Multi-scene multi-category multifunctional quality inspection AI robot

Abstract

The invention provides a multi-scene multi-category multifunctional quality inspection AI robot, and relates to the technical field of intelligent quality inspection. The multi-scene, multi-category and multifunctional quality inspection AI robot comprises a sensing module, a data processing module, an execution module, an interaction module, a charging settlement module and a scene adaptation module, comprising a visual detection unit, a touch detection unit, an olfactory detection unit, an auditory detection unit and a spectrum detection unit. The technical problems that existing quality inspection equipment is poor in scene adaptability, single in function, insufficient in detection precision and lack of an integrated service process are solved, and multi-scene rapid adaptation, multi-category precise detection, multifunctional mode switching and integrated service of quality inspection, quotation and charging are achieved.

Description

Technical Field

[0001] This invention relates to the field of intelligent quality inspection technology, specifically to an AI robot for quality inspection in multiple scenarios and for multiple product categories. Background Technology

[0002] With the large-scale development of the manufacturing industry and the diversified upgrading of the commodity circulation field, higher requirements are placed on the efficiency, accuracy, and coverage of product quality testing. Existing testing equipment is mostly designed for specific scenarios or single product categories, with poor scenario adaptability. It is difficult to meet the quality inspection needs of multiple scenarios such as shopping malls, supermarkets, production sites, and logistics warehouses. It usually only supports fixed-mode testing and cannot flexibly switch testing modes according to the characteristics and quality requirements of the tested objects. Moreover, it lacks an integrated process for testing results and pricing, making it difficult to achieve closed-loop management of quality inspection services. In multi-category quality inspection scenarios, the quality inspection characteristics of different types of products vary greatly. Existing testing equipment lacks a unified multi-dimensional quality inspection evaluation model and cannot comprehensively analyze the multi-dimensional characteristics of different product categories, resulting in insufficient testing accuracy.

[0003] Therefore, this invention proposes an AI robot that can adapt to multiple scenarios, cover multiple categories, have multi-functional detection modes, and integrate precise quality inspection and pricing. It becomes the key to solving the current pain points of the quality inspection industry and promotes the upgrading of production and life and realizes random supervision and monitoring of the entire chain of production, sales and consumption. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a multi-scenario, multi-category, and multi-functional quality inspection AI robot. This robot solves the technical problems of poor scenario adaptability, limited functionality, insufficient detection accuracy, and lack of integrated service processes in existing quality inspection equipment. It enables rapid adaptation to multiple scenarios, accurate detection of multiple categories, multi-functional mode switching, and integrated services for quality inspection, pricing, and payment.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A multi-scenario, multi-category, multi-functional quality inspection AI robot includes a perception module, a data processing module, an execution module, an interaction module, a billing and settlement module, and a scenario adaptation module. The perception module is used to collect multi-dimensional feature data of the object to be inspected, including a visual detection unit, a tactile detection unit, an olfactory detection unit, an auditory detection unit, and a spectral detection unit. The data processing module analyzes and processes the feature data based on a preset quality inspection evaluation model. The quality inspection evaluation model includes mathematical calculation logic for feature weight calculation, detection accuracy correction, and confidence assessment. The execution module is used to perform detection actions in different modes, supporting four detection modes: fast detection, slow detection, flow detection, and spot check. The interactive module is used to output detection results and receive user commands, and includes a display unit, a voice unit, and a printing unit; The billing and settlement module is used to generate a quotation and complete the payment based on the test type and results. It supports three payment methods: QR code payment, card payment and account deduction. After the payment is completed, a test report and a payment voucher are generated and output through the interaction module. The scenario adaptation module pre-stores environmental parameters, quality inspection standards, and testing procedures for scenarios such as shopping malls, supermarkets, pharmacies, commercial streets, production sites, logistics warehouses, customs, and industrial and commercial management.

[0006] Furthermore, the visual detection unit of the perception module uses a high-definition camera and a 3D laser scanner to collect data on the appearance, color, size, and defect characteristics of the object to be detected. The tactile detection unit uses a pressure sensor array to collect data on the hardness, elasticity, and surface roughness of the object to be detected. The olfactory detection unit uses a gas sensor array to collect data on the volatile gas composition and concentration of the object to be detected. The auditory detection unit uses a sound sensor to collect acoustic characteristic data of the object to be detected under vibration or impact. The spectral detection unit uses a near-infrared spectrometer to collect data on the composition and content characteristics of the object to be detected.

