A product authentication information acquisition method

Abstract

The present application relates to a product authentication information acquisition method, which associates the position information of a product placement position in a product on-shelf state with a product placed in the product placement position, and acquires the authentication information of the product through the position information of the product placement position. Furthermore, the present application is free from the single dependence of the anti-counterfeit label or the authentication label on the proof of the authenticity or the authentication of the product (i.e. only relying on the anti-counterfeit label or the authentication label to prove the authenticity or the authentication of the product), and the authentication information corresponding to each product is inseparable from the product and unique. The authentication information of each product covers each link and the links between the links from the planting or production to the on-shelf sales of the product, and has continuity. The present application realizes the unique verification of the product, and the authentication information corresponding to each product does not have the possibility to prove other products.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically, to a method for obtaining product certification information. Background Technology

[0002] To verify the authenticity of products or various certifications, anti-counterfeiting labels and certification labels are typically used. However, because the labels and products are not inextricably linked—counterfeit products and anti-counterfeiting labels, and products that do not meet certification requirements and certification labels are manufactured separately—if the anti-counterfeiting label or certification label is genuine, regardless of whether the product is genuine or meets certification requirements, identification based solely on the anti-counterfeiting label or certification label will inevitably result in the product being considered genuine and compliant. This deficiency manifests in reality as unscrupulous merchants affixing anti-counterfeiting labels to counterfeit and substandard products, selling them as genuine, and then selling products without the anti-counterfeiting labels as "genuine" products, profiting from the price difference between genuine and counterfeit goods. Consequently, this leads to public distrust of various anti-counterfeiting and certification labels and increases the cost of verifying product authenticity. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for obtaining product certification information, which inextricably links the product with the continuous certification information from planting or production to sale, and identifies and obtains the information through location information to ensure the authenticity of the certification information.

[0004] The technical solution of the present invention is as follows:

[0005] A method for obtaining product certification information involves acquiring certification information for a product at each stage from planting or production to shelf sale, as well as across these stages. When the product is on the shelf for sale, a product placement location is associated with the product, and the product placement location is configured with location information, which is associated with the product's certification information. When the location information of the product placement location is obtained through a data device, the certification information associated with the location information of the product placement location is acquired.

[0006] Preferably, the location information of the product is obtained at each stage from planting or production to shelf sale, and across the stages; when the product is on the shelf for sale, the location information is used to associate the product with the product placement location; the data device includes an identification device, which identifies the product, obtains the current location information of the product, and then obtains the location information of the product placement location.

[0007] As a preferred method, image information is acquired at each stage of the product's journey from planting or production to its sale, as well as across these stages. When the product is on the market, the recognition device uses image recognition to identify the current product and related operations performed on it based on image features and angles, thereby obtaining the product's current location information.

[0008] Preferably, when the identification device performs product identification, it displays a virtual label in the identification image or video; the virtual label corresponds one-to-one with the position information of the product placement position, so the identification device identifies the virtual label and obtains the position information of the product placement position corresponding to the product.

[0009] Preferably, the location information of the product is obtained at each stage from planting or production to sale and across the stages; when the product is on the shelf for sale, a label is set for the product placement position; the data device includes a reading device that reads the label to obtain the location information of the corresponding product placement position.

[0010] Preferably, the identification label corresponds one-to-one with the location information of the product placement position; the reading device reads the identification label to obtain the location information to be verified, first regards the location information to be verified as the location information of the product placement position, obtains the certification information corresponding to the location information, and verifies whether the location information of the product placement position associated with the product to which the certification information belongs is consistent with the location information to be verified. If it is consistent, the certification information of a product associated with the product placement position corresponding to the current location information is obtained; if not, the current identification label is invalid.

[0011] As a preferred option, if the product includes both contents and outer packaging, then certification information is obtained for each stage of the process from planting or production to packaging and sales, as well as between these stages.

[0012] Preferably, the deployment includes image acquisition devices that cover all stages of the product's journey from planting or production to sale and across different stages, as well as various data acquisition devices corresponding to the various data required for certification information, in addition to the image acquisition devices. The image acquisition devices acquire monitoring videos of the product's journey from planting or production to sale and across different stages. In adjacent stages, the image acquisition devices used to monitor the preceding stage, the stages crossing between stages, and the following stage have overlapping monitoring ranges. Correspondingly, the monitoring videos of the preceding stage, the stages crossing between stages, and the following stage have overlapping video frames, forming a continuous video and completing the uniqueness verification of the product.

[0013] As a preferred option, the basic data collected by the data acquisition device, including the image acquisition device, is encrypted and stored according to the timestamp, and the decryption key is stored in various third-party regulatory agencies with authentication rights.

[0014] As a preferred method, for various products, based on the certification standards and claimed characteristics corresponding to the preset certification type, the basic data is verified. If the verification passes, the corresponding certification information and claimed characteristics are determined; otherwise, the corresponding certification information and claimed characteristics cannot be determined or are cancelled. If the verification cannot be completed, or the amount of basic data is less than the preset quantity standard, the encrypted basic data is sent to a third-party regulatory agency with certification authority. The third-party regulatory agency decrypts and verifies the basic data to determine the verification result.

[0015] As a preferred method, the system automatically searches product sales pages and verifies the certification type and claimed features of the labels by verifying the basic data. If the verification cannot be completed, the system will report the issue to a third-party regulatory agency with certification authority.

[0016] As a preferred method, the authentication and verification method is as follows:

[0017] Real-time authentication and verification of basic data;

[0018] Alternatively, the basic data can be authenticated and verified at a preset duration or period.

[0019] Alternatively, the basic data can be authenticated and verified in real time or at a preset duration or period to obtain the current verification result; when the next piece of basic data differs from the previous piece of basic data or the difference value is greater than a preset difference threshold, the next piece of basic data can be authenticated and verified; and so on.

[0020] As a preferred method, when obtaining authentication information, an access link to the basic data is also obtained, and the basic data is then accessed by decrypting it using a decryption key held by a third-party regulatory agency.

[0021] As a preferred option, audible and visual alarm devices are installed for each stage of the product's journey from planting or production to sale and across different stages. When the monitoring video captured by the image acquisition device is found to contain obstructions to the product, the corresponding audible and visual alarm device will sound an alarm. If the obstruction of the product exceeds a preset time, the monitoring video is deemed discontinuous, and the product's uniqueness verification fails.

[0022] As a preferred method, video surveillance can be used to monitor and identify the behavior of staff during the planting or production of the product; corresponding data acquisition devices can be used to detect and collect data on the gases, liquids, soil, and raw materials that the product comes into contact with during the planting or production process.

[0023] As a preferred option, basic data or authentication information is stored through blockchain or peer-to-peer computing systems, and the compliance of the basic data or authentication information is confirmed by various third-party regulatory agencies with authentication rights.

[0024] As a preferred option, for a user-specified product, a unique identifier is set, and image data of the product is acquired through an image acquisition device at each stage from planting or production to sale, as well as across the stages; in the image data, the product and the unique identifier coexist in the same frame.

[0025] This invention utilizes a peer-to-peer computing system to perform non-specific feature recognition and location recognition of targets, including products, raw materials, additives, workers, production tools, and automated production equipment.

[0026] The peer-to-peer computing system includes multiple node devices, and there is no hierarchy among the node devices. Each node device is equipped with a data acquisition device and a computing module. The data acquisition device includes at least one type of sensor, including an image acquisition device, for collecting different types of sensing data. Node devices located at different acquisition positions collect at least one point sample of the target, and the point sample is sensing data of the corresponding sensor type.

[0027] For a given node device, the collected sensing data is processed to obtain result data, which is then propagated to other node devices. Other node devices receiving the result data use it as one of the original collected data, influencing the result data of other node devices. Based on this, without needing to obtain the target's identity information, multiple node devices in the peer-to-peer computing system perform collaborative computation to determine each unique target as itself, achieving non-specific feature recognition and target location identification, including product positioning information.

[0028] As a preferred method, the component parameters of each control component of the automated production equipment are obtained; each control component of the automated production equipment, or a combination of multiple control components that form the same function, is added to the peer-to-peer computing system through one or more node devices; if, based on collaborative computing, it is determined that a certain control component or a combination of multiple control components of the automated production equipment needs to be automatically controlled based on the corresponding control requirements, the current node device will send control commands to the control components connected to the current node device according to the calculated result data, and control the control components to complete the control actions.

