Terminal consumption behavior verification method and system based on vacancy state identification and adaptive verification

Abstract

The application discloses a terminal consumption behavior verification method and system based on vacancy state recognition and adaptive verification, acquires a verification request submitted by a terminal user; the verification request comprises a package image and a package unique identification code; the state of the package is judged by using the package image; the state of the package comprises a vacancy state of content exhaustion and a non-vacancy state of content non-exhaustion; if the state of the package is the vacancy state, terminal consumption behavior data records responding to the package unique identification code are generated. The application improves the reliability and confidence of consumption verification.

Description

Technical Field

[0001] This invention relates to the fields of Internet of Things, computer vision and data security technology, and in particular to a terminal consumer behavior verification method and system based on the identification and adaptive verification of the empty state of specific commercial packaging bags (empty state: the empty state where the goods have been used up and the contents are basically exhausted). Background Technology

[0002] In industries producing bulk commodities such as feed, fertilizer, and cement, where flexible woven bags are the primary packaging material, companies have long faced two core pain points: distorted end-consumer data and the interception of marketing resources during the distribution process. Existing technological solutions for acquiring data and incentivizing users all suffer from the following fundamental flaws:

[0003] 1. The fundamental flaw of the physical reward card / built-in code solution lies in the fact that its verification code is inside the packaging bag, which leads to the following "data black box" in the entire process of product circulation and consumption:

[0004] a. Untraceable logistics and warehousing: Because the barcodes are sealed inside the bags, non-destructive scanning and verification are impossible at key stages such as warehousing, logistics, and transshipment after the goods leave the factory. The system cannot know the specific location of the goods or whether they have been moved, leaving huge room for internal fraud or cross-channel sales.

[0005] b. The status of the distribution channels is completely unknowable: After receiving the goods, the manufacturer has no way of monitoring whether they have been sold or stockpiled in the warehouse. This is because the code is only exposed and potentially scanned when the end consumer opens the package; before that, the product is "invisible" in the digital world. This makes it extremely costly to prevent distributors from stockpiling goods for arbitrage through scanning.

[0006] c. Consumer verification is completely decoupled from physical packaging: Even if consumers open the package, receive the reward card, and scan the code, the system can only record "a certain code has been scanned," but it cannot answer key questions such as "Is the bag of feed corresponding to this code really used up?" or "Is this code the same physical entity as the empty packaging bag?"

[0007] 2. Plain label scheme: Easily scanned and copied in batches, unable to be linked to real consumer behavior.

[0008] 3. General QR code scanning solution: It can only verify "the code has been scanned", but cannot verify "the item has been consumed". The data confidence is low and it cannot prevent dealers from hoarding QR codes for arbitrage.

[0009] Therefore, existing solutions cannot solve the fundamental problem of how to remotely and automatically verify that standard packaged goods have been actually consumed, resulting in missing or low-confidence end-consumer data, and making it difficult to accurately quantify and effectively incentivize the labor value at the end of the industrial chain. Summary of the Invention

[0010] The technical problem to be solved by this invention is to provide a terminal consumption behavior verification method and system based on idle state identification and adaptive verification, which addresses the shortcomings of existing technologies in the above-mentioned scenarios in the bulk commodity industries such as feed, fertilizer, and cement, thereby improving the reliability and confidence of consumption verification.

[0011] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a terminal consumption behavior verification method based on idle state recognition and adaptive verification, comprising the following steps:

[0012] Obtain the verification request submitted by the end user; the verification request includes a packaging image and a unique packaging identifier.

[0013] The packaging image is used to determine the state of the packaging; the state of the packaging includes an empty state where the contents are exhausted and a non-empty state where the contents are not exhausted.

[0014] If the packaging is in an empty state, a terminal consumer behavior data record is generated in response to the unique identifier of the packaging.

[0015] Preferably, determining the state of the packaging using the packaging image includes:

[0016] Determine whether the unique identification code of the packaging is valid and not marked as consumed; if so, verify the credibility of the source of the packaging image; if the source is credible, determine the confidence level of empty bag status identification based on the image, and determine the packaging status according to the relationship between the confidence level and a set threshold.