[0007] Furthermore, in the quality inspection evaluation model of the data processing module, the feature weight calculation adopts a combination of the analytic hierarchy process (AHP) and the entropy weight method, and the weighting formula is as follows: ; in, For the first The combined weight of each feature, The weights of the analytic hierarchy process are... , For the first The weights of each feature are obtained through the analytic hierarchy process (AHP). For the first The weights of each feature are obtained using the entropy weighting method.

[0008] Furthermore, the detection accuracy correction formula of the data processing module is used to eliminate the influence of environmental interference on the detection results under different scenarios, and its weighting formula is as follows: ; in, For the corrected feature data, The raw feature data collected by the perception module. This is the environmental interference correction factor. This represents the difference between the current scene environment parameters and the standard environment parameters. This represents the overall weight of the feature.

[0009] Furthermore, the data processing module determines the reliability of the quality inspection results using a confidence assessment formula, the weighting formula of which is: ; in For the confidence level of the quality inspection results and , For the first The detection similarity of each feature (i.e., the degree of matching between the features of the object to be detected and the standard features). This is the error coefficient during the detection process; when When a pre-set reliability threshold is set, the quality inspection results are deemed valid; when When this happens, a re-detection process is triggered.

[0010] Furthermore, the pricing calculation formula used by the billing and settlement module during billing is as follows: ; in, For the final testing quote, Basic testing fees, For the detection time coefficient, This is the ratio of the actual testing time to the standard testing time. For the detection accuracy coefficient, This is the ratio of actual detection accuracy to standard detection accuracy. The scene complexity coefficient. This represents the complexity level of the current scenario.

[0011] Furthermore, the display unit of the interaction module adopts a touch screen to display the testing progress, testing results, quotation information and operation instructions; the voice unit supports voice broadcasting of testing results and voice reception of user commands; the printing unit is used to print paper versions of testing reports and payment vouchers; the interaction module also supports data interaction with mobile terminals or back-end management systems through the wireless communication unit to realize remote viewing and management of testing data.

[0012] Furthermore, the scene adaptation module supports custom scene configuration. Users can input environmental parameters, quality inspection standards, and testing procedures for new scenes through the interaction module. The scene adaptation module verifies and stores the input data to achieve rapid adaptation to new scenes. The workflow steps of this multi-scenario, multi-category, and multi-functional quality inspection AI robot are as follows: Step 1: Scene adaptation; Once the robot enters the target scene, the scene adaptation module collects real-time environmental data through environmental sensors, matches it with pre-stored scene parameters, and automatically loads the corresponding quality inspection standards and testing procedures. If matching fails, the user can manually select a scene or perform custom configuration. Step 2: Test preparation; Users place the object to be detected in the detection area, select the detection mode through the interactive module, and input the basic information of the object to be detected; Step 3: Data Collection; The execution module controls the perception module to start the corresponding detection unit according to the selected detection mode, collects multi-dimensional feature data of the object to be detected, and displays the detection progress in real time during the collection process. Step 4: Data processing; The data processing module preprocesses the collected raw feature data, calculates the comprehensive feature weights using a combined weighting method, eliminates environmental interference using a detection accuracy correction formula, and finally determines the reliability of the quality inspection results using a confidence assessment formula. Step 5: Output the results; If the confidence level of the quality inspection result is ≥0.85, the interactive module displays the test result, and the billing and settlement module generates a quote based on the test parameters. If the confidence level is <0.85, the test is automatically re-tested, up to 3 times. If the result still does not meet the requirements, a manual review will be prompted. Step 6: Payment Settlement: The user completes the payment through the selected payment method. After successful payment, the interactive module prints the test report and payment voucher, and simultaneously synchronizes the test data and payment information to the back-end management system. The user can query the test data and payment records through the mobile terminal or the back-end management system. The robot then resets and waits for the next test task.

[0013] This invention provides a multi-functional quality inspection AI robot applicable to multiple scenarios and product categories. It offers the following advantages: 1. This invention provides a multi-scenario, multi-category, and multi-functional quality inspection AI robot. This quality inspection AI robot has stronger multi-scenario adaptability. Through the scenario adaptation module, it pre-stores typical scenario parameters and supports custom configuration, which can quickly adapt to multiple scenarios such as shopping malls, supermarkets, and production sites. There is no need to configure separate testing equipment for different scenarios, reducing the cost of use. In addition, the perception module in the quality inspection robot integrates multi-dimensional detection units, which can collect characteristic data such as appearance, composition, and mechanical properties of different product categories. Combined with a multi-dimensional quality inspection evaluation model, it can achieve accurate detection of multiple product categories.