[0029] As a preferred approach, the control components of automated production equipment are incorporated into the peer-to-peer computing system as node devices. If an anomaly is determined to exist in the control components based on collaborative computing, the anomaly data is used as one of the inputs to participate in the calculation of the result data, and a processing solution is obtained through collaborative computing.

[0030] The beneficial effects of this invention are as follows:

[0031] The product certification information acquisition method of this invention associates the location information of the product placement position with the product placed there during the product's sale on a shelf. It then obtains the product's certification information through this location information. This eliminates the reliance on anti-counterfeiting or certification labels as the sole means of proving a product's authenticity or certification (i.e., relying solely on anti-counterfeiting or certification labels to prove the product's authenticity or certification). Furthermore, the certification information for each product is indivisible and unique, covering all stages from planting or production to sale and spanning between stages. This consistent certification information ensures the uniqueness of each product and prevents the certification information from being used to prove the authenticity of other products.

[0032] The virtual label or identification tag set in this invention is unrelated to the product and only related to the space. That is, the virtual label or identification tag is used to obtain the location information of the product placement location, and then obtain the corresponding product's certification information through the location information of the product placement location. Furthermore, by restricting the correspondence between the virtual label or identification tag and the space, the validity of the virtual label or identification tag can be guaranteed, and the virtual label or identification tag can be prevented from being used to obtain the certification information of other products.

[0033] This invention utilizes a peer-to-peer computing system for collaborative computation, performing non-specific feature identification and location identification on targets (including products, raw materials, additives, workers, production tools, automated production equipment, etc.) to achieve identity recognition and positioning. In this peer-to-peer computing system, there is no hierarchy among the node devices, and no fixed connection paths exist between them. Each node device only receives the computation results from other node devices and sends out its own computational results. The detection of events and / or the corresponding execution device (in this invention, the execution device mainly includes automated production equipment) does not rely on a single node device for identification and control, but rather on the collaborative computation and joint confirmation of multiple node devices within the peer-to-peer computing system. Without requiring specific features or specific identity information, each unique target can be identified as itself, achieving non-specific feature identification. This invention uses non-specific feature identification for target identification, identity verification, or event monitoring, resulting in high accuracy and precise location identification. This invention can identify targets and verify identities without relying on specific features, protecting privacy while simultaneously addressing issues related to transportation, education, medical convenience, epidemic prevention, public services, market behavior, safe production, and civilized behavior.

[0034] This invention employs non-specific feature recognition, effectively preventing risks caused by theft or counterfeiting of specific features, thus significantly enhancing security. It utilizes a non-contact, passive method for seamless target identification, greatly improving ease of execution. Based on the aforementioned peer-to-peer computing system, this invention can be easily deployed across coverage areas ranging from hundreds of meters to hundreds of kilometers, making it suitable for various geographical scales.

[0035] In this invention, the execution device responds to the computation results obtained through collaborative computing, resulting in high response efficiency and avoiding illegal responses such as false execution or failure to execute when required due to network attacks. To prevent hijacking, this invention can also use multiple node devices to collaboratively control the execution device, further enhancing its immunity to hijacking attacks. Detailed Implementation

[0036] The present invention will be further described in detail below with reference to the embodiments.

[0037] To address the insufficient evidentiary value of existing anti-counterfeiting labels, this invention provides a method for obtaining product certification information. This method avoids using separate anti-counterfeiting and certification labels to verify the authenticity of products, preventing the misuse of these labels and avoiding fraudulent certifications. This invention collects and stores certification information across all stages of a product's journey from planting or production to sale. When the product is on the market, the method allows viewing its certification information by understanding the spatial relationship between the product and its placement. It also allows further viewing of basic certification data, certification results from authorized third-party regulatory agencies, and verification results. In this invention, certification information is inextricably linked to the product, thus eliminating the need for traditional anti-counterfeiting and certification labels.

[0038] The product certification information acquisition method of this invention aims to ensure the continuity of certification information and enhance its evidentiary value by collecting product certification information throughout the entire product lifecycle. This invention acquires certification information across all stages of the product's journey from planting or production to shelf sale, including those spanning between stages. These "stages from planting or production to shelf sale" primarily include, but are not limited to, planting or production, processing, warehousing, logistics, and sales. In specific implementation, stages can be set according to implementation needs, such as from scratch, from raw materials to finished products, and from production to sales. Correspondingly, "spanning between stages" refers to the process of transferring information between adjacent stages. The acquired certification information is used during the product shelf sale stage, retrieved upon request or command. In this invention, when the product is on the shelf for sale, the product placement location is associated with the product, and the product placement location is configured with location information. This location information is associated with the product's certification information. When the location information of the product placement location is obtained through a data device, the certification information associated with that location information is acquired. In practical implementation, one or more identical products can be placed in a single product placement slot. Correspondingly, the certification information should be related to the one or more identical products and meet their certification requirements. This invention obtains the certification information associated with the product placement slot's location information; this is the corresponding product's certification information. If a product is placed in another product placement slot, the product and placement slot are re-associated, the association between the product and the original placement slot is broken, and the original placement slot's location information is obtained. Therefore, the product's certification information cannot be obtained. Furthermore, if the product in the original placement slot is removed and other products (including counterfeit products) are placed in it, the original product's certification information cannot be obtained, meaning the original product's certification information cannot be misused.

[0039] As a method for associating product placement location information with product certification information, specifically, it acquires the product's location information across various stages from planting or production to shelf sale, and between these stages. When the product is on the shelf for sale, the location information is used to associate the product with its placement location; that is, the location information of the product placement location is associated with the product through the product's location information, and subsequently, with the product's certification information. In this embodiment, the data device includes an identification device (which can be a handheld device with identification capabilities). After identifying the product, the identification device acquires the product's current location information, then acquires the location information of the product placement location, and finally obtains the associated certification information. Specifically, the identification device acquires image information across various stages from planting or production to shelf sale, and between these stages. When the product is on the shelf for sale, the identification device, based on image recognition, identifies the current product and the associated operations performed on it through image features and angles, thereby acquiring the product's current location information. The identification of the associated operations performed on the current product mainly includes identifying the actions performed on the current product, such as execution actions and movement actions, to assist in associating the product with its placement location. For example, monitoring, identifying, and documenting the action of moving a product from one placement location to another ensures the accuracy of the association between product placement and product, even when the product cannot be distinguished from image features. This is particularly important for products with identical appearances but different certification levels. For instance, products a, b, and c may look identical but have different certification levels. If product placement is adjusted between products a, b, and c, confusion may arise if only image features are used for identification. However, by identifying associated operations, the interchange of product placements between products a, b, and c can be recognized, thus maintaining the accurate association between the product and certification information.

[0040] Furthermore, virtual tags can be used to assist in obtaining the location information of the product placement position. Specifically, when the recognition device performs product recognition, virtual tags are displayed in the recognition image or recognition video (image data obtained by the recognition device, i.e., image data used to recognize the product). The virtual tags correspond one-to-one with the location information of the product placement position, so the recognition device recognizes the virtual tags and obtains the location information of the product placement position corresponding to the product.

[0041] As another method for associating product placement location information with product certification information, specifically, it involves acquiring the product's location information across various stages from planting or production to shelf sale, and between these stages. When the product is on the shelf, a corresponding identification label is set for the product placement location. Since the product placement location information is associated with the product's location information, the product's certification information can be obtained by reading the identification label and acquiring the product placement location information. In this embodiment, the data device includes a reading device (which can be a handheld device with reading capabilities), which reads the identification label to obtain the corresponding product placement location information.

[0042] To prevent the labels from being moved or misused (i.e., the labels not matching the corresponding product placement locations), this embodiment establishes a one-to-one correspondence between the label and the product placement location information. If the location information obtained from reading the label does not match the location information of the product placement location, the label is deemed invalid. Specifically, the reading device reads the label to obtain the location information to be verified, treats this location information as the location information of the product placement location, obtains the authentication information corresponding to the location information, and verifies whether the location information of the product placement location associated with the authentication information matches the location information to be verified. If they match, the authentication information of a product associated with the current location location is obtained; otherwise, the current label is invalid. In practice, the verification process can be implemented as invisible, meaning the authentication information is not displayed until the verification is successful.

