Multi-machine collaborative operation and task distribution system and method for indoor ground cleaning machine

Abstract

The invention relates to a multi-machine collaborative operation and task distribution system and method for indoor floor cleaning machines, and the method comprises the steps: responding to task release, and casting the information of a to-be-cleaned task into a task NFT through a task factory intelligent contract; the task NFT is published to a task market smart contract, and a plurality of cleaning robot nodes calculate bidding cost according to respective states and submit encrypted bidding; the task market intelligent contract automatically executes uncovering and verification, and awards the task NFT ownership to the cleaning robot node winning the bid; the bid-winning robot generates a PoC data packet containing state characteristics before and after cleaning before and after operation and submits the PoC data packet to a PoC verification smart contract; and the PoC verification intelligent contract performs automatic verification on the data packet, and triggers an economic incentive mechanism after the verification is passed. According to the invention, the cleaning robot cluster can realize autonomous bidding distribution of tasks, credible cleaning effect verification and self-driven operation excitation in a decentralized network without a central server.

Description

Technical Field

[0001] This invention relates to the field of intelligent robot scheduling technology, and in particular to a multi-machine collaborative operation and task allocation system and method for indoor floor cleaning machines. Background Technology

[0002] Indoor floor cleaning robots are increasingly used in large public places, and to improve cleaning efficiency, collaborative operation by multiple robots has become the mainstream development direction. Currently, the mainstream technical solution for multi-robot collaborative operation generally adopts a centralized scheduling architecture. In this architecture, a central server acts as a "command center," responsible for receiving tasks, performing global calculations, and issuing specific instructions to each cleaning robot. This model can effectively organize operations under certain conditions.

[0003] However, as the scale of applications expands, the inherent flaws of this centralized technology solution become increasingly apparent:

[0004] 1. System reliability issues: The central server is a single point of failure in the system. If it fails or the network is interrupted, the entire robot cluster will be paralyzed, unable to meet the high requirements of commercial venues for continuous cleaning services.

[0005] 2. Scalability bottleneck: When the number of robots increases significantly, the computing and communication load of the central server will increase dramatically, which can easily become a performance bottleneck and limit the scale of robot clusters that the system can manage efficiently.

[0006] 3. Task Verification and Trust Issues: In complex business models involving multiple vendors or operators, relying on a single center for task allocation and workload assessment makes it difficult to guarantee fairness and transparency. Furthermore, the lack of an objective, neutral, and tamper-proof mechanism to verify the actual cleaning effectiveness of robots creates difficulties in service quality monitoring and payment settlement.

[0007] Therefore, designing a novel system and method that does not require a central server, supports large-scale self-organizing collaboration of cleaning robot clusters, and establishes a trusted verification mechanism has become an urgent technical problem to be solved in this field. Summary of the Invention

[0008] To help solve the technical problems existing in the prior art, the present invention provides a multi-machine collaborative operation and task allocation system and method for indoor floor cleaning machines, which enables a cluster of cleaning robots to achieve autonomous bidding and allocation of tasks, reliable verification of cleaning effects, and self-driven operation incentives in a decentralized network without a central server.

[0009] This invention discloses a method for multi-machine collaborative operation and task allocation of indoor floor cleaning machines, comprising the following steps:

[0010] In response to the task publishing node calling the task factory smart contract deployed on the blockchain network, the task information to be cleaned, including location coordinates and task description, is minted into a task non-fungible token (NFT) to uniquely identify the task to be cleaned.

[0011] The task NFT is published to a task marketplace controlled by a task marketplace smart contract, and the multiple cleaning robot nodes calculate the bidding cost based on their respective local states and submit encrypted bids corresponding to the bidding cost to the task marketplace smart contract.

[0012] After the preset bidding window closes, the task marketplace smart contract automatically executes the bidding and verification process, and grants ownership of the task NFT to a winning cleaning robot node based on the bidding cost.

[0013] The winning cleaning robot node collects and generates a Proof-of-Work (PoC) data packet containing pre-cleaning and post-cleaning state features before and after performing cleaning operations, and submits the PoC data packet to the PoC verification smart contract for automated verification.

[0014] The PoC verification smart contract verifies the PoC data packet according to a preset adjudication logic, and triggers the economic incentive mechanism within the system after successful verification.