[0014] 2. This invention provides a multi-functional quality inspection AI robot for multiple scenarios and product categories. It features flexible multi-functional detection modes, supporting four modes: rapid detection, slow detection, mobile detection, and spot checks. These modes can be flexibly switched according to the characteristics of the object being inspected, accuracy requirements, and scenario needs, balancing detection efficiency and accuracy to meet the needs of different quality inspection scenarios. Furthermore, it achieves high detection accuracy by rationally allocating feature weights through a combined weighting method, introducing a detection accuracy correction formula to eliminate environmental interference, and combining a confidence assessment mechanism to ensure the reliability of the detection results. Attached Figure Description

[0015] Figure 1 This is a functional architecture diagram of the AI ​​quality inspection robot for multiple scenarios, multiple categories, and multiple functions according to the present invention. Figure 2 This is a flowchart illustrating the workflow steps of the AI ​​robot for quality inspection in multiple scenarios and product categories according to the present invention. Detailed Implementation

[0016] 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 embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example 1

[0017] like Figure 1-2 As shown, this embodiment of the invention provides a multi-scenario, multi-category, multi-functional quality inspection AI robot, including a perception module, a data processing module, an execution module, an interaction module, a billing and settlement module, and a scenario adaptation module. The perception module is used to collect multi-dimensional feature data of the object to be inspected, including a visual detection unit, a tactile detection unit, an olfactory detection unit, an auditory detection unit, and a spectral detection unit. The data processing module analyzes and processes the feature data based on a preset quality inspection evaluation model. The quality inspection evaluation model includes mathematical calculation logic for feature weight calculation, detection accuracy correction, and confidence assessment. The execution module is used to perform detection actions in different modes, supporting four detection modes: fast detection, slow detection, flow detection, and spot check. The interactive module is used to output detection results and receive user commands, and includes a display unit, a voice unit, and a printing unit; The billing and settlement module is used to generate a quotation and complete the payment based on the test type and results. It supports three payment methods: QR code payment, card payment and account deduction. After the payment is completed, a test report and a payment voucher are generated and output through the interaction module. The scenario adaptation module pre-stores environmental parameters, quality inspection standards, and testing procedures for scenarios such as shopping malls, supermarkets, pharmacies, commercial streets, production sites, logistics warehouses, customs, and industrial and commercial administration. The visual detection unit of the perception module uses a high-definition camera and a 3D laser scanner to collect data on the appearance, color, size, and defect characteristics of the object to be detected. The tactile detection unit uses a pressure sensor array to collect data on the hardness, elasticity, and surface roughness of the object to be detected. The olfactory detection unit uses a gas sensor array to collect data on the volatile gas composition and concentration of the object to be detected. The auditory detection unit uses a sound sensor to collect acoustic characteristic data of the object to be detected under vibration or impact. The spectral detection unit uses a near-infrared spectrometer to collect data on the composition and content characteristics of the object to be detected. In the quality inspection evaluation model of the data processing module, the feature weight calculation adopts a combination of the analytic hierarchy process (AHP) and the entropy weight method, and the weighting formula is as follows: ; in, For the first The combined weight of each feature, The weights of the analytic hierarchy process are... , For the first The weights of each feature are obtained through the analytic hierarchy process (AHP). For the first The weights of each feature are obtained using the entropy weighting method; The detection accuracy correction formula of the data processing module is used to eliminate the influence of environmental interference on the detection results under different scenarios. Its weighting formula is as follows: ; in, For the corrected feature data, The raw feature data collected by the perception module. This is the environmental interference correction factor. This represents the difference between the current scene environment parameters and the standard environment parameters. This is the overall weight of the feature; The data processing module determines the reliability of the quality inspection results using a confidence assessment formula, the weighting formula of which is: ; in For the confidence level of the quality inspection results and , For the first The detection similarity of each feature (i.e., the degree of matching between the features of the object to be detected and the standard features). This is the error coefficient during the detection process; when When a pre-set reliability threshold is set, the quality inspection results are deemed valid; when When this occurs, a re-detection process is triggered; The pricing calculation formula for the billing and settlement module during billing is as follows: ; in, For the final testing quote, Basic testing fees, For the detection time coefficient, This is the ratio of the actual testing time to the standard testing time. For the detection accuracy coefficient, This is the ratio of actual detection accuracy to standard detection accuracy. The scene complexity coefficient. This represents the complexity level of the current scenario. The interactive module's display unit uses a touch screen to display testing progress, results, pricing information, and operation instructions. The voice unit supports voice broadcasting of test results and receiving user commands. The printing unit prints paper test reports and payment receipts. The interactive module also supports data interaction with mobile terminals or a back-end management system via a wireless communication unit, enabling remote viewing and management of test data. The scene adaptation module supports custom scene configuration; users can input environmental parameters, quality inspection standards, and testing procedures for new scenes through the interactive module. The scene adaptation module verifies and stores the input data, achieving rapid adaptation to new scenes. Example 2

