A recycling behavior identity tracing method based on face recognition

Abstract

The application provides a recycling behavior identity tracing method based on face recognition, comprising the following steps: step one: user face information pre-registration and feature library construction, building an intelligent recycling background management system, establishing a user exclusive face feature database, receiving a recycling request of a user, and obtaining multi-modal feature information of the user, including basic information and face information, and storing the multi-modal feature information of the user into the face feature database; the recycling behavior and the actual operator are one-to-one bound through the face recognition technology, the problem of inaccurate identity tracing caused by account transfer, proxy and false investment under the traditional card swiping and code scanning verification mode is solved, the responsibility subject of behaviors such as illegal investment, malicious damage to equipment can be accurately positioned, the accurate landing of illegal dissuasion and punishment can be ensured, and the standardized management of the recycling system is promoted.

Description

Technical Field

[0001] This invention belongs to the field of data processing, specifically a method for tracing the identity of recycling behavior based on facial recognition. Background Technology

[0002] With the intelligent upgrading of urban waste sorting and waste recycling industries, self-service intelligent recycling equipment has been widely used in residential communities, schools, and commercial districts, replacing the traditional manual recycling model and significantly improving recycling efficiency. However, current mainstream intelligent recycling systems suffer from numerous technical flaws in their identity verification and behavior recording methods, severely hindering the standardized and refined management of the recycling industry.

[0003] Existing recycling systems generally suffer from a lack of identity traceability: The disposal process often employs an open disposal model without identity verification, making it difficult to accurately identify the responsible party when mixed disposal or illegal disposal occurs, hindering the effective implementation of warnings and penalties for violations. Some recycling systems use card or QR code verification, which can lead to account transfers and fraudulent disposal, making it impossible to match disposal behavior with the actual operator, resulting in insufficient accuracy in identity traceability. Furthermore, existing recycling data is mostly stored in a fragmented and unlinked manner, making it susceptible to tampering and preventing the formation of complete and traceable recycling ledgers. This hinders closed-loop management of the recycling supervision system and fails to meet the current needs for refined management of waste sorting.

[0004] In summary, this invention provides a method for tracing the identity of recycling behavior based on facial recognition to solve the above problems. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method for tracing the identity of recycling behavior based on facial recognition, thereby solving the problems in existing technologies where recycling data is mostly stored in a scattered manner without binding, data is easily tampered with, and a complete and traceable recycling ledger cannot be formed.

[0006] A method for tracing the identity of recycling behavior based on facial recognition includes the following steps:

[0007] Step 1: Pre-register user facial information and build a feature database. Set up an intelligent recycling backend management system, establish a user-specific facial feature database, receive user recycling requests, and obtain the user's multimodal feature information, including basic information and facial information. Store the user's multimodal feature information into the facial feature database.

[0008] Step 2: Real-time dynamic facial verification before recycling. When a user initiates a disposal operation on the smart recycling device, the device automatically wakes up the front-end facial recognition module, collects the dynamic characteristics of the user's face in real time, preprocesses and extracts features from the real-time collected facial images, generates a real-time facial feature vector, determines whether the user is a registered user in the database, compares and matches it with the user's facial feature database stored in the cloud, and calculates the feature similarity.

[0009] Step 3: Collect and bind all-dimensional data on recycling behavior. After identity verification is passed, the device starts the delivery monitoring function to record all-dimensional data of this recycling behavior in real time, and all data is bound to the facial identity information of the currently verified user in real time.

[0010] Step 4: Data encryption storage and traceability archive generation. The data on the recycling behavior, the video delivery, and the facial verification records bound to the facial identity information are encrypted and packaged. The data is then encrypted and stored on the blockchain using a blockchain hash algorithm to prevent data tampering and loss.

[0011] Step 5: Abnormal behavior identification and precise traceability and handling. Real-time monitoring of violations during the recycling and placement process. The monitoring function of the intelligent recycling equipment is used to obtain image data of the recycling process as the first operation image data. The appearance image of the recyclable is collected by the internal monitoring equipment, and the weight of the recyclable is verified by the internal weight sensor to make a multi-source judgment on the information of the recyclable.

[0012] Furthermore, in step one, the user's multimodal feature information mainly includes basic information and facial information, wherein:

[0013] Basic information includes name, mobile phone number, residential building, user ID, etc.