[0043] The two methods described above for associating the product placement location information with the product's certification information can be implemented individually or simultaneously, with cross-verification performed. For example, it can be verified whether the location information obtained by identifying the virtual identifier corresponds to the product placement location to prevent misuse caused by image recognition deception.

[0044] For products that must be packaged, such as tea and rice, this invention also acquires certification information for the packaging and / or the product itself. Specifically, if the product includes both contents and outer packaging, it acquires certification information for the contents at each stage from planting or production to packaging and sale, as well as across these stages. The contents can be products obtained through planting, production, or processing, such as tea or rice from planting to harvest, or they can be finished products that are directly packaged, such as purchased food products packaged and sold as contents.

[0045] When a user requests a specific product for personalization, this invention provides information about the product throughout its entire lifecycle and ensures the user can confirm its exclusivity and uniqueness. For example, the user can write a unique identifier or have the manufacturer provide one, which is placed near the specified crop or attached to the crop in a way that does not affect its growth. An image acquisition device captures image data of the product at each stage from planting or production to sale, and across these stages. In this image data, the product and the unique identifier coexist in the same frame. This simultaneous presence of the product and unique identifier in the same frame ensures a clear correspondence between them in the image data, facilitating user identification from the monitoring video and ensuring that the user confirms that the product is indeed the one they requested.

[0046] To provide more compelling basic data and ensure that third-party regulatory agencies with certification authority can accurately monitor and judge the basic data and certification information, this invention provides coherent basic data. This ensures greater probative value than fragmentary evidence, especially in cases where feature comparison is difficult. Coherent basic data can more strongly presume the validity and probative value of certification information. Specifically, the system deploys image acquisition devices that monitor all stages of the product's journey from planting or production to sale and across these stages, as well as various data acquisition devices corresponding to the different types of data required for certification information, excluding image acquisition devices. The image acquisition devices acquire monitoring videos of the product's journey from planting or production to sale and across these stages. The various data acquisition devices are used to collect corresponding types of sensory data. In this embodiment, corresponding types of data acquisition devices, such as gas composition sensors, water quality sensors, and soil composition sensors, detect and collect real-time data on the gases, liquids, soil, and raw materials that the product comes into contact with during the planting or production process. In this process, adjacent stages utilize image acquisition devices that monitor the preceding stage, the stages crossing between stages, and the subsequent stages. These devices have overlapping monitoring ranges, resulting in overlapping video feeds from the preceding stage, the stages crossing between stages, and the subsequent stages. This creates a continuous video stream, verifying the uniqueness of the product. Through this continuous video stream, even if the product's behavior or state during production cannot be determined from the video feed at certain moments, the uniqueness of each product can be established based on the continuous video, thus verifying its uniqueness. For example, if a product cannot be identified at a certain moment, but is re-identified in the next moment of the continuous video stream, then the product can be considered the same product even when identification was initially impossible. The same principle applies to various certifications during the production process. In this embodiment, multi-angle cameras can cover each product's journey from planting or production to shelf sales, as well as the process between these stages, ensuring that there are no gaps in monitoring. For example, in the logistics process, multiple infrared cameras need to be installed in the transport vehicle to ensure that monitoring videos that meet certification requirements are obtained in low-light scenarios, thus ensuring accurate and effective product monitoring.

[0047] To remind staff to prevent product obstruction and avoid uniqueness verification failure, this invention includes audible and visual alarm devices at each stage of the product's journey from planting or production to sale, and across different stages. When the monitoring video captured by the image acquisition device detects product obstruction, the corresponding alarm device sounds. If product obstruction persists for a preset duration, the monitoring video is considered discontinuous, and product uniqueness verification fails. Furthermore, this invention monitors and identifies staff behavior during the planting or production stages, ensuring compliance with regulations, including safe production and product certification requirements. It provides real-time identification of the production process, raw materials, additives, processing procedures, and operational steps, ensuring the compliance of staff actions, processing steps, raw materials, and equipment used.

[0048] To ensure data security, in this invention, the basic data collected by the data acquisition device, including the image acquisition device, is encrypted and stored according to a timestamp. The decryption key is stored by various third-party regulatory agencies with authentication rights. These third-party regulatory agencies can use the decryption key to decrypt the encrypted basic data and perform subsequent verification, authentication, and monitoring. To allow users to view the corresponding basic data while viewing product certification information, this invention also obtains an access link to the basic data when acquiring certification information, and decrypts it using the decryption key held by the third-party regulatory agency to access the basic data.

[0049] In this invention, for various products, automatic verification can be performed based on basic data according to the certification standards and claimed characteristics corresponding to the preset certification types. Specifically, for the preset certification types and claimed characteristics of a product, the basic data is verified. If the verification passes, the corresponding certification information and claimed characteristics are determined; otherwise, the corresponding certification information and claimed characteristics cannot be determined or are cancelled. If verification cannot be completed, or the amount of basic data is less than the preset standard, the encrypted basic data is sent to a third-party regulatory agency with certification authority. The third-party regulatory agency decrypts and verifies the basic data to determine the verification result. For example, in online sales, the system automatically searches product sales pages, verifies the identified certification types and claimed characteristics by verifying the basic data, and determines the verification result; if verification cannot be completed, the result is fed back to a third-party regulatory agency with certification authority.

[0050] In this embodiment, the authentication verification method is as follows:

[0051] Method 1: Real-time authentication and verification of basic data;

[0052] Method 2: Verify and authenticate the basic data at a preset duration or period;

[0053] Method 3: Verify the basic data in real time or at a preset duration or period to obtain the current verification result; when the next piece of basic data differs from the previous piece of basic data or the difference value is greater than the preset difference threshold, verify the next piece of basic data; and so on.

[0054] In practice, real-time and / or statistical comparisons can be conducted based on industry management regulations, national and local standards, and the characteristics claimed by the company for each type of product. If a suspected violation of the aforementioned industry management regulations, national and local standards, or claimed characteristics is found, or if verification cannot be completed (e.g., the surveillance video obscures the product, or the number of damaged data acquisition devices is insufficient to complete the verification of a certain product), the encrypted basic data will be sent to a third-party regulatory agency with certification authority. After verifying the authenticity of the data by decrypting the key, the third-party regulatory agency will conduct an investigation and collect evidence. If it is found that the relevant clauses have indeed been violated, the relevant certifications of the products involved in the behavior, such as certification certificates, will be revoked.

[0055] To ensure data security, this invention stores basic data or authentication information using a blockchain or peer-to-peer computing system. The compliance of this basic data or authentication information is verified by various third-party regulatory agencies with authentication rights. In practice, third-party regulatory agencies can conduct legally recognized evidence collection through blockchain or peer-to-peer computing systems. The obtained evidence is stored by the third-party regulatory agencies with authentication rights, whose credibility confirms its compliance.

[0056] In practical implementation, traditional single-point identification methods can be used to identify and locate the identity and location information of products at various stages from planting / production to sales, as well as between stages, to achieve the purpose of identity confirmation and location information association. Alternatively, the peer-to-peer computing system provided by this invention can be used for non-specific feature-based identity recognition. The peer-to-peer computing system of this invention is based on collaborative computing, does not rely on single-point identification, and distributes computing functions across the entire network, reducing the hardware and software requirements of single-point computing, resulting in high execution efficiency and greatly improving anti-attack capabilities. The system maintains a relatively symmetrical information state among node devices, which can prevent illegal data tampering. Even if a single node device is physically hacked and its transmitted data is tampered with, the tampering of data transmitted by a single node device does not affect the overall network computing results because the network-wide computing is a highly redundant and complex calculation with multiple dimensions of verification. Moreover, it can quickly locate the faulty and tampered node device, ensuring the credibility of the network-wide computing results. Therefore, it can resolve the contradiction between data sharing and information security between departments.

[0057] The result data transmitted between node devices can be the processing result of information rather than the information itself. Therefore, the raw data collected (i.e., perceived data) does not need to be stored. Node devices only receive the calculation results output by other node devices and send out their own calculation results. The amount of information contained in a single calculation result is insufficient to reconstruct any event or target information. A definite result can only be obtained by joint calculation of the calculation results of the entire peer-to-peer computing system, multi-dimensional data matrix elements, and physical space and facility correspondence. The collaborative calculation has less dependence on the information transmitted by a few node devices, thus fundamentally changing the nature of traditional information technology's single-point security sensitivity.