[0017] This invention constructs a dual anti-counterfeiting verification system to ensure an extremely high level of confidence in end-consumer data:

[0018] The first layer of anti-counterfeiting: verification of the credibility of the image source. Technical means are used to ensure that the packaging images submitted by end users are real-time and authentic, preventing the use of forged, altered, or pre-prepared images for deception, and providing a reliable data foundation for subsequent judgment.

[0019] Furthermore, after acquiring the packaging image, the system parses the packaging image and extracts the identification code information presented in the image; if the extracted identification code information is inconsistent with the unique packaging identification code in the verification request, the image source is determined to be untrustworthy, the process is terminated, and an exception log is recorded.

[0020] Based on the successful verification of the image source's credibility, this invention further implements a second layer of anti-counterfeiting: verification of the packaging's physical state authenticity. In addition to the credibility of the image source, computer vision technology is used to analyze the image content to accurately determine whether the packaging is in an empty state where the contents have been exhausted, preventing the use of unconsumed goods to falsely claim that consumption has been completed. These two anti-counterfeiting mechanisms work synergistically to form a complete and reliable verification chain from the data collection source to the physical state assessment.

[0021] Furthermore, this invention extracts and analyzes specific physical deformation characteristics of the packaging bag as its contents are depleted, serving as a key basis for determining its empty state. These physical deformation characteristics include, but are not limited to: the ratio change between the visual size of the unique packaging identifier and the visual length of the packaging bag, and the texture and distribution characteristics of wrinkles on the packaging bag surface.

[0022] The system extracts one or more physical deformation features using computer vision technology and performs feature fusion and collaborative decision-making with a deep learning-based image classification model, thereby constructing a multi-layered, highly robust mechanism for verifying the authenticity of the packaging's physical state. This mechanism can effectively distinguish between natural deformation caused by the depletion of contents and artificial forgery, greatly improving the accuracy of verification and anti-cheating capabilities.

[0023] The method of the present invention further includes:

[0024] The terminal reward distribution signal is triggered based on the data records.

[0025] Before triggering the terminal reward distribution signal based on the data records, the following steps are also included:

[0026] Determine whether the packaging is in a high-risk area. If so, the process ends; otherwise, generate a terminal consumer behavior data record in response to the unique identifier of the packaging.

[0027] The specific implementation process of determining the state of packaging using the packaging image includes:

[0028] 1) Determine if the confidence level for identifying the empty bag status is not less than the first preset threshold. If yes, proceed to step 2. Otherwise, determine if the confidence level for identifying the empty bag status is greater than the second preset threshold. If yes, verify the status of the packaging, including whether the contents are exhausted. If exhausted, proceed to step 2.

[0029] 2) Determine whether the unique identification code of the packaging is valid, and if the unique identification code of the packaging is not marked as consumed, then determine that the state of the packaging is an empty state where the contents are exhausted;

[0030] Wherein, the first set threshold is greater than the second set threshold.

[0031] If the confidence level of the empty bag status identification is not greater than the second set threshold, then the process ends.

[0032] After obtaining the verification request submitted by the end user, and before determining the status of the packaging using the packaging image, the process further includes:

[0033] Determine if the total incentive amount is less than a set threshold. If so, use the packaging image to determine the packaging status; otherwise, end the process.

[0034] As an inventive concept, the present invention also provides a terminal consumer behavior verification system based on idle state recognition and adaptive verification, including a memory, a processor, and a computer program stored in the memory; the processor executes the computer program to implement the steps of the above method.

[0035] As an inventive concept, the present invention also provides a terminal device, including a memory, a processor, and a computer program stored in the memory; the processor executes the computer program to implement the steps of the above method.

[0036] As an inventive concept, the present invention also provides a computer-readable storage medium having a computer program / instructions stored thereon; characterized in that the computer program / instructions, when executed by a processor, implement the steps of the above-described method.

[0037] As an inventive concept, the present invention also provides a computer program product, including a computer program; characterized in that, when the computer program is executed by a processor, it implements the steps of the above-described method.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0039] 1. This invention provides a reliable verification mechanism that uses "packaging in an empty state" as physical evidence of completed consumption, ensuring that subsequent operations such as marketing incentives and data recording are strongly linked to actual end-consumer behavior;

[0040] 2. This invention constructs an adaptive model that can intelligently switch the verification rigor according to the commodity circulation path and business scenario (such as a high-reliability direct sales model or traditional multi-level distribution), ensuring that the method can operate safely and effectively in complex business environments;

[0041] 3. This invention creates an automated generation pipeline from physical consumption events to high-confidence digital records, providing key and reliable data for the industrial internet.

