A supply chain settlement method based on monitoring identification

Abstract

The present application relates to a kind of supply chain settlement methods based on monitoring identification, to each manufacturer from raw material delivery, production to transport to next manufacturer each link, raw material to finished product is identified and position identification, to determine the purchase of raw material, the delivery of finished product, to identify result as trusted credit guarantee, realize that manufacturer in supply chain no longer needs to use cash to settle each business activity, both sides need not to collect and pay for current business activity, only need to collect or pay for all business activities that occur in itself, in settlement, based on the difference of income and expenditure offset, then for each manufacturer in supply chain, can reduce the dependence on cash. The present application can greatly reduce the problem of manufacturer's capital turnover, reduce the risk of capital chain break; and comprehensive settlement for each manufacturer itself, settlement logic is simple, improve the efficiency of each manufacturer settlement simultaneously, continuously.

Description

Technical Field

[0001] This invention relates to the field of transaction processing technology, and more specifically, to a supply chain settlement method based on monitoring and identification. Background Technology

[0002] In modern business operations, especially in B2B scenarios, digital transaction settlement has become almost ubiquitous, simplifying the receipt and payment of accounts receivable and payable. However, in digital transactions, the payer still needs sufficient funds in their account to complete the payment. For a company or manufacturer, under normal circumstances, due to continuous business activities such as procurement, production, and sales, there is always dynamic inflow and outflow of funds. Correspondingly, the account balance also fluctuates dynamically, potentially leading to "cash flow" problems at certain times. In particular, insufficient funds can prevent payments, causing default risks or even serious problems such as a broken cash flow, negatively impacting the company's development in ways that could otherwise be avoided. Consequently, companies tend to be more conservative with their finances, which to some extent limits their growth rate.

[0003] The implementation and application of existing transaction settlement technologies all rely on the premise that a company's account balance is sufficient to complete the payment. This is because existing transaction settlement technologies cannot form credit assets for a company's business activities or provide reliable credit guarantees for commercial activities between companies. Therefore, existing transaction settlement technologies cannot solve a company's cash flow problems, nor can they guarantee the security of funds between companies conducting commercial activities without increasing their account balances, thus failing to promote the company's positive development. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a supply chain settlement method based on monitoring and identification. By monitoring and identifying the production activities of manufacturers in the supply chain and the business activities between manufacturers, the reliable identification results are equivalent to forming a credit guarantee. Manufacturers can offset their income and expenditure by paying the contract amount corresponding to their own business activities, which can significantly reduce their dependence on and consumption of cash flow.

[0005] The technical solution of the present invention is as follows:

[0006] A supply chain settlement method based on monitoring and identification involves using a monitoring system to identify and locate each manufacturer within its coverage, from raw material delivery and production to transportation to the next manufacturer, thus confirming the completion of raw material purchases and finished product delivery. For manufacturers with upstream and downstream relationships in the supply chain, the settlement system uses purchase contracts and sales contracts for each purchase as receipts and expenditure vouchers. Based on the expenditure amount of the purchase contract and the revenue amount of the sales contract, the system offsets receipts and expenditures to obtain a settlement result. Based on the settlement result, the settlement system sends a collection request or payment request to the manufacturer.

[0007] Preferably, if several manufacturers form a closed-loop payment path based on purchasing or sales relationships, then based on the settlement results, the settlement system sends payment requests to the manufacturers that need to pay and collects the corresponding payment amount; based on the settlement results, the settlement system allocates the payment amount to the manufacturers that need to collect payment and sends collection requests to the manufacturers that need to collect payment, transferring the corresponding collection amount.

[0008] As a preferred option, the settlement time point is based on the production cycle of each batch of finished products, and a settlement is carried out among the manufacturers involved in the production of each batch of finished products; or, a settlement is carried out among all manufacturers according to the time cycle.

[0009] As a preferred option, the corresponding finished products are produced by the manufacturer through automated production equipment, and the finished products are transported and loaded onto vehicles through automated handling equipment, thus automatically completing the production, handling, and loading process from raw materials to finished products.

[0010] As a preferred option, the settlement system is deployed within a financial institution. The settlement system receives supply requests from downstream manufacturers in the supply chain and sends delivery requests to upstream manufacturers based on these requests. Once the monitoring system determines that the upstream manufacturer has completed the delivery and that it meets the contractual standards of the purchase contract, the settlement system pays the upstream manufacturer the corresponding profit using the financial institution's own funds and records the corresponding outstanding payments to the downstream manufacturers. Based on this, when a downstream manufacturer supplies goods to its downstream manufacturers, it performs the corresponding settlement and obtains the corresponding profit.

[0011] As a preferred approach, the system analyzes and obtains the contract standards for purchase and sales contracts. These standards include, but are not limited to, raw material grade standards, process flow standards, and production environment parameter standards. It monitors and obtains the raw material grade, process flow, and production environment parameters from raw material delivery and production to transportation to the next manufacturer at each stage. It then determines whether the obtained raw material grade, process flow, and production environment parameters meet the contract standards. If so, the financial institution uses the settlement system to pay the corresponding profit to the next-level manufacturer for the current finished product and records the corresponding outstanding payment to the next-level manufacturer.

[0012] Preferably, the monitoring system is a peer-to-peer computing system, which uses the peer-to-peer computing system to perform non-specific feature recognition and location recognition of targets. The targets include, but are not limited to, raw materials, additives, finished products, workers, production tools, automated production equipment, and automated handling equipment.

