement trajectory includes left and right swing trajectory and forward and backward swing trajectory, and the posture change trend includes the vertical downward movement of the palm pressing down and the static pointing angle of the fingers.

[0044] If the user agrees, collecting user images also includes collecting user facial images, locating facial regions, using the AgeNet network to extract age-related features, outputting age judgment results, and providing personalized operations for the user. Personalized operation for users includes providing an age-friendly mode for users aged 60 and above; Age-related characteristics include wrinkles, skin texture, and facial contour proportions; The age-friendly mode includes enlarging the font size, enhancing color contrast, and increasing button size on the touch interface; simplifying routine operation processes; and strengthening voice prompts and animation guidance.

[0045] Preferably, the font size is enlarged to 1.5-2 times the normal size, the color contrast is increased to 8:1 or higher, and the size of important buttons is increased; routine operations are simplified to within 3 steps; the intensity and frequency of voice prompts are increased, and animation guidance is added to key steps.

[0046] In step S2, the simplified gesture set includes digital gestures, command gestures, and region selection gestures. Numeric gestures include standard digit gestures from 1 to 10; Instruction gestures include giving a thumbs up, waving, beckoning, and pressing down with the palm; Region selection gesture: a static gesture based on finger pointing posture; Establish a unique correspondence between each gesture in the gesture set and the weighing operation, including standard numerical gestures from 1 to 10 for quickly inputting the quantity or estimated weight of agricultural products to be weighed; Command gesture correspondence: a thumbs up corresponds to the "confirm" operation, a hand wave corresponds to the "cancel" operation, a hand beckon corresponds to the "return" operation, and pressing down with the palm corresponds to the "print transaction voucher" operation; Area selection gestures: By combining the pixel coordinates of the fingertip with the coordinates of the touch screen, it enables screen operation from a distance without the user having to bend over or get close to the device.

[0047] Among them, the multi-dimensional classification information of agricultural products in step S3 includes classification by season, category, and region; The measurement units supported by the measurement calculation include traditional agricultural measurement units and standard measurement units.

[0048] Preferred traditional agricultural units of measurement include "jin" and "dan"; Standard units of measurement include kilograms and tons.

[0049] Among them, the multimodal interactive feedback step in step S4 supports an independent voice operation mode: if the user cannot perform touch operation, the weighing operation can be triggered directly through voice command. The voice interaction system includes a preset dialect database, an agricultural terminology database, and a colloquial command database.

[0050] The preferred dialect database includes Northeastern Mandarin, Sichuanese, Henanese, Cantonese, and Shandongese. The agricultural terminology database includes names of agricultural products and commonly used agricultural terms; The colloquial command library includes "weigh this", "print the ticket", and "weigh again".

[0051] Example 6: like Figure 2 As shown, this embodiment also provides a multi-functional weighing machine data processing system, including an image data acquisition and preprocessing module 1, a gesture set construction module 2, a weighing and measurement module 3, and a multimodal interaction module 4; Image data acquisition and preprocessing module 1 includes an AI camera and a microphone array, used to acquire user gesture image data, extract gesture features, perform touch operations, and acquire user voice data at the same time; The AI ​​camera includes a high-definition image acquisition unit, an infrared illumination unit, and an edge computing unit; The high-definition image acquisition unit is used to acquire user image data; Infrared supplementary lighting units are used for automatic supplementary lighting in low-light environments; The edge computing unit is equipped with an AI processing chip to process user image data.

[0052] Preferably, the AI ​​camera can automatically detect the user's location through image recognition. When the user is detected to be approaching (within 0.5-2 meters of the weighing machine), the high-definition image acquisition unit is automatically activated; when the user is detected to have left for more than 30 seconds, the power-saving mode is automatically activated.

[0053] Gesture set construction module 2 pre-builds a simplified gesture set adapted to agricultural product trading scenarios and establishes a unique correspondence between each gesture in the gesture set and the weighing operation; The weighing and measurement module 3 performs measurement calculations based on preset multi-dimensional classification information and unit price data of agricultural products, combined with the actual weight data collected by the weighing and measurement unit, and outputs the weighing and measurement transaction operation results. Multimodal interaction module 4 provides voice broadcast of the transaction results and operation status of the weighing operation based on user gestures.

[0054] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The methods disclosed in the embodiments are described simply because they correspond to the systems disclosed in the embodiments; relevant details can be found in the method section.

[0055] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0056] In the embodiments provided by this invention, it should be understood that the disclosed systems, methods, and approaches can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0057] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0058] In addition, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit.

[0059] Similarly, in the various embodiments of the present invention, each processing unit can be integrated into a functional module, or each processing unit can exist physically, or two or more processing units can be integrated into a functional module.

[0060] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0061] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, 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. Without further limitations, 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 said element.

[0062] The above-disclosed embodiments are merely preferred embodiments of the present invention, but the present invention is not limited thereto. Any non-creative variations that can be conceived by those skilled in the art, as well as any improvements and modifications made without departing from the principles of the present invention, should fall within the protection scope of the present invention.