[0042] 4. This invention constructs a dual anti-counterfeiting system that combines image source credibility verification with packaging physical state authenticity verification. It ensures the reliability of consumer verification from two dimensions: data source and physical state, forming a closed-loop anti-fraud mechanism and significantly improving the robustness and practicality of the system in complex business environments.

[0043] 5. This invention creatively transforms the physical deformation patterns of packaging bags in an empty state (specifically manifested as changes in the visual proportion between the identification code and the bag body) into calculable and verifiable key features, and deeply integrates them with deep learning-based visual recognition technology. This multi-feature decision system based on "physical laws + artificial intelligence" not only significantly improves the accuracy and efficiency of empty bag recognition, but also fundamentally establishes a three-dimensional defense against image forgery attacks. This is because the cost of simultaneously forging geometric features that conform to physical deformation patterns and natural visual texture features is extremely high, thus constructing a strong technical barrier.

[0044] 6. The verification process designed in this invention follows the principle of progressive trust building and resource allocation. The system sequentially executes identifier code status verification, image source verification, business trusted credential verification, and multi-feature idle state identification, and performs verification according to management rules and risk control requirements. The computational complexity and resource consumption of each verification level increase progressively, and the result of each level is a necessary condition to enter the next level. This design ensures that the system can achieve the highest confidence level verification of terminal consumption behavior with the lowest overall operating cost, while possessing strong anti-attack resistance and business scalability. Attached Figure Description

[0045] Figure 1 This is a flowchart of the method in the preliminary pilot system (simplified version) of this invention; Figure 2 This is a schematic diagram of the system architecture of the preliminary pilot system (simplified version) in an embodiment of the present invention; Figure 3 This is a flowchart of the post-promotion system (complete version) in an embodiment of the present invention; Figure 4 This is a schematic diagram of the system architecture of the post-promotion system (complete version) in an embodiment of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, 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.

[0047] Example 1

[0048] like Figure 1 and Figure 3 As shown, Embodiment 1 of the present invention provides an adaptive terminal consumption behavior verification and data collection method with "vacancy identification as the core, delivery certificate as the basis, and blockchain consensus as the enhancement". The embodiment of the present invention introduces a "dual-path decision model based on delivery credibility".

[0049] Embodiment 1 of the present invention mainly includes the following steps:

[0050] S1: Identifier Code Global State Consensus Verification - Code Verification (Key Security Pre-emptive and Blocking Steps).

[0051] The end user first scans the unique identification code on the packaging using a mobile application. Upon receiving this identification code, the system must first verify it before performing any subsequent operations (especially image acquisition). This verification aims to globally confirm the validity of the identification code (e.g., whether it is activated) and its unconsumed state. Preferably, this verification is performed through a trusted state consensus system based on a consortium blockchain.

[0052] Verification process: The blockchain network is queried for the status record chain of the identifier code, and the following is verified: (a) the latest status is a pre-consumption status (e.g., "shipped"); (b) this status is issued by a registered authoritative node (e.g., the manufacturer's server); and (c) there is no subsequent "consumed" status on the chain. This distributed consensus mechanism ensures the global trustworthiness and tamper-proof nature of the verification results.

[0053] If the verification fails, the process will terminate immediately and the user will be prompted with an error message to prevent invalid or illegal requests from entering subsequent stages, thus protecting system resources and user experience.

[0054] S2: Photo guidance and image source credibility verification - image verification (second layer of anti-counterfeiting).

[0055] After the S1 identification code verification is successful, the system guides the user to the image acquisition interface. The end user submits a verification request through the mobile application, which must include at least an image of the packaging and a unique packaging identification code.

[0056] To construct the first layer of anti-counterfeiting (image source credibility verification), the mobile application is configured to perform the following steps:

[0057] Forced real-time shooting: Uses the device's native camera interface to take pictures, and prohibits access to the local photo album.