[0013] 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.

[0014] 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 the positioning information of the finished product.

[0015] Preferably, the current node device receives the result data output by other node devices; for the current node device, the collected sensing data is combined with the result data from other node devices to calculate the result data of the current node device, and then sent to other node devices; the node devices in the peer-to-peer computing system perform collaborative computing as sensing data is collected and result data is calculated.

[0016] As a preferred approach, in a peer-to-peer computing system, for a specific point sample of a target, the subsequent node devices adjust their perceptual attention based on the characteristics of the point sample in the result data transmitted from the node device that collected the point sample to other node devices, or report the characteristics of the point sample for subsequent node devices to adjust their perceptual attention. If other subsequent node devices do not detect the characteristics of the point sample, but can determine from the characteristics of other point samples that the undetected characteristics of the point sample still belong to the target, then the undetected characteristics of the point sample are continued to be represented in the result data of the current node device and transmitted to other node devices.

[0017] As a preferred method, the method of 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, or the features of the point sample, 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.

[0018] Preferably, when processing the result data output by several preceding node devices, the node device, based on the data processing model, merges the point sample features and other information described by each node device into the same target when the target described by several preceding node devices can be determined as the same target through certain common point sample features.

[0019] Preferably, when the result data received by the 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.

[0020] As a preferred method, the method for converting the flag used by the current node device to identify the target before the current reception of result data is as follows:

[0021] 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;

[0022] 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.

[0023] 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.

[0024] As a preferred option, 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.

[0025] Preferably, for one or more point samples collected simultaneously by node devices at different acquisition locations, if the node devices at different acquisition locations collect data on 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, one or more point samples collected by node devices at different acquisition locations are correlated.

[0026] Preferably, the data acquisition device of the node device includes one or more of the following: an image acquisition device, an electromagnetic induction device, a temperature measurement device, a vibration frequency sensing device, and a lidar. The data acquired by the above devices 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 form an attributed three-dimensional point cloud. Combining electromagnetic induction, temperature law, vibration frequency change characteristics, motion correlation, and reflectivity, the correspondence between each region of the attributed three-dimensional point cloud and each part or related part of the consumer's 3D appearance is determined.

[0027] Preferably, the data acquisition device includes one or more of the following: an image acquisition device, a gas composition sensor, a water quality sensor, a soil composition sensor, an audio acquisition device, a temperature measurement device, a vibration frequency sensing device, a lidar, a chemical sensor, and an electromagnetic induction device.

[0028] Preferably, each control component of automated production equipment or automated handling 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 automated production equipment or automated handling equipment needs to be automatically controlled based on the corresponding control requirements, the current node device will send control commands to the control component connected to the current node device according to the calculated result data, and control the control component to complete the control action.

[0029] Preferably, the control requirements are represented by the 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 performs the corresponding control action.

[0030] Preferably, 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.

[0031] Preferably, the control component receives the result data output by other node devices. The principle is as follows: when the 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, and the one or more node devices directly transmit the result data to the control component or the node device connected to the control component; or, the control component receives the result data output by other node devices in a layer-by-layer transmission manner.

[0032] As a preferred option, based on preset conditions or algorithm output and model output, the corresponding control components are added to the node list for transmitting result data.

[0033] Preferably, the control component is a node device that connects to the execution component for a specific function. The execution feedback information of the control component of the control component is fed back to the control component and participates in the calculation of the subsequent result data of the control component.

[0034] As a preferred approach, the control components of automated production equipment and automated handling equipment are used as node devices and added to the peer-to-peer computing system. If the control components are determined to be abnormal based on collaborative computing, the abnormal 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.

[0035] The beneficial effects of this invention are as follows:

[0036] The supply chain settlement method based on monitoring and identification described in this invention identifies and locates each manufacturer at every stage, from raw material delivery and production to transportation to the next manufacturer, ensuring the completion of raw material purchases and finished product delivery. The identification results serve as reliable credit guarantees. Furthermore, it continuously monitors and identifies each manufacturer simultaneously, enabling offsetting of income and expenditure for each manufacturer's ongoing business activities within the coverage area. Therefore, this invention eliminates the need for manufacturers in the supply chain to settle each business activity with cash. Based on reliable credit guarantees, both parties in business activities do not need to collect or pay for current transactions; instead, they only need to settle payments based on the difference between income and expenditure offsets for all their business activities. This reduces the reliance on cash for each manufacturer in the supply chain. Consequently, this invention significantly alleviates manufacturers' cash flow problems and reduces the risk of supply chain disruptions. Moreover, it provides comprehensive settlement for each manufacturer, with a simple settlement logic, improving the efficiency of simultaneous and continuous settlement among manufacturers.

[0037] This invention utilizes a peer-to-peer computing system for collaborative computing to perform non-specific feature recognition and location recognition on targets (including raw materials, additives, finished products, workers, production tools, automated production equipment, automated handling equipment, etc.) to complete identity recognition and positioning. Furthermore, the automated production equipment and automated handling equipment respond and execute accordingly based on the calculation results obtained from the collaborative computing, thereby achieving automation of production and handling, and ultimately enabling automated settlement with reliable results.

[0038] In the peer-to-peer computing system described in this invention, there is no master-slave relationship among all node devices, and no fixed connection path between them. Each node device only receives the computation results from other node devices and sends out its own computational results. The response to event discovery and / or corresponding execution devices (in this invention, execution devices mainly include automated production equipment and automated handling equipment) does not rely on a single node device for identification and control, but rather on collaborative computation and joint confirmation by multiple node devices in 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 recognition. This invention performs target identification, identity verification, or event monitoring through non-specific feature recognition, 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, emergency response, community services, market behavior, safe production, and civilized behavior.