[0058] In this invention, the acquisition of the target's identity information and location information can be achieved through collaborative computation using the peer-to-peer computing system provided by this invention. Specifically, this invention utilizes the peer-to-peer computing system to perform non-specific feature recognition and location recognition of targets, including products, raw materials, additives, workers, production tools, and automated production equipment. The term "non-specific feature recognition" differs from the common understanding of "recognition" in a strict conceptual definition. Commonly, "recognition" refers to identifying the concrete form or specific identity information of a target, such as who it is (including name, specific information indicating the target's identity), or what it is (e.g., a car, a person). However, the "recognition" in this invention refers to identifying each unique target as itself; that is, for a given object to be identified, its existence is unique. After achieving "non-specific feature recognition," this invention determines that the object to be identified (i.e., the target that has not been identified or confirmed) is itself, and not other objects to be identified. The result of "non-specific feature recognition" does not require determining the specific characteristics of the object to be identified, nor does it require determining the object's identity information or concrete form. For example, if a person is considered object A to be verified, and an object is considered object B to be verified, then after implementing "non-specific feature recognition," it is not necessary to identify whether object A is a person or what their specific identity is, nor is it necessary to identify whether object B is an object or what kind of object it is; rather, it is necessary to determine that object A is object A itself, and object B is object B itself. Then, corresponding services or controls can be provided for object A or object B.

[0059] The peer-to-peer computing system comprises multiple node devices, all without a hierarchy, forming a decentralized network and computing architecture. Unlike traditional single-point aggregation computing models, the data transmission direction between node devices in this invention does not have a fixed, predetermined path relationship. In the peer-to-peer computing system described in this invention, a particular node device processes the collected raw data to obtain result data, and then propagates the result data to other node devices. Other node devices that receive the result data use it as one of their collected raw data sets, thus influencing the result data of other node devices. For ease of description, the aforementioned "particular node device" is referred to as the "current node device," and the "other node devices" are referred to as "subsequent node devices." One aspect of this influence is that the result data obtained by subsequent node devices is not entirely determined by their own collected raw data, but rather by the result data output by the current node device. Specifically, the result data output by the current node device may alter the data processing model and parameters used by subsequent node devices to calculate the result data, thereby affecting the result data of subsequent node devices. For example, if the output data of the current node device is correlated with the raw data collected by subsequent node devices, it is necessary to consider the impact of the output data of the current node device on the accuracy of the output data of the subsequent node devices. Specifically, for the perception of a specific target, if the result data is calculated based solely on the raw data collected by subsequent node devices, it can only reflect the real-time (including real-time location and time) single-point result judgment of the target within the perception range of the subsequent node devices. However, the output data of the current node device reflects the direct perception data and result judgment of the target at other locations and at other times, or other indirectly related perception data and result judgments, which helps to improve the accuracy and comprehensiveness of the result data of the subsequent node devices, including superimposed calculations of the same dimension and correlation references of different dimensions.

[0060] Because there is no master-slave relationship between nodes in a peer-to-peer computing system, point-to-point transmission is possible. Therefore, for a given calculation result corresponding to a specific perceived data point of a target, as reflected in the output data of one node, the information is relatively symmetrical among other nodes receiving that result data. Other nodes use the received result data as input, combining it with their own sensor data to calculate their own result data. Their own result data naturally encompasses both the received result data and the information reflected by their own sensors, and is transmitted to other nodes in the next layer. Thus, for a specific perceived data point of a target, information is relatively symmetrical across all nodes. This prevents the impact of tampering or falsification of the calculation process and results of a single node on the result data. It also serves as a means to detect faulty, tampered, or non-compliant node devices. This fundamentally solves the inherent hidden dangers of traditional information technology, namely, the false, falsified, and erroneous information caused by information asymmetry, which becomes a point of entry for fraud and cyberattacks. It also addresses the problems of poor accuracy, excessive time consumption, low credibility, and poor responsiveness in complex integrated applications. Therefore, it can truly become the information infrastructure for comprehensive management of large areas and the infrastructure for the digital economy. Unlike blockchain technology, which relies on independent computation by each node to determine the result and emphasizes the preservation of original data, this invention focuses on peer-to-peer collaborative computation among node devices. Through this collaborative computation, each node device can adjust its own data processing model (i.e., the algorithm for calculating the result data) and parameters when processing data. This adjustment is a feedback mechanism from all node devices, transforming the computation of all node devices into a unified whole. Instead of individual nodes performing calculations independently, all node devices collaboratively complete the computation. The adjustments to the node device's data processing model are objectively real and will impact subsequent data processing iterations.

[0061] Node devices are equipped with data acquisition devices (in specific implementations, these may include one or more of the following: image acquisition devices, gas composition sensors, water quality sensors, soil composition sensors, audio acquisition devices, temperature measurement devices, vibration frequency sensing devices, lidar, chemical sensors, and electromagnetic induction devices) and a computing module. The data acquisition devices include at least one type of sensor, including image acquisition devices, used to collect sensing data of different corresponding types. The computing module calculates the resulting data based on a data processing model. Node devices located at different acquisition positions (i.e., at different physical installation locations) collect at least one point sample of the target; the point sample is sensing data corresponding to the sensor type. Based on this, without needing to obtain the target's identity information, multiple node devices in the peer-to-peer computing system perform collaborative calculations to determine that each unique target is itself, achieving non-specific feature recognition; and, it achieves target location recognition, including product positioning information.

[0062] Specifically, taking a given node device as the current node device, and considering the data transmission between its preceding and subsequent node devices (in this invention, "preceding node device" and "subsequent node device" only describe their sequential relationship with the current node device in the current calculation and data transmission process, and do not imply any necessary sequential or priority relationship between them), the current node device receives the result data output by other node devices (including preceding node devices), and subsequent node devices receive the result data output by other node devices (including the current node device). For the current node device, the collected sensing data is combined with the result data from other node devices (including preceding node devices) to calculate the result data of the current node device, and this result data is sent to other node devices (including subsequent node devices). Similarly, the working process of subsequent node devices is the same as that of the current node device, and preceding node devices also receive the result data from the preceding node devices of their predecessors and perform the same working process as the current node device; that is, the node devices in the peer-to-peer computing system perform the same working process. Furthermore, the node devices in the peer-to-peer computing system perform collaborative calculations as sensing data is collected and result data is calculated. In this process, the output data of a certain node device is only received and used as input by the subsequent layer of node devices, and the output data of the subsequent layer of node devices will cover the output data of the preceding layer of node devices (including the aforementioned node device).

[0063] In a peer-to-peer computing system, all events are processed synchronously, and it is not necessarily necessary to explicitly produce staged results such as what event was discovered or what the specific content of the event is. In a peer-to-peer computing system, only the sensor's perception and the corresponding execution device (in this invention, mainly including automated production equipment) are explicitly detected. Other intermediate processes are processed simultaneously through collaborative computing. That is, during the operation of this invention, the intermediate process of event discovery is imperceptible. As collaborative computing proceeds and the node device obtains the result data, the corresponding execution device automatically responds and executes.

[0064] To further ensure the trustworthiness of the data source and computation process, in this invention, all node devices encrypt their computational results based on an encrypted consensus mechanism, obtaining encrypted results, which are then sent to other node devices. The encrypted consensus mechanism includes one or more consensus mechanisms, with different mechanisms corresponding to changes in the encryption algorithm structure and parameters of the node devices.

[0065] Node devices communicate using standard-sized data packets (i.e., result data or calculation results). In this invention, the node devices in the peer-to-peer computing system are similar to human neurons. Just as each neuron does not transmit specific data directly describing external events, the node devices do not output raw data. Instead, they process the raw data acquired by connected sensors and data acquisition devices into standard-sized data packets (i.e., result data or calculation results, similar to nerve impulses in neurons) based on their own data processing models (similar to the biological characteristics of nerve cells). The information contained in a single data packet is insufficient to reconstruct any event or target information. A definite result can only be obtained through collaborative computation involving the calculation results across the entire peer-to-peer computing system, multi-dimensional data matrix elements, and the correspondence between physical space and facilities. Collaborative computation has little dependence on the data output by a few node devices, and it simultaneously processes all requests received or initiated by all node devices. It is a collaborative verification computation of highly multi-dimensional related information, thus fundamentally changing the traditional single-point security sensitivity of information systems.