[0058] Add multiple anti-counterfeiting features: At the moment of shooting, automatically overlay an invisible or visible digital watermark containing a server timestamp, session random code, and geostamp (if feasible) onto the image. At the same time, record and extract the EXIF ​​metadata of the image file (including shooting time, device model, GPS coordinates, etc.).

[0059] Instant encapsulation and uploading: The image with added anti-counterfeiting features and extracted metadata are encapsulated together with the identification code into a verification request and immediately submitted to the server. The entire collection process should be completed within a single continuous session to minimize the chance of tampering during the process.

[0060] Upon receiving a verification request, the system first performs image source verification, which involves decrypting and verifying the anti-counterfeiting label, analyzing the logical consistency of EXIF ​​metadata, etc., to confirm that the image was captured in real time and has not been tampered with. If the verification fails, the process terminates immediately; if it succeeds, the system further parses the packaging image to extract the identification code information presented in the image; if the extracted identification code information is inconsistent with the unique packaging identification code obtained in S1, the verification is deemed to have failed, the process terminates, and an exception log is recorded. If they match, the process proceeds to S3.

[0061] S3: Path Decision and Differentiated Idle State Verification - Credit Verification (Core Adaptive Process).

[0062] Based on the successful verification of the S2 image source, the system executes the following steps in sequence:

[0063] Path Decision: The system queries whether there exists a "verified data credential delivered to the end consumer" associated with the identifier (this credential is provided by an independent and trusted distribution traceability system). If it exists, it proceeds to Path 1 (agile verification in a high-reliability delivery scenario); if it does not exist or cannot be verified, it proceeds to Path 2 (strict verification in a standard scenario).

[0064] Path 1 (Agile Validation in High-Reliability Delivery Scenarios)

[0065] Applicable scenarios: The commodity circulation path is highly transparent and traceable, and there are already strong and reliable delivery certificates (such as orders from factory to end).

[0066] Technical features: It adopts a relatively lenient verification threshold and a highly automated processing flow in order to pursue the ultimate verification efficiency and user experience.

[0067] Commercial applications: It can support large-scale commercial operations with high-amount, high-frequency incentives, and match marketing strategies for high-value customers or high-margin products.

[0068] Path Two (Strict Verification in Standard Scenarios)

[0069] Applicable scenarios: Goods are distributed through traditional multi-level channels and lack strong and reliable delivery certificates in advance.

[0070] Technical features: It adopts stricter verification thresholds and introduces enhanced risk control mechanisms such as manual review to cope with higher uncertainty and potential risks.

[0071] Commercial Applications: By flexibly configuring incentive strategies (such as incentive probability and value) and risk control rules, large-scale commercial operations can be supported while ensuring controllable risks. Its design naturally guides ecosystem participants to gradually accumulate credit and migrate towards a more trustworthy path.

[0072] S4: Differentiated Vacancy Status Verification - Vacancy Bag Verification (Core AI and Multi-Feature Fusion Decision)

[0073] The identification of empty states in both Path 1 and Path 2 is based on a multi-feature fusion decision engine. The engine's workflow includes: a) Deformation ratio feature extraction: Using the known physical dimensions of the unique packaging identifier as a benchmark, image analysis techniques are used to calculate its visual size in the current image, and the visual length of the packaging bag is further estimated. The ratio R_current of "identifier visual size / bag visual length" is calculated; b) Visual content feature extraction: A visual AI model trained on a deep convolutional neural network is invoked to analyze visual content features such as the packaging bag's wrinkles, texture, shape, and bottom state in the image, outputting a confidence score C_ai for an empty state; c) Feature fusion decision:

[0074] For Path 1 (high-reliability delivery scenario), the system compares R_current with a "baseline interval for empty bag ratio" learned from historical data, and combines this with a relatively lenient AI confidence threshold (e.g., C_ai ≥ 80%) for a comprehensive judgment. When both requirements are met, the process proceeds quickly.

[0075] For path two (standard scenario), the system employs a stricter strategy. Besides requiring R_current to be significantly greater than the theoretical full-bag ratio R_full recorded at the factory (e.g., R_current > 1.1 × R_full), it also requires the AI ​​confidence score C_ai to reach a higher threshold (e.g., ...). If the conditions are not met, the system will be forced to undergo manual review (e.g., only if ≥85% is met). This multi-feature fusion method, by combining physical geometric laws with visual semantic understanding, makes the judgment of vacancy status objective, accurate, and anti-counterfeiting.