[0039] 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.

[0040] 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

[0041] The present invention will be further described in detail below with reference to the embodiments.

[0042] This invention addresses the shortcomings of existing technologies, such as the need for direct settlement of income and expenditure between buyers and sellers in commercial activities, the requirement for sufficient cash for settlement, and the inability to provide reliable credit guarantees. It provides a supply chain settlement method based on monitoring and identification. This method identifies and locates raw materials and finished products at each stage, from raw material delivery and production to transportation to the next manufacturer. The identification results from the monitoring and identification of completed raw material purchases and finished product deliveries serve as the basis for each manufacturer's comprehensive settlement of all its commercial activities. This allows for the offsetting of income and expenditure among manufacturers in the supply chain, achieving a comprehensive settlement. Based on reliable identification results as credit support for comprehensive settlement among all manufacturers in the supply chain, this invention eliminates the need for explicit cash settlement for every single commercial activity, changing the fixed mindset that requires explicit settlement for every single transaction.

[0043] Because manufacturers in the supply chain continuously engage in various business activities (which can be simply understood as buying and selling) with different suppliers, corresponding accounts receivable and payable are constantly generated, resulting in a continuous dynamic change in the manufacturers' account balances. For each manufacturer in the supply chain, due to the financial interdependence between upstream and downstream manufacturers, if one or more upstream manufacturers default on payments for various reasons, downstream manufacturers will face increased cash flow pressure due to slow payments, increasing the risk of a broken capital chain. Simultaneously, the problem of untimely payments will ultimately slow down the cash flow of the supply chain, affecting the overall operational efficiency of the supply chain; this creates a vicious cycle, and in severe cases, can lead to the entire supply chain coming to a standstill, ultimately affecting the survival of each manufacturer.

[0044] Therefore, this invention eliminates the need for explicit cash settlements between manufacturers in every business activity. Instead, it focuses on each manufacturer's own accounts receivable and payable for all business activities, offsetting these receivables and payables to arrive at a settlement result—whether the overall transaction constitutes revenue or expenditure. Based on this, after comprehensive settlement among manufacturers in the supply chain, the amount of revenue or expenditure required for each manufacturer can be determined. The manufacturers requiring expenditure then pay the difference between revenue and expenditure, ultimately forming a cash pool within the supply chain. Based on the settlement results, the cash in the pool is allocated according to the settlement results of surplus manufacturers, completing the supply chain settlement described in this invention. Thus, the implementation of this invention allows manufacturers requiring expenditure to only pay the settlement difference between revenue and expenditure, eliminating the need for full cash payments for each business activity.

[0045] The supply chain settlement method based on monitoring and identification described in this invention uses the purchase of raw materials and the sale of finished products by each manufacturer as settlement nodes. Purchases are recorded as expenses, and sales are recorded as revenue. Comprehensive settlement can then be performed within a certain time frame to offset expenses and revenues, resulting in a final settlement. When all manufacturers in the supply chain are included in the comprehensive settlement—meaning both parties involved in the comprehensive settlement business activity are within the monitoring system's coverage—comprehensive settlement of all business activities in the supply chain can be completed when performing comprehensive settlement for each manufacturer. Specifically, this invention uses the monitoring system to identify and locate each manufacturer within its coverage, from raw material delivery and production to transportation to the next manufacturer, thus confirming the completion of raw material purchases and finished product deliveries. If the raw materials purchased by a current manufacturer are actually finished products from another manufacturer, then in this business activity, the current manufacturer and the other manufacturer, as the two parties in the business activity, record the sale of the other manufacturer's finished products as revenue and the purchase of the current manufacturer's raw materials as expenses when performing comprehensive settlement for themselves. Although the two manufacturers in this business activity determine their income and expenses at different times, this does not affect their overall settlement. Therefore, the current manufacturer does not need to prepare sufficient cash for this business activity; similarly, the other manufacturer does not need to prepare sufficient cash for its other business activities. The same principle applies to all business activities undertaken by manufacturers throughout the supply chain.

[0046] Based on the identification results, during settlement, for manufacturers that form upstream and downstream relationships in the supply chain (i.e., both parties in the aforementioned comprehensive settlement business activities, all existing within the monitoring system's coverage; these can be two manufacturers directly engaged in business activities, or multiple manufacturers with multiple business activities forming a revenue and expenditure path, such as manufacturers A, B, C, and D; if two adjacent manufacturers both engage in business activities, then cash can be considered to flow sequentially from manufacturer A to manufacturer D, and manufacturers A, B, C, and D form an upstream and downstream relationship in the supply chain), when performing comprehensive settlement for a particular manufacturer, the settlement system uses the purchase contract corresponding to each purchase and the sales contract corresponding to each sale as revenue and expenditure vouchers. Based on the expenditure amount of the purchase contract and the revenue amount of the sales contract, revenue and expenditure are offset to obtain the settlement result. Similarly, comprehensive settlement for all manufacturers forming upstream and downstream relationships in the supply chain can be completed. Ultimately, the settlement result for the entire supply chain based on all business activities is obtained; that is, at the settlement time, after all manufacturers have conducted all business activities, the revenue and expenditure difference after offsetting all purchase contracts and sales contracts. Manufacturers use raw materials to produce finished products, typically creating added value, and thus the settlement result of the supply chain is usually value-added. Since the settlement result of the supply chain is the difference between income and expenditure, rather than the sum of the amounts due from all business activities, it can greatly reduce the cash needs and consumption of individual manufacturers.