[0066] To ensure data integrity and the effective execution of collaborative computing, this invention deploys a QoS mechanism in the peer-to-peer computing system. The QoS mechanism prioritizes ensuring the transmission quality of result data between node devices.

[0067] In practical implementation, the peer-to-peer computing system can be networked using one or more combinations of 4G, 5G, or MESH modes to suit different application scenarios. The optimal solution is achieved by considering factors such as feasibility and cost. The MESH mode is based on the LTE standard, communicating at the LTE physical layer. Data is carried by a customized frame structure, and interaction is performed using a dedicated wireless communication protocol. Customizing the frame structure to suit peer-to-peer computing and employing a proprietary wireless communication protocol developed for urban cluster peer-to-peer computing further enhances its security and reliability. Furthermore, the wireless algorithm is fully adapted to the multipath channel environment controlled by a consensus mechanism required for peer-to-peer computing, achieving communication distances of 100 meters to 10 kilometers within cities and 120 kilometers in the field using omnidirectional antennas. In this embodiment, the Mesh network communication distance is 50-150 meters between indoor nodes and 50 meters to 120 kilometers between outdoor nodes, with each node capable of connecting to 65,535 nodes. In addition, when networking in 4G and 5G modes, there is no limit to the communication distance, and the number of node devices that can be connected depends on the computing power of the computing chip and the communication latency.

[0068] In a peer-to-peer computing system, for a specific point sample of an object to be identified, the result data transmitted from the node device that collected the point sample to other node devices allows subsequent node devices to adjust their perceptual attention based on the features of that point sample (it is not necessary for the result data to contain the features of that point sample, but rather that the features of that point sample participate in the computation of the preceding node device, so that the result data of the preceding node device can be used as input to the data processing model of the subsequent node device, allowing the subsequent node device's data processing model to achieve the effect of adjusting perceptual attention during computation); or, the features of that point sample can be reported for subsequent node devices to adjust their perceptual attention (the features of that point sample are directly described in the result data). If other subsequent node devices do not detect the features of that point sample, but can determine from the features of other point samples that the undetected features of that point sample still belong to the object to be identified, then the features of that undetected point sample are continued to be described in the result data of the current node device and transmitted to other node devices. For example, if a preceding node device senses the color of an object A to be identified, but the current node device does not sense the color of the object A to be identified, but it can be determined from the sensing data of other node devices that there is another object A to be identified besides other objects to be identified, then the color of the object A to be identified that has not been sensed will still be represented in the result data of the current node device.

[0069] In this embodiment, the method for reporting the features of the point sample for subsequent node devices to adjust the perceptual attention is as follows: adjusting the parameters of the data processing model of the subsequent node device based on the features of the point sample provided by the preceding node device, so that the subsequent node device can improve the computing power of the point sample to identify its features; or, the subsequent node device uses the perceptual attention model to match the features of the received point sample to adjust the computing power.

[0070] The “feature” mentioned above has a different meaning from the “feature recognition” in the prior art. The “feature recognition” in the prior art usually refers to information that can determine the identity of a target, while the “feature” in this invention represents a kind of perceived data belonging to the object to be identified, such as coordinates, colors belonging to the object to be identified, etc. The “non-specific feature recognition” of the object to be identified cannot be directly completed by the “feature” perceived by a single point.

[0071] In this embodiment, the method for reporting the features of the point sample for subsequent node devices to adjust the perceptual attention is as follows: based on the result data expressing the features of the point sample provided by the preceding node device (in this invention, the features of the point sample are usually not provided themselves, but expressed in the result data), or the features of the point sample (i.e. the features of the point sample itself), the parameters of the data processing model of the subsequent node device are adjusted so that the subsequent node device can improve the computing power of the point sample to identify its features; or, the subsequent node device uses the perceptual attention model to match the features of the received point sample or the result data expressing the features of the point sample to adjust the computing power.

[0072] When a node device processes the output data from several preceding node devices, based on the data processing model, if the objects to be identified described by several preceding node devices can be determined to be the same target through certain common point sample features, the point sample features and other information described by each node device are merged into the same target. For example, point sample features in physical space that almost completely overlap at the same time can be determined to be the same target.

[0073] When the result data received by a node device indicates that the flag used by the current node device to identify the object to be identified before the current reception of result data is different from the flags used by other node devices to identify the object to be identified, and the flags assigned to the object by other node devices have been updated, then the flag used by the current node device to identify the object to be identified before the current reception of result data is converted. Specifically, the method for converting the flag used by the current node device to identify the object to be identified before the current reception of result data is as follows:

[0074] The flag used by the current node device to identify the object to be identified before the current reception of result data is replaced with the latest flag assigned to the object by other node devices; this is a simpler implementation of the present invention.

[0075] Alternatively, the conversion relationship between the flag used by the current node device to identify the object to be identified before the current receiving result data and the updated flag assigned to the object by other node devices can be recorded, and the conversion can be performed when the current node device's current receiving result data needs to be referenced; this is a relatively complex implementation method provided by the present invention.

[0076] Alternatively, the node device can deploy a conversion model to perform corresponding conversions on the labels of multiple objects to be identified based on the input raw data or result data; this is a more complex implementation provided by the present invention.

[0077] In this invention, in order to improve the effectiveness of "non-specific feature recognition", for one or more point samples collected successively by node devices at different collection locations, if the feature values ​​of one or more point samples at different collection locations meet the preset similarity conditions or are determined by a specific model to have a correlation threshold, and are unique at each collection location, then it is determined that the point samples at different collection locations are correlated.

[0078] On the other hand, for one or more point samples collected simultaneously by node devices at different collection locations, if the node devices at different collection locations collect data on the same spatial field, and there is only one object to be identified in the spatial field, or the collected point sample can correctly point to one of the multiple objects to be identified, then for a certain object to be identified, one or more point samples collected by node devices at different collection locations are correlated.

[0079] In this invention, the data acquisition device of the node device includes one or more combinations of an image acquisition device, an electromagnetic induction device, a temperature measurement device, and a vibration frequency sensing device, and a lidar. The data acquired by the aforementioned devices (i.e., one or more combinations of the image acquisition device, electromagnetic induction device, temperature measurement device, and vibration frequency sensing device) and the three-dimensional point cloud acquired by the lidar, or the point cloud generated from images acquired by multiple image acquisition devices, are jointly calculated to obtain three-dimensional points with data. The image color, contour, lines, reflectivity, motion trend, electromagnetic characteristics, temperature, temperature change trend, vibration frequency, and vibration frequency change trend based on two-dimensional perception are used as additional attributes of the corresponding three-dimensional points to constitute an attributed three-dimensional point cloud. Combining electromagnetic induction, temperature patterns, vibration frequency change characteristics, motion correlation (different motion correlations exhibited by different materials such as ropes and fabrics), and reflectivity, the correspondence between each region of the attributed three-dimensional point cloud and each part or related part of the 3D appearance of the object to be identified is determined. This embodiment utilizes the attributes and correlations of attributed 3D point clouds to determine the relationships between points, the correspondence between the regions to which each related point belongs and each part or related part of the 3D appearance of the object to be identified, and can more accurately determine the point sample features belonging to the object to be identified, thereby improving the efficiency and accuracy of "non-specific feature recognition".

[0080] In the process of "non-specific feature recognition," this invention can also acquire the identity information of the object to be identified when necessary. Specifically, when it is determined that the identity information of the object to be identified needs to be acquired, an identity information acquisition command is triggered. This command is used as one of the inputs in the calculation of the result data of the node device. By driving the node device in the peer-to-peer computing system, which is connected to the barrier-free data collection conditions capable of obtaining the identity information of the object to be identified, to respond with the corresponding result data, the identity information of the object to be identified is acquired. The acquisition of identity information is also the result of collaborative computing; that is, the acquisition of identity information is triggered by the determination that it needs to be acquired, rather than by an additional triggering through a specific request command. Based on this invention, if permission calculation is triggered by a request command, in most cases it can be completed without acquiring identity information. Only in a few cases, when it is found that permission calculation cannot be completed without acquiring identity information, will the determination that it is necessary to acquire identity information be generated according to implementation requirements. For example, if collaborative computing reveals that a person's identity information exists in several location-based QR code registration systems, package pickup registration systems, or consumer registration systems, and prior authorization from the person or legal access to these systems is obtained, then the peer-to-peer computing system can drive node devices connected to these systems via barrier-free data collection. The obtained information is then sent to the peer-to-peer computing system through each node device for information comparison and to provide accurate identity information. Based on this, the present invention can also minimize the possibility of identity tampering with a system.