[0015] Specifically, the technical solution of this invention aims to construct a complete self-organizing workflow through a series of automated steps executed in a decentralized network. The first step of this method is to respond to a task-issuing node's call to a task factory smart contract deployed on the blockchain network, minting the task information to be cleaned, including location coordinates and a task description, into a task non-fungible token (NFT) to uniquely identify the task. This step is the starting point of the entire process. In a specific implementation, when a task-issuing node (such as a smart camera) detects an area to be cleaned, it triggers the task factory smart contract. After receiving information such as the task's location and stain type, the smart contract generates a unique, indivisible digital certificate, i.e., the task NFT, on the blockchain according to NFT standards such as ERC-721. This NFT represents the specific cleaning task, and all task attributes are embedded in its metadata. By transforming physical tasks into unique digital assets on the chain, a technical foundation is provided for subsequent tracking, transfer, and verification. The second step of this method is to publish the task NFT to a task marketplace controlled by a task marketplace smart contract. Multiple cleaning robot nodes calculate their bidding costs based on their local states and submit encrypted bids corresponding to these costs to the task marketplace smart contract. This step enables the market-based allocation of tasks. After the task NFT is minted, its ownership enters the task marketplace smart contract and is broadcast to all online cleaning robot nodes. Each robot node, upon receiving the task information, independently runs its internal cost evaluation algorithm, comprehensively considering factors such as its distance from the task location, remaining battery power, and tool compatibility to calculate a quantified cost for executing the task. To ensure fairness, the robots do not submit plaintext costs but rather encrypted bids generated through hash calculations and other methods, corresponding to the cost value, thus ensuring that all bids are kept confidential before the bidding opens. The third step of this method is for the task marketplace smart contract to automatically execute the bidding and verification process after the preset bidding window closes, and to grant ownership of the task NFT to the winning cleaning robot node based on the bidding cost. This step is the core decision-making stage for task allocation. After the bidding deadline, the task marketplace smart contract automatically executes its pre-defined logic, requiring all bidders to disclose their original bids and the random numbers used for encryption. The contract verifies the validity of each bid, then iterates through all valid bids to find the one with the lowest cost. Finally, the contract automatically executes the ownership transfer operation, transferring the task NFT representing the task execution rights from the task marketplace contract to the on-chain address of the winning cleaning robot node. The entire process is enforced by code, is transparent, and requires no human intervention.

[0016] The fourth step of this method involves the winning cleaning robot node collecting and generating a Proof-of-Work (PoC) data packet containing pre-cleaning and post-cleaning state characteristics before and after performing the cleaning operation. This PoC data packet is then submitted to the PoC verification smart contract for automated verification. This step is crucial for ensuring operation quality and establishing trust. The winning robot must perform two standardized state acquisitions of its work area at the start and end of the cleaning operation. Using its onboard sensors (such as a down-view camera), it quantifies the physical cleanliness of the ground (e.g., stain coverage) into two comparable digital feature values. These two feature values, along with key information such as the time and location of the operation, are packaged and digitally signed with the robot's unique private key to form a complete and tamper-proof Proof-of-Work (PoC) data packet, which is then submitted to the blockchain as a transaction. The fifth step of this method involves the PoC verification smart contract verifying the PoC data packet according to a preset adjudication logic. Upon successful verification, the system's economic incentive mechanism is triggered. This step marks the end of the closed-loop process for the entire task. Upon receiving the data packet, the PoC verification smart contract automatically executes a series of rigorous verifications, including verifying the authenticity of the digital signature, verifying the consistency between the work location and the task location, and, most importantly, verifying whether the post-cleaning state characteristics meet the preset cleaning standards compared to before cleaning. Only when all verification conditions are met will the contract determine that the task is complete. This "verification passed" trusted on-chain result will further trigger subsequent economic incentive contracts, automatically issuing predetermined token rewards to the cleaning robot that performed the work, thus completing a full cycle of "task-execution-verification-incentive".

[0017] According to a multi-machine collaborative operation and task allocation method for an indoor floor cleaning machine of the present invention, the step of calculating the bidding cost based on the local state of each cleaning robot node specifically includes: the cleaning robot node obtaining the path distance between its current position and the position coordinates recorded in the task NFT, its remaining power, and the matching degree between its cleaning tool and the stain type recorded in the task NFT; calculating the bidding cost through a preset cost evaluation function that includes the path distance, the remaining power, and the tool matching degree; in the cost evaluation function, the greater the path distance, the lower the remaining power, or the worse the tool matching degree, the higher the calculated bidding cost.