[0047] The settlement system sends payment or collection requests to manufacturers based on the settlement results. In the aforementioned supply chain settlement results, some manufacturers may need to pay cash for business activities, while others may receive cash. The settlement system then sends corresponding payment or collection requests to different manufacturers, who then process the payments or collections accordingly. In this invention, after each comprehensive settlement, each manufacturer only needs to process one payment or collection transaction based on the settlement results, saving a significant amount of workload.

[0048] In terms of compliance, this invention uses the purchase contract corresponding to each purchase and the sales contract corresponding to each sale as receipts and payment vouchers. This allows the manufacturer to provide the relevant purchase and sales contracts to the tax authorities, and the manufacturer can then file taxes based on the contract amount, thus avoiding the risk of tax non-compliance.

[0049] Because in the entire supply chain, not all manufacturers can typically be connected through a single revenue and expenditure path. Instead, some manufacturers form revenue and expenditure paths, and multiple paths intersect to constitute the overall revenue and expenditure relationship of the supply chain. In this embodiment, for manufacturers forming a closed-loop revenue and expenditure path—that is, if several manufacturers form a closed-loop revenue and expenditure path based on purchasing or selling relationships, for example, manufacturer A sells finished products to manufacturer B, manufacturer B sells finished products to manufacturer C, and manufacturer C sells finished products to manufacturer A—a closed-loop revenue and expenditure path is formed. Based on the settlement result, the settlement system sends payment requests to the manufacturers that need to pay and collects the corresponding payment amount. Based on the settlement result, the settlement system allocates the payment amount to the manufacturers that need to collect payment and sends collection requests to the manufacturers that need to collect payment, allocating the corresponding collection amount. Therefore, in the execution of this invention, the settlement of the closed-loop revenue and expenditure path can be performed separately, that is, the settlement of the closed-loop revenue and expenditure path and the overall supply chain settlement are performed in separate timeframes. This not only does not affect the overall supply chain settlement but also simplifies the complexity of the overall supply chain settlement to a certain extent.

[0050] In this invention, the settlement time can be set according to different implementation needs, including but not limited to the finished product settlement principle and the periodic settlement principle. The finished product settlement principle uses the production cycle of each batch of finished products as the settlement time point, and settles accounts once between the manufacturers involved in the production of each batch of finished products; the periodic settlement principle settles accounts once with all manufacturers according to a time cycle.

[0051] To provide reliable identification results and form a credible credit guarantee, ensuring the credibility of contracts as proof of income and expenditure, this invention utilizes automated production equipment to produce the corresponding finished products from the manufacturer, and automated handling equipment to transport and load the finished products, automatically completing the production, handling, and loading process from raw materials to finished products. By monitoring and identifying the automated production and handling equipment, identification results for the production, handling, and loading of finished products are obtained, including identification and location identification from raw materials to finished products. This automatic acquisition of identification results reduces the interference and impact of human intervention on the identification process.

[0052] Furthermore, this invention deploys a settlement system within a financial institution. The settlement system receives supply requests from downstream manufacturers in the supply chain and, based on these requests, sends delivery requests to upstream manufacturers. The monitoring system determines that the upstream manufacturer has completed delivery and that the delivery meets the contract standards of the purchase contract. The settlement system then uses the financial institution's own funds to pay the upstream manufacturer the corresponding profit and records the corresponding outstanding payments to the downstream manufacturer. Specifically, the system parses and obtains the contract standards of the purchase and sales contracts, including but not limited to raw material grade standards, process flow standards, and production environment parameter standards. From raw material delivery and production to transportation to the next manufacturer, the system monitors and obtains the raw material grade, process flow, and production environment parameters from raw materials to finished products. It determines whether the obtained raw material grade, process flow, and production environment parameters meet the contract standards. If so, the financial institution uses the settlement system to pay the upstream manufacturer the corresponding profit for the current finished product and records the corresponding outstanding payments to the downstream manufacturer. Based on this, when a downstream manufacturer supplies goods to its downstream manufacturer, it performs the corresponding settlement and obtains the corresponding profit. When contract standards are monitored across all manufacturers in the supply chain, it becomes possible to monitor the entire supply chain, including raw materials (finished products), the technological processes of each manufacturer, and processing environmental parameters. For example, if a raw material is manufactured into a finished product by one manufacturer while meeting contract standards, and then supplied as raw material to the next manufacturer, the monitoring result of its compliance with contract standards in the previous manufacturer remains valid in the next manufacturer. This process can be repeated to achieve full supply chain monitoring of contract standards. Furthermore, by ensuring compliance with contract standards, the influx of counterfeit or substandard products into the market can be significantly reduced, protecting consumer interests and lowering legal risks for manufacturers.

[0053] By integrating automated production and handling equipment, automated production, handling, and loading can be achieved, further enabling automated settlement. Subsequently, financial institutions can provide alternative loan services to manufacturers based on this settlement function. Furthermore, with reliable identification results serving as reliable credit guarantees, manufacturers do not need to provide collateral or pledges when financial institutions provide services. Due to the significantly reduced need for cash, financial institutions do not need to advance funds to financial service providers, greatly reducing their risk. This is particularly beneficial for manufacturers with closed-loop revenue and expenditure paths, allowing for rapid settlement. While ensuring low-risk financial services from financial institutions, it also enables more manufacturers facing cash shortages to access financial services. On the other hand, since the accounting involved in financial services is conducted by financial institutions, the relationship between financial services and manufacturers' creditworthiness can be more closely linked, improving the rationality and accuracy of accounting.