[0081] Specifically, the peer-to-peer computing system determines the permissions of an object by verifying the authenticity of its identity information. In this system, node devices capable of acquiring identity information may not provide the identity information (or may provide it depending on implementation requirements), but instead express the verification result in their own result data based solely on the verification requirements for the authenticity of the identity information within the received result data. That is, in this invention, even when a node device capable of acquiring identity information does not provide it, the verification result is expressed in its own result data based solely on the verification requirements for the authenticity of the identity information within the received result data.

[0082] When a node device in a peer-to-peer computing system that can obtain identity information does not provide identity information, the information source device that drives the provision of identity information establishes an encrypted file transmission channel with the input terminal of the node device that needs to obtain identity information, or establishes an encrypted information transmission channel using other network communication modes; and uses the identity information as one of the inputs of the node device.

[0083] When necessary, in order to meet the needs of other traditional computing modes for raw data, such as the need for evidence preservation in traditional evidence presentation, in this embodiment, the node settings can be equipped with a data storage device for storing the raw data sensed by the sensor.

[0084] In practical implementation, the node device can also be equipped with leakage protection and other functions in its power supply. The node device can also provide various communication interfaces, including fiber optic interfaces and wireless communication interfaces; it can also provide a data interface for connecting external storage devices. The node device can be powered by solar energy or mains power. When implemented outdoors, the node device can be installed on poles such as streetlights (without crossarms, mounted on the main pole, or integrated into the lampshade); in pole-less areas, if implemented indoors, it can be wall-mounted or integrated into the ceiling.

[0085] When this invention is implemented indoors and outdoors, the node devices, as artificial intelligence facilities installed in public spaces, can serve as digital economic infrastructure for urban clusters, providing 24 / 7 seamless coverage. Through collaborative computing across node devices, vehicle identification at any location within the coverage area can achieve near 100% accuracy, with location identification accuracy related to sensor accuracy.

[0086] In this peer-to-peer computing architecture, all node devices are of the same type and function. Each node device dynamically adjusts its data processing model in real time according to the network's consensus mechanism. The raw data collected by the data acquisition devices (including sensors, cameras, etc.) connected to each node device is processed and encrypted by the node device according to its own data processing model, generating byte-level processing and encryption results (i.e., result data). This result data is then sent to other node devices (the computational and encryption results output by other node devices simultaneously received by the current node device are also considered part of the raw data collected by the current node device). Therefore, the effect of the raw data sensed by each sensor will propagate exponentially among a massive number of peer-to-peer node devices. If each node device sends its result data to 100 surrounding node devices, after four units of time, hundreds of millions of node devices will be affected by the event sensed by that sensor. In this computing model, information is relatively symmetrical and immune to tampering and forgery. It fundamentally solves the inherent hidden dangers of traditional information technology, namely, the false, forged, and erroneous information caused by information asymmetry, which in turn become entry points for fraud and cyberattacks, as well as the problems of long cycles, poor accuracy, and poor adaptability in complex and integrated applications. In turn, it truly becomes an information infrastructure for comprehensive management of large areas and a digital economy infrastructure.

[0087] This invention utilizes the collaborative computing of a peer-to-peer computing system. When the results of this collaborative computing can identify an event, the event is detected. In this embodiment, the event detection by the peer-to-peer computing system includes the event's content, its location, and the corresponding response. In a peer-to-peer computing system, all events are processed synchronously; it is not necessary to explicitly produce staged outputs such as what event was detected or its specific content. In a peer-to-peer computing system, only the sensor's perception and the corresponding execution device's response are explicitly defined. All other intermediate processes are handled simultaneously by collaborative computing. That is, during the operation of this invention, the intermediate process of event detection is imperceptible; it is achieved as collaborative computing progresses, the node devices acquire their result data, and the corresponding execution devices automatically respond and execute.

[0088] In this invention, automated production equipment is primarily used as the execution device, and each device joins the peer-to-peer computing system through one or more node devices. To prevent hijacking, this invention can use multiple node devices to collaboratively control the automated production equipment, further improving its immunity to hijacking attacks. The automated production equipment, acting as the execution device, also connects to the node devices as an access device, submitting control requests to the peer-to-peer computing system. In this invention, the execution control command of the automated production equipment as the execution device can be considered a request command. The response to the request command includes various different scenarios such as "request-execution," "request-response," or others. When the result data calculated by one or more node devices in the peer-to-peer computing system matches the request command, the result corresponding to the request command is represented in the result data output by one or more node devices, according to preset conditions, a pre-deployed program, or a data processing model deployed on the node device. If, based on collaborative computing, it is determined that the current node device needs to respond to the request command, the current node device will send instructions to the execution device connected to it according to the calculated result data, controlling the execution device to complete the response action; that is, the "request-execution" scenario. In this invention, if a certain control component or a combination of control components of an automated production equipment is determined to require automatic control based on corresponding control requirements based on collaborative computing, the current node device will send control commands to the control components connected to the current node device according to the calculated result data, and control the control components to complete the control actions.

[0089] Based on peer-to-peer computing, collaborative computing allows the execution device to act as a node device. As collaborative computing progresses, when the result data obtained by the execution device corresponds to the request command and can be used to perform related operations, the execution device completes the response to the request command. In this invention, control requirements are represented by result data; automated production equipment receives the result data output by the connected node devices. If a specific element in the result data indicates that the control component needs to be automatically controlled;

[0090] Alternatively, the resulting data can be used as one of the inputs to the data processing model of the node device. If the calculation determines that the corresponding control component needs to automatically control the device, then the control component will perform the corresponding control action.

[0091] When automatic control of a component is required, the automated production equipment combines the result data received from other node devices to calculate its own result data. Based on this result data, it controls the control components on the control unit to execute corresponding control actions. In this invention, the automated production equipment does not need to first determine whether it needs to respond. Instead, it combines the result data received from other node devices with the perception data collected by its own sensors, inputs this data into its own data processing model, and outputs the control actions performed by the control components.

[0092] In this invention, the calculated result data includes the optimal solution for all situations obtained through collaborative calculation of all targets within the monitored area and the external environment at the current moment; the control actions are executed through the control components on the control components. In this invention, the results of the peer-to-peer computing system for calculating various types of information are all represented as result data. All automated production equipment, as one of the node devices, contributes the optimal solution for all situations when participating in the collaborative calculation of the peer-to-peer computing system. Furthermore, the execution control commands of all automated production equipment are the optimal solution commands output by the node devices connected to it after collaborative calculation. This invention does not have the traditional generation and transmission commands, precisely to avoid security vulnerabilities that could make automated production equipment a risk point.

[0093] In this embodiment, the automated production equipment is a node device that connects to execution components with specific functions. The execution feedback information of the execution components of the automated production equipment is fed back to the automated production equipment and participates in the calculation of the subsequent result data of the automated production equipment.

[0094] In this invention, since automated production equipment can be used as one of the node devices, its response execution is based on the computational results obtained through collaborative computing, resulting in high response efficiency and avoiding illegal responses such as false execution or failure to execute when required due to network attacks. To prevent hijacking, this invention can also use multiple node devices to collaboratively control the automated production equipment, further improving its immunity to hijacking attacks.

[0095] In a peer-to-peer computing system, the result data calculated and output by node devices can be implemented as a state corresponding to the perceived data (i.e., the raw data), which can be represented using state values. Therefore, node devices do not need to store and transmit the raw data. In this embodiment, the data or elements in the multidimensional matrix are related to the installation location, attributes, etc., of each node device. Therefore, when transmitting the result data, what is actually transmitted is the transcoded result after transcoding multiple sets of parameters. A multidimensional matrix is ​​actually a combination of multiple sets of parameters. For example, if the path to a target is from abcd, and the physical locations of the abcd node devices are fixed, then the sequence abcd can be expressed using a single character or a similar concept during multi-parameter transcoding and transmission.