[0014] The process involves collecting facial information and preprocessing the collected facial images, including image grayscale conversion, noise reduction, size normalization, facial landmark detection, and removing blurry, backlit, or occluded invalid images.

[0015] Furthermore, in step one, the basic information and facial information are encrypted and bound, stored in the cloud feature database, and the user identity is established. At the same time, the registered user information is desensitized to ensure the privacy and security of the user's biometric information.

[0016] Furthermore, in step two, if the similarity is greater than the preset matching threshold, the identity verification is deemed successful, the user's recycling request is accepted, the device's delivery permission is unlocked, and a recycling behavior tag is created for the user; if the matching fails, multiple people are detected on camera, or the face is severely obscured, the delivery operation is immediately blocked, the user's recycling request is rejected, a pop-up window is displayed indicating an identity verification error, and the abnormal scene is captured for evidence preservation.

[0017] Furthermore, the recycling behavior label includes historical records and credit ratings, with the historical records including the number of recyclings, the quantity of recyclings, and the determination of recyclables.

[0018] Furthermore, in step three, the collected data includes the delivery time, delivery device location, type of recyclable items, basic information of the recyclables, delivery video recordings, operation process trajectory, and equipment operating status.

[0019] Furthermore, in step four, the system automatically generates a unique traceability QR code and traceability number for each recycling activity, constructing an electronic file for each user's recycling behavior. The file records all user placement, verification, and abnormal operation records throughout the process, forming a permanently traceable data ledger. The backend system supports searching traceability files by multiple dimensions such as user identity, placement time, equipment location, and recyclable category. The unique traceability QR code serves as a unique identification code for each recyclable item, recording a fine-grained operation log of the recyclable item's internal flow during the recycling process. When a traceability request is made, the cloud monitoring platform verifies the completeness of the traceability data by comparing the hash value of the unique traceability QR code with the fine-grained operation log.

[0020] Furthermore, in step five, violations include mixed disposal, illegal disposal of hazardous waste, frequent abnormal verification, and malicious touching or damage to equipment.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] 1. This invention uses facial recognition technology to bind recycling and disposal behavior to the actual operator, solving the problem of inaccurate identity tracing caused by account transfer and impersonation under the traditional card swiping and QR code verification mode. It can accurately locate the responsible party for illegal disposal, malicious damage to equipment, etc., ensuring that the dissuasion and punishment of violations can be accurately implemented, and promoting the standardized management of the recycling system.

[0023] 2. This invention binds the entire recycling process data to the user's facial identity and encrypts and stores it on the blockchain using a blockchain hash algorithm. This avoids the problems of data being easily tampered with or lost in the traditional decentralized storage mode. It can form a complete, permanent, and traceable recycling ledger, which meets the current regulatory needs for refined management of item recycling and helps to form a closed-loop management of recycling supervision.

[0024] 3. This invention desensitizes personal information during the user registration stage and encrypts and stores all user biometric information. While achieving accurate traceability, it fully protects users' personal privacy and information security, balances regulatory effectiveness with user information security needs, and improves the security of identity verification. Attached Figure Description

[0025] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0026] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0027] like Figure 1 As shown, this invention provides a method for tracing the identity of recycling behavior based on facial recognition, including the following steps:

[0028] Step 1: Pre-register user facial information and build a feature database. Set up an intelligent recycling backend management system, establish a user-specific facial feature database, receive user recycling requests, and obtain the user's multimodal feature information, including basic information and facial information. Store the user's multimodal feature information into the facial feature database.

[0029] Step 2: Real-time dynamic facial verification before recycling. When a user initiates a disposal operation on the smart recycling device, the device automatically wakes up the front-end facial recognition module, collects the dynamic characteristics of the user's face in real time, preprocesses and extracts features from the real-time collected facial images, generates a real-time facial feature vector, determines whether the user is a registered user in the database, compares and matches it with the user's facial feature database stored in the cloud, and calculates the feature similarity.

[0030] Step 3: Collect and bind all-dimensional data on recycling behavior. After identity verification is passed, the device starts the delivery monitoring function to record all-dimensional data of this recycling behavior in real time, and all data is bound to the facial identity information of the currently verified user in real time.