[0054] Especially for manufacturers with closed-loop revenue and expenditure paths, under current technology, applying for loans from financial institutions inevitably requires a large amount of written documentation for credit verification, on-site verification, and physical collateral to avoid bad debts. Based on this invention, with the monitoring system covering upstream and downstream manufacturers, and based on reliable identification results, financial institutions provide guarantees. Raw materials are supplied from upstream manufacturers to downstream manufacturers, and the use of these raw materials by downstream manufacturers is monitored and identified. Once the raw materials are used, the accounting method described in this invention can be used for bookkeeping, awaiting comprehensive settlement. It is evident that in this supply chain, financial institutions do not need to lend cash to downstream manufacturers to purchase raw materials, thus helping more manufacturers overcome funding barriers while reducing the risk for financial institutions. Furthermore, for import and export trade, due to foreign exchange controls, the review period for foreign exchange conversion is long. Based on this invention, within the coverage of the monitoring system, profits earned overseas can be offset by purchasing raw materials or other goods, directly reported as in-kind, eliminating the foreign exchange conversion step.

[0055] In practical implementation, traditional single-point identification methods can be used. From raw material delivery and production to transportation to the next manufacturer, the identity and location information of raw materials to finished products are identified and located at designated locations to achieve identity verification linked to location information. Alternatively, the peer-to-peer computing system provided by this invention can be used as a monitoring system, performing non-specific feature-based identity recognition based on collaborative computing within the peer-to-peer computing system. This invention's peer-to-peer computing system is based on collaborative computing, does not rely on single-point identification, and distributes computational functions throughout the entire system, reducing the hardware and software requirements of single-point computation, resulting in high execution efficiency and significantly improved anti-attack capabilities. The system maintains a relatively symmetrical information state between node devices, making it immune to illegal data tampering. Even if a single node device is physically compromised and its data is tampered with, the computation of the entire peer-to-peer computing system involves highly redundant and complex calculations and multi-dimensional verification. Therefore, the tampering of data from a single node device does not affect the overall computational results of the entire peer-to-peer computing system. Furthermore, it can quickly locate the faulty and tampered node device, ensuring the reliability of the overall computational results. This, in turn, resolves the conflict between data sharing and information security between departments.

[0056] 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.

[0057] 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. These targets include, but are not limited to, raw materials, additives, finished products, workers, production tools, automated production equipment, and automated handling equipment. The term "non-specific feature recognition" differs from the common understanding of "recognition" in a strict conceptual definition. Commonly, "recognition" refers to determining 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 had its identity confirmed) is itself, and not another object 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.

[0058] 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.

[0059] 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.

[0060] 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 the positioning information of the finished product.

[0061] 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).

[0062] In a peer-to-peer computing system, all events are processed synchronously, and it is not necessarily necessary to explicitly produce staged outputs 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 and automated handling equipment) respond are clearly defined. 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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:

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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".

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] When this invention is implemented indoors and outdoors, the node devices, as artificial intelligence facilities installed in public spaces, can serve as digital economy 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.

[0085] 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 consensus mechanism of the entire peer-to-peer computing system. 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 computation 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.

[0086] 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.

[0087] In this invention, the execution devices mainly include automated production equipment and automated handling equipment. These devices, acting as execution devices, are connected to 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 and automated handling equipment, further improving immunity to hijacking attacks. The automated production equipment and automated handling equipment, acting as execution devices, also connect to the node devices as access devices, submitting control requests to the peer-to-peer computing system. In this invention, the execution control commands for the automated production equipment and automated handling equipment can be considered as request commands. Responses to request commands include various 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 or a pre-deployed program or data processing model deployed on the node devices. 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, a "demand-execution" scenario. In this invention, if, based on collaborative computing, it is determined that a certain control component or a combination of control components of an automated production equipment or automated handling equipment needs to be automatically controlled based on a corresponding control demand, the current node device will send control instructions to the control component connected to it according to the calculated result data, controlling the control component to complete the control action. In this invention, the component parameters of each control component of the automated production equipment or automated handling equipment are obtained, and the control demand is determined based on the component parameters of each control component.

[0088] Based on peer-to-peer computing, the execution device can act as one of the node devices. As collaborative computing progresses, when the result data obtained by the execution device corresponds to the request command and can perform the relevant operation, the execution device completes the response to the request command. In this invention, control requirements are represented by result data; automated production equipment and automated handling equipment receive 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;

[0089] 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.

[0090] When automatic control of the components is required, the automated production equipment and automated handling equipment combine the result data received from other node equipment to calculate their own result data. Based on this result data, they control the control components on the control unit to execute the corresponding control actions. In this invention, the automated production equipment and automated handling equipment do not need to first determine whether they need to respond. Instead, they combine the result data received from other node equipment with the perception data collected by their own sensors, input it into their own data processing model, and the output result data is the control action to be performed by the control components on the control unit.

[0091] 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 and automated handling equipment, as node devices, contribute 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 and automated handling equipment are the optimal solution commands output by the node devices connected to them after collaborative calculation. This invention does not have traditional generation and transmission commands, precisely to avoid security vulnerabilities that could make automated production equipment and automated handling equipment risk points.