[0018] Understandably, this technical solution aims to ensure that each robot's bidding decision is a rational judgment based on its most critical physical state and task relevance. Specifically, by comprehensively considering three core dimensions—path distance, remaining battery power, and tool compatibility—the cost evaluation function can generate a highly personalized cost value for the robot that reflects the actual cost of the operation. Path distance is directly related to time cost and mobile energy consumption; remaining battery power determines whether the robot is capable of completing the task and returning smoothly; and tool compatibility relates to cleaning effectiveness and operational efficiency. This localized, multi-dimensional cost evaluation ensures that the final bidding results truly allocate the task to the robot with the best overall conditions, thereby achieving Pareto optimal allocation of resources for the entire robot cluster and avoiding resource misallocation caused by incomplete information or a single evaluation dimension.

[0019] According to a multi-machine collaborative operation and task allocation method for indoor floor cleaning machines of the present invention, the step of submitting an encrypted bid to the task market smart contract specifically includes: the cleaning robot node concatenating the calculated bid cost with a random number to calculate a bid hash value; the cleaning robot node submitting the bid hash value as an encrypted bid to the task market smart contract; after the bidding window is closed, the cleaning robot node then publicly submits the original bid cost and the random number to the task market smart contract for comparison and verification.

[0020] This technical solution aims to ensure that the calculation results of each cleaning robot node during bidding decisions are not affected by the information submitted by other nodes in the network, thus guaranteeing the determinism of the bidding process and the optimality of the final allocation result through a "commitment-disclosure" mechanism based on hash functions. In a distributed network, if a node submits its bid cost in plaintext, subsequent submitting nodes can obtain the data from earlier submitting nodes. This raises a technical problem: the final allocation result of the system may deviate from the globally optimal solution. Specifically, suppose the following scenario exists: Cleaning robot A has a real cost of 10 and submits its bid first. Cleaning robot B has a real cost of 12. After observing that the current lowest market price is 10, robot B's local decision-making algorithm may generate and submit a strategic bid below 10, such as 9.9, to ensure winning the bid. At this time, a cleaning robot C with a real cost of 9.5 comes online to prepare for bidding. When robot C queries the market status, it finds that the current lowest bid is 9.9 submitted by robot B. Based on this information, robot C's local algorithm may determine that there is no chance of winning the bid or that the profit margin is too low, and decide not to participate in this bidding. Ultimately, the task will be incorrectly assigned to robot B (actual cost 12, strategic bid 9.9), whose actual cost is not the lowest, while robot C (actual cost 9.5), which has the lowest actual cost in the system, will fail to participate in the competition due to misinformation. This leads to suboptimal allocation of system resources, meaning the task is not assigned to the robot that is actually capable of completing the job at the lowest cost. This solution avoids this problem by using hash encryption. During the bidding phase, robot nodes do not submit their original cost values, but rather an encrypted commitment to those original cost values. Due to the one-way nature of hash functions, no node can know the actual bids of other nodes before the bidding window closes. Therefore, the decision-making environment of all robot nodes is isolated from information; each node must and can only submit its initially calculated actual cost based on its own state and task information. This forcibly ensures that the decision-making processes of all participating nodes are independent of each other, eliminating strategic bidding behavior caused by information asymmetry. Ultimately, the lowest-cost robot selected by the smart contract is the truly globally optimal choice, thereby maximizing the collaborative efficiency of the entire robot cluster.

[0021] According to a multi-machine collaborative operation and task allocation method for an indoor floor cleaning machine of the present invention, the step of generating a proof-of-work (PoC) data packet specifically includes: the winning cleaning robot node using its downward-facing camera to capture an image of the area to be cleaned, and using an image binarization processing algorithm to quantify the percentage of dirt pixels in the image to the total pixels, and using the percentage value as the pre-cleaning state feature and the post-cleaning state feature respectively; encapsulating the pre-cleaning state feature and the post-cleaning state feature together with the position coordinates and timestamp during the scanning, and digitally signing the encapsulated data using the private key of the winning cleaning robot node to generate the PoC data packet.