[0076] S5: Management Rules and Risk Control Verification – Verification of Management Rules.

[0077] After the vacancy status verification passes, the system performs a final risk control verification based on preset business rules. These rules include, but are not limited to:

[0078] Anti-counterfeiting verification: Verify whether the current user's geographical location matches the sales area bound when the identification code was activated.

[0079] Geofence verification: Verifies whether the user is in a high-risk area preset by the system (such as a dealer warehouse).

[0080] Incentive pool circuit breaker check: Determines whether the cumulative incentives issued have reached the preset upper limit.

[0081] These rules can be configured differently depending on the selected path (Path 1 or Path 2). For example, in Path 1 (High Trust), geofence verification can be skipped or relaxed; in Path 2 (Standard), all verifications can be forced. If any verification fails, the process terminates; if all verifications pass, it proceeds to S6.

[0082] S6: Credible consumption data record generation and triggering - result output.

[0083] The system generates a structured data record labeled "Verified Empty Bag Consumption" and stores it in a trusted database only if all verification steps of the selected path pass. This record can securely trigger subsequent applications (such as issuing incentives or updating user profiles).

[0084] The present invention also provides a corresponding system, whose key modules include image anti-counterfeiting acquisition and source verification, path decision engine, vacancy status recognition engine, blockchain verification interface, manual review arbitration platform and data record generation module.

[0085] This invention pioneers an intelligent verification logic based on "delivery credibility". The system can automatically route requests to verification paths of different levels of stringency based on whether there are "credible delivery credentials" (such as logistics systems and data that enable dual electronic signatures between factory clients and farmers) in the business scenario.

[0086] Strategic dual-path design: This is reflected in two flexibly configurable operating models:

[0087] High-trust direct sales path: suitable for innovative business models. When the system confirms the existence of a "trusted delivery certificate," it can match high incentives and adopt a strong verification combination including "blockchain consensus verification" to ensure that the data has legal-grade credibility and can be directly used in serious scenarios such as insurance and high-limit loans.

[0088] Traditional Channel Adaptation Path: Serving existing distribution networks. In the absence of reliable delivery credentials, the system automatically activates a verification chain that includes rules such as geofencing and frequency limits, and matches it with standard incentives. This path features a lightweight technical architecture designed to transform and empower existing channels at minimal cost.

[0089] The "Empty Bag as Token" data generation pipeline: This invention creatively establishes the "empty packaging state," a necessary physical outcome of completed consumption, as the sole physical token for digital verification. Through visual recognition technology, this is transformed into a data gate, ensuring that each data record corresponds to a genuine completion event, thereby generating high-value strategic data assets similar to an "empty bag index."

[0090] A multi-layered, closed-loop anti-cheating system:

[0091] Physical layer: Forging a large number of physical empty bags with different characteristics is extremely costly.

[0092] At the technical level: "AI visual recognition" and "manual review of key suspicious cases" form a closed loop for judgment.

[0093] Rule layer: The above dual-path design, combined with image anti-counterfeiting verification, geofencing, blockchain state locks, etc., systematically eliminates channel fraud and data abuse from the perspective of operational rules.

[0094] Example 2: Validating the Launch and Pilot Phase Model (Centralized MVP Architecture)

[0095] This embodiment corresponds to Figure 1 and Figure 2 As shown, this approach is suitable for the initial validation phase of business models, technological reliability, and user acceptance. Its core objective is to validate the core closed loop at the lowest cost and fastest speed; therefore, it adopts a lightweight, centralized technical architecture and a strict risk control strategy.

[0096] System configuration features:

[0097] The core verification is centralized: the validity and unconsumed status of the identifier code are determined by querying a centralized database deployed in the cloud.

[0098] Standardized verification process: Regardless of delivery route, all verification requests undergo a unified and rigorous verification process, namely: image source verification (S2), vacancy status identification using a high-standard confidence threshold (e.g., ≥90%) (S4), and verification of the full set of management rules, including geofencing (S5). Requests with a confidence level at the pending decision threshold (e.g., 85%~90%) will be transferred to manual review.