[0092] In this embodiment, the automated production equipment and the automated handling equipment are node devices that connect execution components with specific functions. The execution feedback information of the execution components of the automated production equipment and the automated handling equipment is fed back to the automated production equipment and the automated handling equipment, respectively, and participates in the calculation of the subsequent result data of the automated production equipment and the automated handling equipment.

[0093] In this invention, since automated production equipment and automated handling equipment can be used as node devices, their response execution is based on the calculation 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 and automated handling equipment, further improving immunity to hijacking attacks.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] In this invention, automated production equipment and automated handling equipment receive result data output from other node equipment. 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 equipment 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 equipment directly transmits the result data to the automated production equipment, automated handling equipment, or the node equipment connected to the automated production equipment and automated handling equipment. In this invention, based on preset conditions or algorithm output or model output, the corresponding automated production equipment and automated handling equipment are added to the node list for transmitting result data.

[0098] Alternatively, automated production equipment and automated handling equipment receive the result data output by other node equipment in a layer-by-layer transmission manner. During the collaborative computing process of the peer-to-peer computing system, each node equipment calculates a list of nodes that need to receive the result data. Based on the current result data, it clearly knows which one or more execution devices (mainly automated production equipment and automated handling equipment) need to be added, and these will be added to the node list. The execution device or the node device connected to it will be directly used as the next-layer node device to directly receive the current result data, achieving cross-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, if the corresponding node device is added to the node list, it can directly receive the result data of the current node device in the next layer of transmission, thus greatly shortening the processing time and improving responsiveness. This invention adopts 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.

[0099] In the event of unavoidable malfunctions, this invention incorporates the control components of automated production and handling equipment as node devices within a peer-to-peer computing system. Furthermore, if collaborative computing determines that a control component is abnormal—meaning changes occur in various stages from raw material delivery and production to transportation to the next manufacturer—the abnormal data is used as input to calculate the result data, leading to a processing solution through collaborative computing. This processing solution can be based on collaborative computing. For example, if the peer-to-peer computing system detects a malfunction in a component of the current automated handling equipment (in this system, numerous node devices perform collaborative computing to detect events; in many cases, the event detection process is unnecessary, as numerous features are incorporated into the calculation, allowing the corresponding node device's connected control component to respond), the fault-free automated handling equipment automatically moves to meet the faulty equipment, intercepting or replacing it. This achieves automatic alarm and automatic merging, significantly improving troubleshooting efficiency. When necessary, such as when automated production equipment or automated handling equipment that has a fault is detected based on collaborative computing and needs to record raw data, the current node device receives the result data transmitted by other node devices and saves it as the raw input on the raw data storage device it is connected to.

[0100] For automated production equipment and automated handling equipment, each automated production equipment and automated handling equipment is equipped with a safety mechanism in read-only storage mode. If all the node devices connected to the control component are damaged, and a safety accident is detected based on collaborative computing, the safety mechanism of the automated production equipment and automated handling equipment will be activated. According to the read-only storage preset mechanism, it will take over other control components and control the automated production equipment and automated handling equipment to slow down or stop.

[0101] Collaborative computing based on a peer-to-peer computing system utilizes result data that lacks explicit meaning. Through transmission and computation between node devices, a clear meaning is derived, enabling non-media storage and retrieval, achieving integrated storage and computation. The result data itself does not directly indicate any explicit meaning; that is, the result data itself cannot be reproduced or interpreted by any means. Instead, it is obtained during the transmission and computation process, thus eliminating the need for any data content with explicit meaning or storage. This achieves non-media storage and retrieval while simultaneously performing business logic calculations, judgments, and execution. It fundamentally changes the traditional single-point security sensitivity of information systems. The data source and computation process are reliable, with high execution efficiency, low hardware requirements, and high recognition accuracy. It achieves non-specific feature recognition of targets without requiring specific feature identification or obtaining target identity information. Furthermore, the node devices do not store the original data (unless, according to legal or management regulations, sensors can be configured for storage for traditional evidence preservation), nor do they perform point-to-point identification using the collected original data on a single node device.

[0102] Peer-to-peer computing systems do not actually store data with explicit meaning. Instead, when there is a need (including "traceability" needs (such as obtaining data with explicit meaning) or execution needs (such as operations based on obtained data with explicit meaning)), the resulting data (the perceived state of each node device) is transmitted and computed between node devices to obtain the result corresponding to the need. This achieves an effect similar to "reading" from "storage," simultaneously realizing the computation, judgment, and execution results of massive business logic. The "result" corresponding to the "traceability" need is not obtained by generating data corresponding to the "result" and storing it through a medium, but rather by computing according to the need, thus achieving the integration of storage and computation. When there is an execution need, in the process of achieving a "read" from "storage," the computation, judgment, and execution of the business logic corresponding to the execution need are completed simultaneously. Furthermore, the process of achieving a "read" from "storage" does not require outputting a "result" corresponding to data with explicit meaning; that is, the process of achieving a "read" from "storage" is seamless.

[0103] Collaborative computing enables continuous causal computation for massive logic across a vast number of node devices (the entire peer-to-peer computing system handles massive demands simultaneously). Hackers' intrusion, hijacking, or tampering with any node device will cause errors in the interactive computation between the intruded, hijacked, or tampered node device and other node devices, thereby detecting node device anomalies, activating corresponding measures for repair, or replacing faulty node devices.