[0031] Step 4: Data encryption storage and traceability archive generation. The data on the recycling behavior, the video delivery, and the facial verification records bound to the facial identity information are encrypted and packaged. The data is then encrypted and stored on the blockchain using a blockchain hash algorithm to prevent data tampering and loss.

[0032] Step 5: Abnormal behavior identification and precise traceability and handling. Real-time monitoring of violations during the recycling and placement process. The monitoring function of the intelligent recycling equipment is used to obtain image data of the recycling process as the first operation image data. The appearance image of the recyclable is collected by the internal monitoring equipment, and the weight of the recyclable is verified by the internal weight sensor to make a multi-source judgment on the information of the recyclable.

[0033] In one embodiment of the present invention, in step one, the user's multimodal feature information mainly includes basic information and facial information, wherein:

[0034] Basic information includes name, mobile phone number, residential building, user ID, etc.

[0035] The process involves collecting facial information and preprocessing the collected facial images, including image grayscale conversion, noise reduction, size normalization, facial landmark detection, and removing blurry, backlit, or occluded invalid images.

[0036] Relying on facial recognition and liveness detection technology, the system uses the user's unique biometric features as the basis for identity verification, replacing traditional account, card, and QR code verification methods. This eliminates problems such as proxy submission, impersonation, and account sharing at the source, ensuring that the recycling behavior corresponds one-to-one with the real natural person, resulting in high accuracy in tracing the source.

[0037] In one embodiment of the present invention, in step one, basic information and facial information are encrypted and bound, stored in a cloud feature database, and user identity is established. At the same time, the registered user information is desensitized to ensure the privacy and security of user biometric information.

[0038] It only stores encrypted facial feature vectors and does not retain the original images. Combined with data encryption and blockchain technology, it comprehensively protects users' biometric privacy and security. At the same time, it supports offline verification and 24 / 7 operation, and can be adapted to various smart recycling scenarios such as communities, campuses, factories, and business districts. It has extremely strong versatility and practicality.

[0039] In one embodiment of the present invention, in step two, when the similarity is greater than the preset matching threshold, the identity verification is deemed to be successful. The user's recycling request is then accepted, the device's delivery permission is unlocked, and a recycling behavior tag is created for the user. If the matching fails, multiple people are detected on camera, or the face is severely obscured, the delivery operation is immediately blocked, the user's recycling request is rejected, and a pop-up window is displayed indicating an identity verification error. At the same time, the abnormal image is captured and stored as evidence.

[0040] As one embodiment of the present invention, the recycling behavior label includes historical records and credit ratings. The historical records include the number of recyclings, the quantity of recyclings, and the determination of the recyclables.

[0041] In one embodiment of the present invention, step three involves collecting data including the delivery time, delivery device location, type of recyclable items, basic information of recyclable items, delivery video recordings, operation process trajectory, and equipment operating status.

[0042] In one embodiment of the present invention, in step four, the system automatically generates a unique traceability QR code and traceability number for each recycling activity, constructing an electronic file of the user's personal recycling behavior. The file records all user placement, verification, and abnormal operation records throughout the process, forming a permanently traceable data ledger. The backend system supports searching traceability files by multiple dimensions such as user identity, placement time, equipment location, and recyclable category. The unique traceability QR code serves as a unique identification code for a single recyclable item, recording a fine-grained operation log of the recyclable item's internal flow during the recycling process. When a traceability request is made, the cloud monitoring platform verifies the completeness of the traceability data by comparing the hash value of the unique traceability QR code with the fine-grained operation log.

[0043] The system constructs an integrated traceability archive encompassing "facial recognition, delivery data, operation video, and anomaly records," combined with blockchain-encrypted storage. This ensures the data is tamper-proof and permanently retained, supports multi-dimensional and precise retrieval, and significantly improves the precision of recycling supervision.

[0044] In one embodiment of the present invention, in step five, violations include mixed disposal, illegal disposal of hazardous waste, frequent abnormal verification, and malicious touching or damage to equipment.

[0045] It automatically identifies various illegal dumping and malicious damage behaviors, locks down the perpetrators in real time and retains complete evidence data, realizes automatic early warning, accurate tracing and closed-loop handling of abnormal behavior, reduces the cost of manual supervision and standardizes the recycling market order.