Claims

1. A data processing method for a multi-functional weighing machine, characterized in that, Includes the following steps: Step S1: Acquiring and preprocessing user image data. The AI ​​camera acquires user gesture image data, extracts gesture features, and performs touch operations. At the same time, the microphone unit acquires user voice data. Step S2: The step of constructing the gesture set corresponding to the weighing operation. A simplified gesture set adapted to the agricultural product trading scenario is constructed in advance, and a unique correspondence is established between each gesture in the gesture set and the weighing operation. Step S3: The steps for executing the weighing transaction are as follows: Based on the preset multi-dimensional classification information and unit price data of agricultural products, combined with the actual weight data collected by the weighing unit, the weighing calculation is performed, and the weighing transaction operation result is output. Step S4: The multimodal interactive feedback step involves providing voice broadcasts of the weighing operation results and operation status based on user gestures.

2. The data processing method for a multi-functional weighing machine according to claim 1, characterized in that, The collected user gesture image data includes image data containing the user's hand area; The image of the user's hand region includes the user's hand region and the background region; The user's hand area includes the palm, finger outline, and finger joint posture; The background area is the area where environmental objects are located; The algorithm extracts the outlines of all objects in the user's gesture image data using an edge detection algorithm. Then, it combines the geometric features of the user's hand area to filter out the outline of the user's hand, thereby achieving the segmentation of the hand area from the background area and outputting the pixel coordinates of the user's fingertips.

3. The data processing method for a multi-functional weighing machine according to claim 2, characterized in that, The geometric features of the user's hand area include palm area, finger length ratio, and number of fingers; Collecting user gestures includes extracting key features of user gestures, namely geometric features and motion features; Movement characteristics include hand movement trajectory and posture change trends; The hand movement trajectory includes left and right swing trajectory and forward and backward swing trajectory, and the posture change trend includes the vertical downward movement of the palm pressing down and the static pointing angle of the fingers.

4. The data processing method for a multi-functional weighing machine according to claim 3, characterized in that, If the user agrees, collecting user images also includes collecting user facial images, locating facial regions, using the AgeNet network to extract age-related features, outputting age judgment results, and providing personalized operations for the user. Personalized operation for users includes providing an age-friendly mode for users aged 60 and above; The age-related characteristics include wrinkles, skin texture, and facial contour proportions; The age-friendly mode includes enlarging the font size, enhancing color contrast, and increasing button size on the touch interface; simplifying routine operation procedures; and strengthening voice prompts and animation guidance.

5. The data processing method for a multi-functional weighing machine according to claim 1, characterized in that, The simplified gesture set includes number gestures, command gestures, and region selection gestures; Numeric gestures include standard digit gestures from 1 to 10; Instruction gestures include giving a thumbs up, waving, beckoning, and pressing down with the palm; The area selection gesture is a static gesture based on the finger pointing posture.

6. The data processing method for a multi-functional weighing machine according to claim 5, characterized in that, Establish a unique correspondence between each gesture in the gesture set and the weighing operation, including standard numerical gestures from 1 to 10 for quickly inputting the quantity or estimated weight of agricultural products to be weighed; Command gesture correspondence: a thumbs up corresponds to the "confirm" operation, a hand wave corresponds to the "cancel" operation, a hand beckon corresponds to the "return" operation, and pressing down with the palm corresponds to the "print transaction voucher" operation; Region selection gestures: Combine the pixel coordinates of the fingertip with the coordinate mapping of the touch screen to perform screen operations from a distance.

7. The data processing method for a multi-functional weighing machine according to claim 1, characterized in that, Agricultural product classification information includes classification by season, category, and region; The measurement units supported by the measurement calculation include traditional agricultural measurement units and standard measurement units.

8. The data processing method for a multi-functional weighing machine according to claim 1, characterized in that, The multimodal interactive feedback steps support an independent voice operation mode: if the user cannot perform touch operation, the weighing operation can be triggered directly through voice command; The voice interaction is pre-set with a dialect library, an agricultural terminology library, and a colloquial command library.

9. A multi-functional weighing machine data processing system, characterized in that, It includes an image data acquisition and preprocessing module, a gesture set construction module, a weighing and measurement module, and a multimodal interaction module; The image data acquisition and preprocessing module, including an AI camera and a microphone array, is used to acquire user gesture image data, extract gesture features, perform touch operations, and acquire user voice data at the same time. The gesture set construction module pre-builds a simplified gesture set adapted to agricultural product trading scenarios and establishes a unique correspondence between each gesture in the gesture set and the weighing operation; The weighing and measurement module performs measurement calculations based on preset multi-dimensional classification information and unit price data of agricultural products, combined with the actual weight data collected by the weighing and measurement unit, and outputs the weighing and measurement transaction results. The multimodal interaction module provides voice broadcasts of the weighing operation results and operation status based on user gestures.

10. A multifunctional weighing machine data processing system according to claim 9, characterized in that, The AI ​​camera includes a high-definition image acquisition unit, an infrared fill light unit, and an edge computing unit; The high-definition image acquisition unit is used to acquire user image data; Infrared supplementary lighting units are used for automatic supplementary lighting in low-light environments; The edge computing unit is equipped with an AI processing chip to process user image data.

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