[0104] The aforementioned "integrated storage and computing" no longer simply stores data with explicit meaning through storage media. Instead, it employs a technical solution of "non-media storage" and "non-media retrieval" for real-time result data computation, achieving a "reverse reasoning" effect to simulate the "reading" effect of "storage." Furthermore, it combines "storage," "reading," and business logic calculations within the same computational process, executing business logic to achieve an effect equivalent to "reading" and "storing" followed by subsequent operations. This peer-to-peer computing system fundamentally changes the traditional single-point security sensitivity of information systems. It ensures reliable data sources and computation processes, high execution efficiency, low hardware requirements, and high recognition accuracy, achieving non-specific feature recognition of targets without requiring specific feature identification or obtaining target identity information.

[0105] Specifically, regarding the definitions of "media storage" and "non-media storage," "media storage" refers to storing data (or data content) with a clear meaning through a storage medium, while "non-media storage" refers to storing data (or data content) with a clear meaning without a storage medium. "Having a clear meaning" means that a clear meaning can be determined from the data, such as user identity information, product type, product quantity, sensor-collected perception data, judgment results or recognition results obtained based on perception data, etc.; that is, the meaning corresponding to the data itself can be determined solely by the data itself, and the content that the data itself is meant to express can be known. Furthermore, "media storage" and "non-media storage" are not actually self-defined antonyms, but rather indicate a fundamental difference in technical solutions, representing completely different technical methods based on entirely different technical principles. The "non-media storage" described in this invention does not mean that no storage medium is used at all during implementation, nor that any data is stored using any storage medium. Rather, in the technical features corresponding to the effects of "storage" and "reading," data content with a clear meaning (or representing a clear result) is no longer directly stored. That is, it is not recorded on the storage medium, but the original data is not directly stored, and no simple single-point calculation is performed on the original data in advance. For example, for the number "1," the data corresponding to the number "1" is no longer recorded on the storage medium directly or after encryption. When "reading" is required, the action to be performed after "reading" the number "1" is actually directly responded to through real-time calculation. This achieves the effect of completing a "reading"-like operation within the calculation step (in the calculation step, there is no distinction between which part of the calculation step is equivalent to "reading" or equivalent to "storage") and performing subsequent operations based on the "storage" of the "reading."

[0106] After obtaining data with clear meaning by "reading" from "storage," there are usually subsequent operations, which are executed by execution devices with specific functions. These operations include outputting the "storage" that was "read," controlling the execution device based on the "storage" that was "read," and the execution device responding to and executing the "storage" that was "read," and so on. For example, displaying data with clear meaning is actually done by displaying the data with clear meaning through a display device (which is an execution device whose specific function is display). For example, controlling a projector device through a user's actions is actually done by recognizing the user's actions, obtaining corresponding control commands, and using the control commands to control the projector to perform corresponding operations. For example, controlling the opening of an access control system based on a visitor's access request and identity information is actually done by the access control system acting as an execution device, responding to the visitor's access request based on the visitor's identity information.

[0107] It is evident that the execution of business logic can infer the inevitable existence of a "reverse reasoning" effect, thus confirming that the present invention necessarily involves calculations (not necessarily output) of "non-media storage" and "non-media reading." In this invention, the "non-media storage" and "non-media reading" of "non-media storage" are forms of intermediate state data. The existence of the corresponding intermediate state data constitutes the "non-media storage" described in this invention. Obtaining a result with a clear meaning through calculation using the intermediate state data constitutes the "non-media reading" described in this invention. When subsequent operations are required based on the "storage" of "reading," the calculation, judgment, and execution of business logic typically only allow the user to perceive or know the execution result. However, "non-media storage" and "non-media reading" may not be perceived by the user, or do not require user perception, or even involve independent "storage" and "reading" processes. Furthermore, by transmitting the result data, this invention can simultaneously achieve the original data, logical judgment, and execution requirements needed for "traceability". It can achieve the "storage-reading-computation-execution-storage" effect of the existing single-point computing architecture with separate computation and storage by simply transmitting data and executing the corresponding execution device.

[0108] When achieving "computation-storage integration," the resulting data itself does not directly show, represent, or express a definite meaning; that is, the resulting data itself is not processed by other means to reproduce or interpret a definite meaning. In fact, during the implementation of this invention, the resulting data represents an intermediate, high-dimensional, mixed computational result encompassing the historical state description of the target. During transmission, there is no transformation, conversion, compilation, or encryption of the original data or data with definite meaning for the purpose of transmission. To obtain a result with definite meaning, such as confirmation of an event or information, this invention obtains it during the transmission and computation of the resulting data, thus requiring no data content with definite meaning to be formed, let alone stored. The "non-media storage" and "non-media reading" of "non-media storage" described in this invention are defined in this way, thus revealing the definition of "computation-storage integration."

[0109] Specifically, the method for transmitting result data between node devices is as follows: the current node device receives the previous result data output by the preceding node device, and calculates the current result data by combining it with the current raw data collected by the current node device, and transmits it to the subsequent node device; and so on, transmitting the result data between node devices. The result data represents the sensing state perceived by the sensors connected to the node device, the state of the node device itself, and the state of the execution device connected to the node device. Through the calculation of the result data and the connection relationship between node devices, the non-media storage of the sensing state perceived by the sensors connected to each node device, the state of each node device itself, and the state of the execution device connected to each node device is completed, and massive computing tasks are performed.