[0018] like Figure 1-2 As shown, this embodiment of the invention provides a multi-scenario, multi-category, and multi-functional quality inspection AI robot. The workflow steps of this multi-scenario, multi-category, and multi-functional quality inspection AI robot are as follows: Step 1: Scene adaptation; Once the robot enters the target scene, the scene adaptation module collects real-time environmental data through environmental sensors, matches it with pre-stored scene parameters, and automatically loads the corresponding quality inspection standards and testing procedures. If matching fails, the user can manually select a scene or perform custom configuration. Step 2: Test preparation; Users place the object to be detected in the detection area, select the detection mode through the interactive module, and input the basic information of the object to be detected; Step 3: Data Collection; The execution module controls the perception module to start the corresponding detection unit according to the selected detection mode, collects multi-dimensional feature data of the object to be detected, and displays the detection progress in real time during the collection process. Step 4: Data processing; The data processing module preprocesses the collected raw feature data, calculates the comprehensive feature weights using a combined weighting method, eliminates environmental interference using a detection accuracy correction formula, and finally determines the reliability of the quality inspection results using a confidence assessment formula. Step 5: Output the results; If the confidence level of the quality inspection result is ≥0.85, the interactive module displays the test result, and the billing and settlement module generates a quote based on the test parameters. If the confidence level is <0.85, the test is automatically re-tested, up to 3 times. If the result still does not meet the requirements, a manual review will be prompted. Step 6: Payment Settlement: The user completes the payment through the selected payment method. After successful payment, the interactive module prints the test report and payment voucher, and simultaneously synchronizes the test data and payment information to the back-end management system. The user can query the test data and payment records through the mobile terminal or the back-end management system. The robot then resets and waits for the next test task.

[0019] The following points should be noted in this article: 1. The accompanying drawings of the embodiments disclosed herein only relate to the structures involved in the embodiments disclosed herein; other structures can be referred to in general design.

[0020] 2. Where there is no conflict, the embodiments of this disclosure and the features in the embodiments can be combined with each other to obtain new embodiments.

[0021] Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

Claims

1. A multi-scenario, multi-category, multi-functional quality inspection AI robot, comprising a perception module, a data processing module, an execution module, an interaction module, a payment and settlement module, and a scenario adaptation module, characterized in that: The perception module is used to collect multi-dimensional feature data of the object to be detected, including a visual detection unit, a tactile detection unit, an olfactory detection unit, an auditory detection unit, and a spectral detection unit. The data processing module analyzes and processes the feature data based on a preset quality inspection evaluation model. The quality inspection evaluation model includes mathematical calculation logic for feature weight calculation, detection accuracy correction, and confidence assessment. The execution module is used to perform detection actions in different modes, supporting four detection modes: fast detection, slow detection, flow detection, and spot check. The interactive module is used to output detection results and receive user commands, and includes a display unit, a voice unit, and a printing unit; The billing and settlement module is used to generate a quotation and complete the payment based on the test type and results. It supports three payment methods: QR code payment, card payment and account deduction. After the payment is completed, a test report and a payment voucher are generated and output through the interaction module. The scenario adaptation module pre-stores environmental parameters, quality inspection standards, and testing procedures for scenarios such as shopping malls, supermarkets, pharmacies, commercial streets, production sites, logistics warehouses, customs, and industrial and commercial management.

2. The AI ​​robot for quality inspection in multiple scenarios and categories according to claim 1, characterized in that: The visual detection unit of the perception module uses a high-definition camera and a 3D laser scanner to collect data on the appearance, color, size, and defect characteristics of the object to be detected. The tactile detection unit uses a pressure sensor array to collect data on the hardness, elasticity, and surface roughness of the object to be detected. The olfactory detection unit uses a gas sensor array to collect data on the volatile gas composition and concentration of the object to be detected. The auditory detection unit uses a sound sensor to collect acoustic characteristic data of the object to be detected under vibration or impact. The spectral detection unit uses a near-infrared spectrometer to collect data on the composition and content characteristics of the object to be detected.