[0022] This technical solution aims to provide a standardized and repeatable method to accurately convert the cleaning effects of the physical world into structured digital evidence that can be processed and verified by computer programs. The solution explicitly uses a robot's downward-facing camera to acquire images and employs image binarization algorithms to quantify stain coverage. This step provides an objective and uniform quantitative standard for cleanliness (i.e., stain pixel percentage), thus eliminating measurement inconsistencies that may be introduced by differences in sensor models or variations in ambient lighting. Simultaneously, the solution emphasizes binding the quantified state characteristics with the location coordinates and timestamp of the operation, and digitally signing them using the robot node's unique private key. The technical purpose of this is to construct a data structure with strong correlation and immutability. The digital signature ensures the authenticity of the data source and the integrity during transmission, while the binding of the spatiotemporal stamp anchors the proof of work to a unique spatiotemporal event. In this way, it technically ensures that each PoC data packet is a faithful and complete digital record of the claimed physical operation.

[0023] According to a method for multi-machine collaborative operation and task allocation of an indoor floor cleaning machine based on the present invention, the step of the PoC verification smart contract verifying the PoC data packet according to a preset adjudication logic includes: performing signature verification, spatiotemporal verification, and effect verification on the PoC data packet; wherein, the logic of the effect verification is: determining whether the post-cleaning state feature is less than the product of the pre-cleaning state feature and a preset cleaning coefficient, and determining whether the pre-cleaning state feature is greater than a preset minimum stain threshold.

[0024] This technical solution aims to solidify the acceptance criteria for cleaning operations into deterministic code logic, thereby achieving a fully automated, objective, and impartial verification process. The solution's adjudication logic comprises multiple layers: signature verification and spatiotemporal verification ensure the legitimacy of the submitted data packet's source and spatiotemporal attributes, forming the foundation of data trustworthiness. The core effect verification logic is implemented through two specific numerical judgments: the first judgment, "the post-cleaning state characteristics are less than the product of the pre-cleaning state characteristics and a preset cleaning coefficient," sets a clear and quantifiable threshold for the operation's effectiveness. Only when the difference in state characteristics before and after cleaning is sufficiently large is the cleaning operation deemed to have produced a valid physical change. The second judgment, "the pre-cleaning state characteristics are greater than a preset minimum stain threshold," adds a constraint as a valid prerequisite for the verification logic. This constraint prevents the system from misjudging invalid operations performed on already clean areas (i.e., those with a very low feature_before) as successful cleaning, ensuring that system resources and rewards are allocated to operations that generate actual cleaning value, guaranteeing the objectivity and consistency of each acceptance.

[0025] According to a method for multi-machine collaborative operation and task allocation of an indoor floor cleaning machine based on the present invention, the economic incentive mechanism includes: after the PoC verification smart contract is verified, the system vault smart contract is automatically triggered to transfer a number of tokens corresponding to the reward amount recorded in the task NFT metadata from its internal system tokens to the on-chain address of the winning cleaning robot node; the cleaning robot node uses the tokens it has obtained to pay the service fees required for charging or replenishing consumables to the service nodes deployed in the blockchain network.

[0026] This technical solution aims to construct a complete and self-consistent value flow loop, providing an intrinsic, rule-based driving force for the continuous operation of the robot swarm. The solution defines two key automated processes: first, reward generation and distribution. When the Proof-of-Concept (PoC) verification is successful, the system's treasury contract automatically transfers a predetermined number of tokens to the robots that have completed the verification task. The technical role of this process is to directly and reliably link abstract workload with concrete, quantifiable digital rewards. Second, reward consumption and value support. Robots must use the tokens they acquire to pay service nodes such as charging stations for the energy and consumables necessary to maintain their operation. The technical role of this process is to give the tokens within the system a clear and rigid use case and purchasing power. Through this "acquisition-consumption" closed-loop design, the continuous operating capability of robot nodes is directly related to the number of tasks they successfully complete. This deterministic rule-based economic model can guide the robot swarm to spontaneously tend towards high-efficiency and high-quality task completion without external instructions, thereby achieving self-driven and sustainable operation of the entire system.