[0110] The "storage" and "retrieval" described in this invention actually involve calculating the result data, which contains a massive set of past features, to achieve an effect similar to "recalling" (i.e., "reverse deduction") historical states. This differs from single-point calculations that retrieve records from historical moments. Therefore, it does not require, and can exist, a storage medium to "store" data with clear meaning for "retrieval." The "recall" of events or information in this invention is the result of real-time calculation. All "recall" results are obtained from calculations by a massive number of node devices. The calculation results of these massive node devices at each unit of time are a common intermediate calculation, encompassing a high-dimensional hybrid calculation result that describes the historical state of the target. The business logic calculations, judgments, and executions based on the events or information confirmed by "recall" in this invention are also the result of real-time calculations, manifested in the corresponding execution of the execution device regarding the events or information.

[0111] 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 supply chain settlement method based on monitoring and identification, characterized in that, The monitoring system tracks all manufacturers within its coverage area, from raw material delivery and production to transportation to the next manufacturer. It identifies the raw materials and identifies the location of finished products to confirm the completion of raw material purchases and finished product delivery. For manufacturers that form upstream and downstream relationships in the supply chain, the settlement system uses purchase contracts and sales contracts for each purchase as receipts and payment vouchers. Based on the expenditure amount of the purchase contract and the revenue amount of the sales contract, the system offsets receipts and payments to obtain the settlement result. The settlement system sends a collection request or payment request to the manufacturer based on the settlement results; The monitoring system is a peer-to-peer computing system that uses peer-to-peer computing to perform non-specific feature recognition and location recognition of targets. The targets include raw materials, additives, finished products, workers, production tools, automated production equipment, and automated handling 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 the positioning information of the finished product.

2. The supply chain settlement method based on monitoring and identification according to claim 1, characterized in that, If several manufacturers form a closed-loop payment path based on purchasing or sales relationships, then based on the settlement results, the settlement system sends payment requests to the manufacturers that need to pay and collects the corresponding payment amount; based on the settlement results, the settlement system allocates the payment amount to the manufacturers that need to collect payment and sends collection requests to the manufacturers that need to collect payment, transferring the corresponding collection amount.

3. The supply chain settlement method based on monitoring and identification according to claim 1, characterized in that, Settlement is conducted once between the manufacturers involved in the production of each batch of finished products, based on the production cycle of each batch; or, settlement is conducted once for all manufacturers based on the time cycle.

4. The supply chain settlement method based on monitoring and identification according to claim 1, characterized in that, The production of finished products corresponding to the manufacturer is carried out through automated production equipment, and the finished products are transported and loaded onto vehicles through automated handling equipment, thus automatically completing the production, handling, and loading process from raw materials to finished products.

5. The supply chain settlement method based on monitoring and identification according to claim 1, characterized in that, The settlement system is deployed within financial institutions. It receives supply requests from downstream manufacturers in the supply chain and sends delivery requests to upstream manufacturers based on these requests. Once the monitoring system determines that the upstream manufacturer has completed delivery and that the delivery meets the contractual standards of the purchase contract, the settlement system pays the upstream manufacturer the corresponding profit using the financial institution's own funds and records the corresponding outstanding payments to the downstream manufacturers. Based on this, when a downstream manufacturer supplies goods to its downstream manufacturers, it performs the corresponding settlement and obtains the corresponding profit.

6. The supply chain settlement method based on monitoring and identification according to claim 5, characterized in that, The system analyzes and obtains the contract standards for purchase and sales contracts, including raw material grade standards, process flow standards, and production environment parameter standards. It monitors and obtains the raw material grade, process flow, and production environment parameters from raw material delivery and production to transportation to the next manufacturer at each stage. It then determines whether the obtained raw material grade, process flow, and production environment parameters meet the contract standards. If so, the financial institution uses the settlement system to pay the corresponding profit to the next-level manufacturer for the current finished product and records the corresponding outstanding payment to the next-level manufacturer.

7. The supply chain settlement method based on monitoring and identification 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.

8. The supply chain settlement method based on monitoring and identification according to claim 7, 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.

9. The supply chain settlement method based on monitoring and identification according to claim 8, 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.

10. The supply chain settlement method based on monitoring and identification according to claim 9, 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.

11. The supply chain settlement method based on monitoring and identification according to claim 10, 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.

12. The supply chain settlement method based on monitoring and identification according to claim 10, 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.

13. The supply chain settlement method based on monitoring and identification according to claim 8, 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.

14. The supply chain settlement method based on monitoring and identification according to claim 8, 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.

15. The supply chain settlement method based on monitoring and identification according to claim 14, 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.

16. The supply chain settlement method based on monitoring and identification 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.

17. The supply chain settlement method based on monitoring and identification according to claim 1, characterized in that, Each control component of automated production equipment and automated handling 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 automated production equipment or automated handling equipment needs to be automatically controlled based on the corresponding control requirements, the current node device will send control commands to the control component connected to the current node device according to the calculated result data, and control the control component to complete the control action.

18. The supply chain settlement method based on monitoring and identification according to claim 17, 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.

19. The supply chain settlement method based on monitoring and identification according to claim 18, 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.

20. The supply chain settlement method based on monitoring and identification according to claim 19, 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.

21. The supply chain settlement method based on monitoring and identification according to claim 20, 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.

22. The supply chain settlement method based on monitoring and identification according to claim 18, 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.

23. The supply chain settlement method based on monitoring and identification according to claim 1, characterized in that, The control components of automated production equipment and automated handling equipment are added as node devices to the peer-to-peer computing system. If the control component is found to be abnormal based on collaborative computing, the abnormal 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.