[0096] Based on the technical characteristics of peer-to-peer computing, it can be applied to various application scenarios that provide targeted services or control for a specific target or event. Since the data transmitted between node devices is the result of information processing, rather than the information itself, the raw data collected (i.e., perceived data) does not need to be stored. Node devices only receive the computation results output by other node devices and send out their own computation results. The information contained in a single computation result is insufficient to reconstruct any event or target information; a definite result can only be obtained through collaborative computation involving the computation results across the entire peer-to-peer computing system, multi-dimensional data matrix elements, and the correspondence between physical space and facilities. Collaborative computation has less dependence on the information transmitted by a few node devices, thus fundamentally changing the traditional single-point security sensitivity of information systems.

[0097] In this invention, since the output data of each node device reflects the state evolution of the output data of the preceding node devices, the behavior, attributes, state, or events of the target when it was perceived by the preceding node devices can be inferred based on the output data received by the current node device. For example, when it is necessary to find the location of target 'a' 15 minutes ago, the location of the node device that perceived target 'a' can be obtained at the current moment, thus inferring the location of target 'a'. Then, based on the transmission path of the output data, it can be inferred back to 15 minutes ago to estimate the location of target 'a' 15 minutes ago (determined by the node device that perceived target 'a'). Furthermore, the node device does not need to store the original data about target 'a'. That is, based on this invention, it is not necessary to identify the original data to find target 'a', but rather to first infer the node device that perceived target 'a', and if necessary, obtain the original data about target 'a' at the time when it needs to be found from the storage device connected to the node device.

[0098] In this invention, the automated production equipment receives result data output from other node devices. The principle is as follows: when a control command requires the corresponding control component to operate automatically, if the result data calculated by one or more node devices can determine the control component that needs to be operated automatically, then the corresponding control component is added to the node list for transmitting the current result data. The one or more node devices directly transmit the result data to the automated production equipment or the node devices connected to the automated production equipment. In this case, based on preset conditions or algorithm output or model output, the corresponding automated production equipment is added to the node list for transmitting result data.

[0099] Alternatively, automated production equipment receives the result data output by other node devices in a layer-by-layer transmission manner. During collaborative computing in a peer-to-peer computing system, each node device calculates a list of nodes that need to receive the result data. Based on the current result data, it clearly identifies one or more execution devices (mainly automated production equipment) that need to be added, and these are added to the node list. The execution device or the node device connected to it is then used as the next-layer node device to directly receive the current result data, thus bypassing normal layer-by-layer transmission and transforming the peer-to-peer computing system into a three-dimensional architecture. For example, if the result data of the current node device clearly indicates the need for evidence, according to the normal layer-by-layer transmission method, the result data of the current node device would require at least one or more layers of transmission to reach the corresponding node device. However, by adding the corresponding node device to the node list, the corresponding node device can directly receive the result data of the current node device during the next layer of transmission, thereby significantly shortening the processing time and improving responsiveness. This invention employs a peer-to-peer computing system; therefore, this temporary construction is precisely the advantage of this invention. Traditional layer-by-layer information aggregation architectures cannot withstand the complex computing demands brought about by such a temporary network construction.

[0100] The above embodiments are merely illustrative of the present invention and are not intended to limit the invention. Any changes or modifications to the above embodiments based on the technical essence of the present invention will fall within the scope of the claims of the present invention.

Claims

1. A method for obtaining product certification information, characterized in that, Acquire certification information across all stages of a product's journey from planting or production to shelf sale, and between these stages; when a product is on the shelf for sale, the product placement location is associated with the product, and the product placement location is configured with location information, which is linked to the product's certification information; when the location information of the product's placement location is obtained through data devices, the certification information associated with the product placement location information is retrieved. The peer-to-peer computing system is used to perform non-specific feature recognition and location recognition of targets, including products, raw materials, additives, workers, production tools, and automated production equipment. The peer-to-peer computing system includes multiple node devices, and there is no hierarchy among the node devices. Each node device is equipped with a data acquisition device and a computing module. The data acquisition device includes at least one type of sensor, including an image acquisition device, for collecting different types of sensing data. Node devices located at different acquisition positions collect at least one point sample of the target, and the point sample is sensing data of the corresponding sensor type. For a given node device, the collected sensing data is processed to obtain result data, which is then propagated to other node devices. Other node devices receiving the result data use it as one of the original collected data, influencing the result data of other node devices. Based on this, without needing to obtain the target's identity information, multiple node devices in the peer-to-peer computing system perform collaborative computation to determine each unique target as itself, achieving non-specific feature recognition and target location identification, including product positioning information.

2. The product certification information acquisition method according to claim 1, characterized in that, The system acquires location information for a product at each stage from planting or production to shelf sale, and across each stage. When a product is on the shelf for sale, the location information is used to associate the product with its placement location. The data device includes an identification device that identifies the product, acquires the product's current location information, and then acquires the location information of the product's placement location.

3. The product certification information acquisition method according to claim 2, characterized in that, The system acquires image information across all stages of a product's journey from planting or production to its sale. When a product is on the market, the recognition device uses image recognition to identify the current product and related operations performed on it based on image features and angles, thereby obtaining the product's current location information.

4. The product certification information acquisition method according to claim 3, characterized in that, When the identification device performs product identification, it displays a virtual label in the identification image or video; the virtual label corresponds one-to-one with the position information of the product placement position, and the identification device identifies the virtual label to obtain the position information of the product placement position corresponding to the product.

5. The product certification information acquisition method according to claim 1, characterized in that, The system acquires location information for products at each stage from planting or production to shelf sale, as well as across these stages; when products are on the shelf for sale, a label is set for each product placement location; the data device includes a reading device that reads the label to obtain the location information of the corresponding product placement location.

6. The product certification information acquisition method according to claim 5, characterized in that, The identification label corresponds one-to-one with the product placement location information; the reading device reads the identification label to obtain the location information to be verified, first treating the location information to be verified as the location information of the product placement location, obtaining the certification information corresponding to the location information, and verifying whether the location information of the product placement location associated with the certification information is consistent with the location information to be verified. If it is consistent, the certification information of a product associated with the product placement location corresponding to the current location information is obtained; if not, the current identification label is invalid.

7. The product certification information acquisition method according to claim 1, characterized in that, If the product includes both contents and outer packaging, obtain certification information for each stage of the process, from planting or production to packaging and sales, and across these stages.

8. The product certification information acquisition method according to claim 1, characterized in that, The system deploys image acquisition devices that cover all stages of a product's journey from planting or production to sale, as well as the data acquisition devices that capture various data required for certification information, in addition to the image acquisition devices. The image acquisition devices capture monitoring videos of the product's journey from planting or production to sale, as well as the data captured between these stages. In adjacent stages, the image acquisition devices used to monitor the preceding stage, the stages between stages, and the following stages have overlapping monitoring ranges. Correspondingly, the monitoring videos of the preceding stage, the stages between stages, and the following stage have overlapping video frames, forming a continuous video and completing the uniqueness verification of the product.

9. The product certification information acquisition method according to claim 8, characterized in that, The basic data collected by data acquisition devices, including image acquisition devices, is encrypted and stored according to timestamps, and the decryption keys are stored in various third-party regulatory agencies with authentication rights.

10. The product certification information acquisition method according to claim 9, characterized in that, For various products, based on the certification standards and claimed characteristics corresponding to the preset certification type, the basic data is verified. If the verification passes, the corresponding certification information and claimed characteristics are determined; otherwise, the corresponding certification information and claimed characteristics cannot be determined or are cancelled. If the verification cannot be completed, or the amount of basic data is less than the preset quantity standard, the encrypted basic data is sent to a third-party regulatory agency with certification authority. The third-party regulatory agency decrypts and verifies the basic data to determine the verification result.

11. The product certification information acquisition method according to claim 10, characterized in that, The system automatically searches product sales pages and verifies the certification type and claimed features of the labels by verifying the basic data. If verification cannot be completed, the system reports the issue to a third-party regulatory agency with certification authority.

12. The product certification information acquisition method according to claim 10, characterized in that, The authentication and verification methods are as follows: Real-time authentication and verification of basic data; Alternatively, the basic data can be authenticated and verified at a preset duration or period. Alternatively, the basic data can be authenticated and verified in real time or at a preset duration or period to obtain the current verification result. When the next basic data differs from the previous basic data or the difference value is greater than the preset difference threshold, the next basic data is authenticated and verified. This cycle continues.