[0046] The embodiments of the present invention are given for the purposes of illustration and description. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for tracing the identity of recycling behavior based on facial recognition, characterized in that, Includes the following steps: Step 1: Pre-register user facial information and build a feature database. Set up an intelligent recycling backend management system, establish a user-specific facial feature database, receive user recycling requests, and obtain the user's multimodal feature information, including basic information and facial information. Store the user's multimodal feature information into the facial feature database. Step 2: Real-time dynamic facial verification before recycling. When a user initiates a disposal operation on the smart recycling device, the device automatically wakes up the front-end facial recognition module, collects the dynamic characteristics of the user's face in real time, preprocesses and extracts features from the real-time collected facial images, generates a real-time facial feature vector, determines whether the user is a registered user in the database, compares and matches it with the user's facial feature database stored in the cloud, and calculates the feature similarity. Step 3: Collect and bind all-dimensional data on recycling behavior. After identity verification is passed, the device starts the delivery monitoring function to record all-dimensional data of this recycling behavior in real time, and all data is bound to the facial identity information of the currently verified user in real time. Step 4: Data encryption storage and traceability archive generation. The data on the recycling behavior, the video delivery, and the facial verification records bound to the facial identity information are encrypted and packaged. The data is then encrypted and stored on the blockchain using a blockchain hash algorithm to prevent data tampering and loss. Step 5: Abnormal behavior identification and precise traceability and handling. Real-time monitoring of violations during the recycling and placement process. The monitoring function of the intelligent recycling equipment is used to obtain image data of the recycling process as the first operation image data. The appearance image of the recyclable is collected by the internal monitoring equipment, and the weight of the recyclable is verified by the internal weight sensor to make a multi-source judgment on the information of the recyclable.

2. The method for tracing the identity of recycling behavior based on face recognition as described in claim 1, characterized in that, In step one, the user's multimodal feature information mainly includes basic information and facial information, wherein: Basic information includes name, mobile phone number, residential building, user ID, etc. The process involves collecting facial information and preprocessing the collected facial images, including image grayscale conversion, noise reduction, size normalization, facial landmark detection, and removing blurry, backlit, or occluded invalid images.

3. The method for tracing the identity of recycling behavior based on face recognition as described in claim 1, characterized in that, In step one, basic information and facial information are encrypted and bound, stored in a cloud feature database, and user identity is established. At the same time, the registered user information is desensitized to ensure the privacy and security of users' biometric information.

4. The method for tracing the identity of recycling behavior based on face recognition as described in claim 1, characterized in that, In step two, if the similarity is greater than the preset matching threshold, the identity verification is deemed successful. The user's recycling request is then accepted, the device's delivery permission is unlocked, and a recycling behavior tag is created for the user. If the matching fails, multiple people are detected on camera, or the face is severely obscured, the delivery operation is immediately blocked, the user's recycling request is rejected, and a pop-up window is displayed indicating an identity verification error. At the same time, the abnormal scene is captured and preserved as evidence.

5. The method for tracing the identity of recycling behavior based on face recognition as described in claim 4, characterized in that, The recycling behavior label includes historical records and credit ratings. The historical records include the number of recyclings, the quantity of recyclings, and the determination of recyclables.

6. The method for tracing the identity of recycling behavior based on face recognition as described in claim 1, characterized in that, In step three, the collected data includes the delivery time, delivery equipment location, type of recyclable items, basic information of recyclable items, delivery video recordings, operation process trajectory, and equipment operating status.

7. The method for tracing the identity of recycling behavior based on face recognition as described in claim 1, characterized in that, In step four, the system automatically generates a unique traceability QR code and traceability number for each recycling activity, constructing an electronic file for each user's recycling behavior. The file records all user placement, verification, and abnormal operation records throughout the process, forming a permanently traceable data ledger. The backend system supports searching traceability files by multiple dimensions such as user identity, placement time, equipment location, and recyclable category. The unique traceability QR code serves as a unique identification code for each recyclable item, recording a fine-grained operation log of the recyclable item's internal flow during the recycling process. When a traceability request is made, the cloud monitoring platform verifies the completeness of the traceability data by comparing the hash value of the unique traceability QR code with the fine-grained operation log.

8. The method for tracing the identity of recycling behavior based on face recognition as described in claim 1, characterized in that, In step five, violations include mixed disposal, improper disposal of hazardous waste, frequent abnormal verification, and malicious touching or damage to equipment.

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