3. The AI ​​robot for quality inspection in multiple scenarios and categories according to claim 1, characterized in that: In the quality inspection evaluation model of the data processing module, the feature weight calculation adopts a combination of the analytic hierarchy process (AHP) and the entropy weight method, and the weighting formula is as follows: ; in, For the first The combined weight of each feature, The weights of the analytic hierarchy process are... , For the first The weights of each feature are obtained through the analytic hierarchy process (AHP). For the first The weights of each feature are obtained using the entropy weighting method.

4. The AI ​​robot for quality inspection in multiple scenarios and categories according to claim 1, characterized in that: The detection accuracy correction formula of the data processing module is used to eliminate the influence of environmental interference on the detection results under different scenarios. Its weighting formula is as follows: ; in, For the corrected feature data, The raw feature data collected by the perception module. This is the environmental interference correction factor. This represents the difference between the current scene environment parameters and the standard environment parameters. This represents the overall weight of the feature.

5. The AI ​​robot for quality inspection in multiple scenarios and categories according to claim 1, characterized in that: The data processing module determines the reliability of the quality inspection results using a confidence assessment formula, the weighting formula of which is: ; in For the confidence level of the quality inspection results and , For the first The detection similarity of each feature (i.e., the degree of matching between the features of the object to be detected and the standard features). This is the error coefficient during the detection process; when When a pre-set reliability threshold is set, the quality inspection results are deemed valid; when When this happens, a re-detection process is triggered.

6. The AI ​​robot for quality inspection in multiple scenarios and categories according to claim 1, characterized in that: The pricing calculation formula for the billing and settlement module during billing is as follows: ; in, For the final testing quote, Basic testing fees, For the detection time coefficient, This is the ratio of the actual testing time to the standard testing time. For the detection accuracy coefficient, This is the ratio of actual detection accuracy to standard detection accuracy. The scene complexity coefficient. This represents the complexity level of the current scenario.

7. The AI ​​robot for quality inspection in multiple scenarios and categories according to claim 1, characterized in that: The interactive module's display unit uses a touch screen to display testing progress, testing results, pricing information, and operation instructions; the voice unit supports voice broadcasting of testing results and voice reception of user commands; the printing unit is used to print paper versions of testing reports and payment receipts; the interactive module also supports data interaction with mobile terminals or back-end management systems via a wireless communication unit, enabling remote viewing and management of testing data.

8. The AI ​​robot for quality inspection in multiple scenarios and categories according to claim 1, characterized in that: The scene adaptation module supports custom scene configuration. Users can input environmental parameters, quality inspection standards and testing procedures for new scenes through the interaction module. The scene adaptation module verifies and stores the input data to achieve rapid adaptation to new scenes.

9. The AI ​​robot for quality inspection in multiple scenarios and categories according to claim 1, characterized in that: The workflow steps of this multi-scenario, multi-category, and multi-functional quality inspection AI robot are as follows: Step 1: Scene adaptation; Once the robot enters the target scene, the scene adaptation module collects real-time environmental data through environmental sensors, matches it with pre-stored scene parameters, and automatically loads the corresponding quality inspection standards and testing procedures. If matching fails, the user can manually select a scene or perform custom configuration. Step 2: Test preparation; Users place the object to be detected in the detection area, select the detection mode through the interactive module, and input the basic information of the object to be detected; Step 3: Data Collection; The execution module controls the perception module to start the corresponding detection unit according to the selected detection mode, collects multi-dimensional feature data of the object to be detected, and displays the detection progress in real time during the collection process. Step 4: Data processing; The data processing module preprocesses the collected raw feature data, calculates the comprehensive feature weights using a combined weighting method, eliminates environmental interference using a detection accuracy correction formula, and finally determines the reliability of the quality inspection results using a confidence assessment formula. Step 5: Output the results; If the confidence level of the quality inspection result is ≥0.85, the interactive module displays the test result, and the billing and settlement module generates a quote based on the test parameters. If the confidence level is <0.85, the test is automatically re-tested, up to 3 times. If the result still does not meet the requirements, a manual review will be prompted. Step 6: Payment Settlement: The user completes the payment through the selected payment method. After successful payment, the interactive module prints the test report and payment voucher, and simultaneously synchronizes the test data and payment information to the back-end management system. The user can query the test data and payment records through the mobile terminal or the back-end management system. The robot then resets and waits for the next test task.

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