13. The product certification information acquisition method according to claim 8, characterized in that, When obtaining authentication information, the system also obtains access links to basic data and decrypts them using decryption keys held by third-party regulatory agencies, thereby gaining access to the basic data.

14. The product certification information acquisition method according to claim 13, characterized in that, Corresponding to each stage of the product's journey from planting or production to its sale, and across different stages, sound and light alarm devices are installed. When the monitoring video captured by the image acquisition device is found to contain obstructions to the product, the corresponding sound and light alarm device will sound an alarm. If the obstruction of the product exceeds a preset time, the monitoring video is deemed discontinuous, and the product's uniqueness verification fails.

15. The product certification information acquisition method according to claim 8, characterized in that, The system monitors and identifies the behavior of staff during the planting or production of the product through video surveillance; it also uses corresponding data acquisition devices to detect and collect data in real time on the gases, liquids, soil, and raw materials that the product comes into contact with during the planting or production process.

16. The product certification information acquisition method according to claim 8, characterized in that, Basic data or authentication information is stored through blockchain or peer-to-peer computing systems, and the compliance of the basic data or authentication information is confirmed by various third-party regulatory agencies with authentication rights.

17. The product certification information acquisition method according to claim 8, characterized in that, For a user-specified product, a unique identifier is set, and image data of the product is acquired through an image acquisition device at each stage from planting or production to sales, as well as across the stages; in the image data, the product and the unique identifier coexist in the same frame.

18. The product certification information acquisition method according to claim 1, characterized in that, The current node device receives the result data output by other node devices; for the current node device, it combines the collected sensing data with the result data from other node devices to calculate the result data of the current node device, and then sends it to other node devices; In a peer-to-peer computing system, node devices perform collaborative computing as they collect sensing data and calculate result data.

19. The product certification information acquisition method according to claim 18, characterized in that, In a peer-to-peer computing system, for a specific point sample of a target, the resulting data transmitted from the node device that collected the point sample to other node devices allows subsequent node devices to adjust their perceptual attention based on the features of that point sample, or report the features of that point sample for subsequent node devices to adjust their perceptual attention. If other subsequent node devices do not detect the features of that point sample, but can determine from the features of other point samples that the undetected features still belong to the target, then the undetected features of that point sample are continued to be represented in the result data of the current node device and transmitted to other node devices.

20. The product certification information acquisition method according to claim 19, characterized in that, The method for reporting the features of the point sample to subsequent node devices for adjusting the perceptual attention is as follows: based on the result data expressing the features of the point sample provided by the preceding node device, or the features of the point sample, adjust the parameters of the data processing model of the subsequent node device so that the subsequent node device can improve the computing power of the subsequent node device to identify the features of the point sample; or, the subsequent node device uses the perceptual attention model to match the features of the received point sample or the result data expressing the features of the point sample to adjust the computing power.

21. The product certification information acquisition method according to claim 20, characterized in that, When a node device processes the output data of several preceding node devices, based on the data processing model, if the target described by several preceding node devices can be identified as the same target through certain common point sample features, the point sample features and other information described by each node device are merged into the same target.

22. The product certification information acquisition method according to claim 21, characterized in that, If the result data received by a node device indicates that the flag used by the current node device to identify the target before the current receipt of result data is different from the flag used by other node devices to identify the target, and the flags assigned to the target by other node devices have been updated, then the flag used by the current node device to identify the target before the current receipt of result data is converted.

23. The product certification information acquisition method according to claim 21, characterized in that, The method for converting the flag used to identify the target by the current node device before the current reception of result data is as follows: Replace the flag used by the current node device to identify the target before the current reception of result data with the latest flag assigned to the target by other node devices; Alternatively, record the conversion relationship between the flag used by the current node device to identify the target before the current reception of result data and the updated flags assigned to the target by other node devices, and perform the conversion when it is necessary to reference the result data received by the current node device in the current reception. Alternatively, node devices can deploy transformation models to perform corresponding transformations on the labels of multiple targets based on the input raw data or result data.

24. The product certification information acquisition method according to claim 19, characterized in that, For one or more point samples collected sequentially by node devices at different collection locations, if the feature values ​​of one or more point samples at different collection locations meet the preset similarity conditions or are determined by a specific model to have a correlation threshold, and are unique at each collection location, then it is determined that the point samples at different collection locations are correlated.

25. The product certification information acquisition method according to claim 19, characterized in that, If node devices at different acquisition locations collect one or more point samples simultaneously, and if the node devices at different acquisition locations collect samples from the same spatial field, and there is only one target in the spatial field, or the collected point sample can correctly point to one of the multiple targets, then for a certain target, the one or more point samples collected by node devices at different acquisition locations are correlated.

26. The product certification information acquisition method according to claim 25, characterized in that, The data acquisition device of the node equipment includes one or more of the following: image acquisition device, electromagnetic induction device, temperature measurement device, vibration frequency sensing device, and lidar. It performs joint calculations on the data acquired by the above devices and the 3D point cloud acquired by the lidar, or on the point cloud generated from images acquired by multiple image acquisition devices, to obtain 3D points with data. It uses image color, contour, lines, reflectivity, motion trend, electromagnetic characteristics, temperature, temperature change trend, vibration frequency, and vibration frequency change trend based on 2D perception as additional attributes of the corresponding 3D points, forming an attributed 3D point cloud. Combining electromagnetic induction, temperature patterns, vibration frequency change characteristics, motion correlation, and reflectivity, it determines the correspondence between each region of the attributed 3D point cloud and each or related part of the consumer's 3D appearance.

27. The product certification information acquisition method according to claim 1, characterized in that, The data acquisition device includes one or more of the following: image acquisition device, gas composition sensor, water quality sensor, soil composition sensor, audio acquisition device, temperature measurement device, vibration frequency sensing device, lidar, chemical sensor, and electromagnetic induction device.

28. The product certification information acquisition method according to claim 1, characterized in that, Obtain the component parameters of each control component of the automated production equipment; each control component of the automated production equipment, or a combination of multiple control components that form the same function, is added to the peer computing system through one or more node devices; if, based on collaborative computing, it is determined that a certain control component or a combination of multiple control components of the automated production equipment needs to be automatically controlled based on the corresponding control requirements, the current node device will send control commands to the control components connected to the current node device according to the calculated result data, and control the control components to complete the control actions.

29. The product certification information acquisition method according to claim 28, characterized in that, Control requirements are represented by result data; the control unit receives the result data output by the connected node device. If a specific element in the result data indicates that the control unit needs to perform automatic control, or if the result data is used as one of the inputs to the data processing model of the node device, and it is calculated that the corresponding control unit needs to perform automatic control, then the control unit executes the corresponding control action.

30. The product certification information acquisition method according to claim 29, characterized in that, When automatic control of the control component is required, the control component combines the result data received from other node devices to calculate its own result data, and controls the control component on the control component to perform the corresponding control action based on the obtained result data.

31. The product certification information acquisition method according to claim 30, characterized in that, The control unit receives the result data output by other node devices. The principle is: when the control command requires the corresponding control unit to operate automatically, if the result data calculated by one or more node devices can determine the control unit that needs to be operated automatically, then the corresponding control unit is added to the node list for transmitting the current result data. The one or more node devices directly transmit the result data to the control unit or the node device connected to the control unit. Alternatively, the control unit receives the result data output by other node devices in a layer-by-layer transmission manner.

32. The product certification information acquisition method according to claim 31, characterized in that, Based on preset conditions or algorithm output and model output, the corresponding control components are added to the node list for transmitting result data.

33. The product certification information acquisition method according to claim 29, characterized in that, The control unit is a node device that connects to the execution unit for a specific function. The execution feedback information of the control unit is fed back to the control unit and participates in the calculation of the subsequent result data of the control unit.

34. The product certification information acquisition method according to claim 1, characterized in that, The control components of automated production equipment are incorporated into the peer-to-peer computing system as node devices. If an anomaly is determined to exist in the control component based on collaborative computing, the anomaly data is used as one of the inputs to participate in the calculation of the result data, and a processing solution is obtained through collaborative computing.