[0027] According to a method for multi-machine collaborative operation and task allocation of an indoor floor cleaning machine based on the present invention, the method further includes a fragmentation and crowdsourcing collaboration step of a task NFT: when the estimated area of ​​a task NFT exceeds a preset large task threshold, the task market smart contract allows any cleaning robot node to call the fragment claim function to claim and process a sub-area within the area corresponding to the task NFT; after receiving the claim request, the task market smart contract automatically mints a fragment task NFT corresponding to the sub-area, and grants ownership of the fragment task NFT to the cleaning robot node that initiated the claim, while updating the estimated area and reward amount of the original task NFT proportionally.

[0028] This technical solution aims to improve the system's flexibility and parallel processing efficiency in handling large-scale, complex tasks that exceed the capabilities of a single robot. When the system generates a cleaning task with an estimated area that is too large, this solution allows the task to be dynamically decomposed. Any robot node can proactively claim and execute a sub-area of ​​the task based on its current resource status (such as power and consumables). The smart contract automatically handles this decomposition and reorganization, dynamically transforming a single, high-cost large task into multiple parallel, low-cost sub-tasks. The technical effects of this mechanism are: First, it significantly lowers the task execution threshold, allowing robots that cannot handle the entire large task due to resource constraints to participate, thereby improving the overall response rate of large tasks. Second, it achieves parallelization of operations. Multiple robots can simultaneously operate in different sub-areas of the same large area, transforming the total task completion time from linear summation to parallel processing, thus multiplying the efficiency in handling sudden large-scale pollution events. Finally, it enhances the system's fault tolerance. The failure of a single robot to execute a sub-task will not cause the interruption of the entire large task; the risk is effectively decomposed and isolated.

[0029] According to a method for multi-machine collaborative operation and task allocation of an indoor floor cleaning machine based on the present invention, the method further includes a pollution hotspot prediction step based on publicly available bidding data on the blockchain: a background analysis service periodically extracts and aggregates bidding records of all tasks in the blockchain network, including information on successful and unsuccessful bids; for each geographic cell in the indoor map, a bidding heat index is calculated based on the frequency of task occurrence and average bidding participation within a specific time window; when the bidding heat index meets preset conditions of sustained high heat or sudden increase in heat, the corresponding geographic cell is identified and marked as a potential pollution hotspot, and a proactive maintenance action is triggered.

[0030] This technical solution aims to leverage publicly available data naturally accumulated during the task bidding process to add a layer of proactive intelligent maintenance capabilities to the system at minimal additional cost. The core technical logic is that each task release and bidding process can be viewed as a passive detection of the environmental state. By aggregating and analyzing all bidding data, including unsuccessful bids, across time and space, the system can calculate a "bidding heat index" for each geographic cell. This index objectively reflects the intensity and trend of cleaning demand in different regions. When the bidding heat index of a region meets the preset algorithmic conditions of "continuous high heat" or "sudden increase in heat," the system identifies it as a potential pollution hotspot. This identification technology enables the system to upgrade from a purely "post-event response" mode to a "pre-event warning" mode. After identifying hotspots, the system can automatically generate preventative patrol tasks, guiding robots to intervene early, thereby shifting cleaning resources from dealing with established and difficult-to-handle pollution to addressing earlier, more controllable pollution sources, ultimately significantly improving the overall environmental cleanliness level.

[0031] Based on the above, the present invention also discloses a multi-machine collaborative operation and task allocation system for indoor floor cleaning machines to implement the above method, comprising: multiple cleaning robot nodes, one or more task issuing nodes, and a set of smart contracts deployed on a private consortium blockchain network composed of the nodes; the smart contracts include: a task factory smart contract, used to receive a request initiated by the task issuing node and mint the information of the task to be cleaned into a task non-fungible token (NFT); a task marketplace smart contract, used to manage the auction of the task NFT, receive encrypted bids calculated and submitted by the multiple cleaning robot nodes according to their respective local states, and after the preset bidding window is closed, grant ownership of the task NFT to a winning cleaning robot node according to the bid; and a PoC verification smart contract, used to receive a proof-of-work (PoC) data packet collected and generated by the winning cleaning robot node before and after the cleaning operation, containing pre-cleaning state features and post-cleaning state features, and automatically verify the PoC data packet according to a preset adjudication logic.