[0099] Risk control circuit breaker: The system sets a strict upper limit on the total amount of incentives to be distributed (e.g., RMB 5 million). When the accumulated incentives approach or reach this upper limit, the system can trigger a business suspension or issue a mandatory upgrade warning to ensure that the pilot risks are absolutely controllable.

[0100] Objective: To validate, in a low-cost and high-efficiency manner, the user acceptance of the core business loop of "scan code - photograph empty bag - receive incentive", the basic accuracy of the core algorithm (deformation ratio and AI recognition) and the system stability in the form of a minimum viable product (MVP).

[0101] Example 3: Scalable Deployment and Highly Reliable Operation Model (Decentralized Enhanced Architecture)

[0102] This embodiment corresponds to Figure 3 and Figure 4 As shown, this is applicable to scenarios where large-scale commercial promotion is to be carried out after successful verification in Example 2, or where there are legal-level credibility requirements for the data. Its core objective is to build a trustworthy, efficient, and scalable industrial data infrastructure.

[0103] System configuration features:

[0104] Decentralized core verification: It mandates the use of global state consensus verification based on consortium blockchain to ensure the immutability of the data source and global credibility.

[0105] Adaptive verification path: Fully enables a dual-path decision model based on trusted delivery credentials. The system provides differentiated verification experiences and risk control strategies based on differences in the transparency of circulating data.

[0106] Path 1 (High Reliability): For orders with "reliable delivery credentials" (such as factory direct), adopt an automated vacancy identification with a lenient threshold (such as ≥80%) to pursue ultimate efficiency and user experience, and support high-value incentives.

[0107] Path Two (Standard): For orders from traditional channels, adopt strict thresholds (e.g., ≥90%) and combine them with manual review to ensure the reliability of data in complex environments.

[0108] Refined operational risk control: The fixed upper limit on total incentive amounts has been eliminated, replaced by dynamic risk control strategies based on data analysis (such as incentive budget allocation based on region and user tags). Management rules for anti-counterfeiting and geofencing can be intelligently configured based on data paths.

[0109] Objective: To build a trustworthy data pipeline with both legal and technological credibility, enabling sustainable large-scale commercial operations. The generated "verified empty-bag consumption" data assets, due to their high credibility, can be directly applied to serious business scenarios such as precision marketing, supply chain optimization, credit risk control, and insurance actuarial science.

[0110] Example 4

[0111] Embodiment 4 of the present invention provides a system corresponding to Embodiment 1 above, including a memory, a processor, and a computer program stored in the memory; the processor executes the computer program in the memory to implement the steps of the method in Embodiment 1 above.

[0112] In some implementations, the memory may be high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device.

[0113] In other implementations, the processor can be any type of general-purpose processor, such as a central processing unit (CPU) or a digital signal processor (DSP), and there is no limitation here.

[0114] Example 5

[0115] Embodiment 5 of the present invention provides a computer-readable storage medium corresponding to Embodiment 1 above, on which a computer program / instructions are stored. When the computer program / instructions are executed by a processor, they implement the steps of the method of Embodiment 1 above.

[0116] A computer-readable storage medium can be a tangible device that holds and stores instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0117] Example 6

[0118] Embodiment 6 of the present invention provides a computer program product, including a computer program; when the computer program is executed by a processor, it implements the steps of the method described in Embodiment 1 above.

[0119] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0120] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0121] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0122] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0123] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A terminal consumption behavior verification method based on idle state recognition and adaptive verification, characterized in that, Includes the following steps: Obtain the verification request submitted by the end user; the verification request includes a packaging image and a unique packaging identifier. The packaging image is used to determine the state of the packaging; the state of the packaging includes an empty state where the contents are exhausted and a non-empty state where the contents are not exhausted. If the packaging is in an empty state, a terminal consumer behavior data record is generated in response to the unique identifier of the packaging.

2. The method according to claim 1, characterized in that, The process of obtaining the packaging image in the verification request submitted by the end user includes: The packaging image is obtained by calling the native camera interface of the terminal device to take real-time pictures, prohibiting access to the device's local album to select existing images, and applying anti-tampering tags to the captured images.

3. The method according to claim 1 or 2, characterized in that, After obtaining the packaging image, the packaging image is parsed to extract the identification code information presented in the packaging image; if the extracted identification code information is inconsistent with the unique packaging identification code in the verification request, the source of the packaging image is determined to be untrustworthy, the process ends, and an exception log is recorded.

4. The terminal consumption behavior verification method based on idle state identification and adaptive verification according to claim 1, characterized in that, Also includes: The terminal reward distribution signal is triggered based on the data records.

5. The terminal consumption behavior verification method based on idle state identification and adaptive verification according to claim 4, characterized in that, Before triggering the terminal reward distribution signal based on the data records, the following steps are also included: Determine whether the packaging is in a high-risk area. If so, the process ends; otherwise, generate a terminal consumer behavior data record in response to the unique identifier of the packaging.

6. The terminal consumption behavior verification method based on idle state recognition and adaptive verification according to claim 1, characterized in that, The specific implementation process of determining the state of packaging using the packaging image includes: 1) Determine whether the unique identification code on the packaging is valid and not marked as consumed; if so, obtain and verify the source credibility of the packaging image containing the identification code; if the source is credible, determine the confidence level of empty bag status identification based on the image, i.e., proceed to step 2); 2) Determine whether the confidence level for identifying the empty bag status is not less than the first set threshold. If so, determine that the status of the packaging is an empty state with the contents exhausted. Otherwise, determine whether the confidence level for identifying the empty bag status is greater than the second set threshold. If so, verify the status of the packaging. If the verification result is that the contents are exhausted, determine that the status of the packaging is an empty state with the contents exhausted. Wherein, the first set threshold is greater than the second set threshold.

7. The terminal consumption behavior verification method based on idle state recognition and adaptive verification according to claim 6, characterized in that, If the confidence level of the empty bag status identification is not greater than the second set threshold, then the process ends.

8. The method according to claim 1, characterized in that, The method of determining the state of packaging using the packaging image further includes: extracting the visual size features of the unique packaging identifier and the visual size features of the packaging bag from the packaging image, and calculating the ratio of the visual size features of the unique packaging identifier to the visual size features of the packaging bag; comparing the ratio with a set threshold, and if the ratio is not less than the set threshold, determining that the state of the packaging is an empty state with the contents exhausted; wherein, the set threshold is a preset threshold or a dynamic threshold determined by a trusted delivery certificate.

9. The terminal consumption behavior verification method based on idle state recognition and adaptive verification according to claim 1, characterized in that, After obtaining the verification request submitted by the end user, and before determining the status of the packaging using the packaging image, the process further includes: Determine if the total incentive amount is less than a set threshold. If so, use the packaging image to determine the packaging status; otherwise, end the process.

10. The method according to claim 1, characterized in that, Before determining the packaging status using the packaging image, the method further includes: performing a blockchain-based global trusted status verification on the unique packaging identifier; and determining the vacancy status based on the existence of a trusted delivery certificate associated with the identifier—if it exists, a first confidence threshold is used to determine the vacancy status; if it does not exist, a second confidence threshold higher than the first confidence threshold is used to determine the vacancy status; wherein, when the second confidence threshold is used: If the confidence level for identifying an empty state is not lower than the second confidence threshold, the package is determined to be in an empty state; if the confidence level is lower than the second confidence threshold but higher than the third confidence threshold, the process proceeds to the review stage; if the confidence level is not higher than the third confidence threshold, the verification is determined to fail. The second confidence threshold is higher than the third confidence threshold, and the third confidence threshold is higher than the first confidence threshold.

11. The method according to any one of claims 1 to 10, characterized in that, The packaging is a soft woven bag for feed, fertilizer, or cement.

12. A terminal consumer behavior verification system based on idle state recognition and adaptive verification, comprising a memory, a processor, and a computer program stored in the memory; characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 11.

13. A terminal device, comprising a memory, a processor, and a computer program stored in the memory; characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 11.

14. A computer-readable storage medium having a computer program / instructions stored thereon; characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 11.

15. A computer program product, comprising a computer program; characterized in that, When the computer program is executed by a processor, it implements the steps of the method described in any one of claims 1 to 11.

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