[0032] According to the present invention, a multi-machine collaborative operation and task allocation system for indoor floor cleaning machines includes a smart contract further comprising: a system vault smart contract for storing utility tokens within the system, and automatically issuing a preset token reward to the winning cleaning robot node when the PoC verification smart contract passes verification; and the system further comprises a service node as a network node, which receives service fees paid by the cleaning robot node using the tokens to enable charging or replenishment of consumables.

[0033] The technical advantages of the multi-machine collaborative operation and task allocation system and method for indoor floor cleaning machines of the present invention include:

[0034] First, by running the entire cleaning robot cluster on a distributed network driven by smart contracts, the reliance on a central scheduling server is completely eliminated. This decentralized collaborative architecture fundamentally solves the single point of failure problem of system paralysis due to central server failure. Even if some cleaning robots go offline due to malfunction or network problems, the remaining robot cluster can still autonomously discover, bid for, and execute tasks, thereby significantly improving the overall operational stability and anti-interference capability of the multi-robot cleaning system.

[0035] Secondly, task allocation adopts a market-based model where all robots bid openly. Each cleaning robot becomes an independent decision-making unit, autonomously evaluating and participating in the bidding, while the allocation decision is automatically completed by efficient smart contracts. This avoids the performance bottleneck caused by the central server needing to process massive amounts of status information and complex global calculations when the number of robots is large. Therefore, the scale of the cleaning system can be smoothly expanded by flexibly increasing or decreasing the number of robots as needed, exhibiting excellent cluster scalability.

[0036] Furthermore, the introduced "Proof of Cleaning Work (PoC)" mechanism requires robots to submit work reports containing quantitative comparison data before and after cleaning. These reports are then subject to objective and neutral automated verification by smart contracts, providing indisputable technical proof of the quality of cleaning operations. This data- and code-based trust mechanism effectively solves the problem of fairly and transparently monitoring and evaluating workload when multiple robots, vendors, or operators are collaborating. This ensures the fairness of task allocation and enables automatic reward settlement based on work results, greatly improving the system's management efficiency and the flexibility of commercial applications. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0038] Figure 1 This is a schematic diagram of the overall architecture of the system of the present invention;

[0039] Figure 2 This is a flowchart illustrating the task lifecycle and market-based allocation process of the system of this invention;

[0040] Figure 3This is a flowchart of the proof-of-concept (PoC) verification method for the cleaning workload of the present invention.

[0041] Figure 4 This is a schematic diagram of the internal economic incentive closed loop of the system of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. The embodiments of this invention are applicable to various types of electric vehicles, hybrid vehicles, driverless vehicles, or mechatronic equipment such as robots controlled by electric drive systems.

[0043] This embodiment provides a multi-machine collaborative operation and task allocation system and method for indoor floor cleaning robots. Firstly, regarding the system itself, this system aims to solve the technical problems of high single-point failure risk and limited system scalability caused by reliance on a central server in existing technologies. By constructing a decentralized blockchain network, this system can achieve self-organized collaboration and highly reliable operation of the cleaning robot cluster. Figure 1 As shown, the system in this embodiment is built on a decentralized network. In a specific implementation, the system mainly consists of four types of node units with different functions: cleaning robot nodes, task publishing nodes, service nodes, and management nodes. These nodes together form and maintain a private consortium blockchain network, thus enabling complex collaborative operations to be completed without a central server.

[0044] Specifically, the cleaning robot node is the core execution unit of the system, and its physical entity is an indoor floor cleaning robot with autonomous movement and floor cleaning capabilities. Each cleaning robot node has independent computing, storage, and network communication capabilities, used to receive tasks, participate in bidding, and execute the final cleaning operation on the blockchain network.

[0045] Specifically, the task issuing node is the source of cleaning tasks. In terms of physical implementation, the task issuing node can be a smart camera deployed indoors with integrated image recognition capabilities, or a specific application (APP) installed on the smart mobile terminal of property management personnel. The task issuing node is responsible for detecting the area to be cleaned and encapsulating the task information before publishing it to the blockchain network.

[0046] Optionally, this system may also include service nodes. Service nodes are entities that provide support services to the cleaning robot nodes. Typical service nodes include smart charging stations that provide power replenishment for the robots or material replenishment stations that provide replacement of consumables such as cleaning fluid and roller brushes. Service nodes also participate in the system's economic cycle as independent nodes in the network.

[0047] Optionally, the system may also include a management node. The management node is responsible for network maintenance and security management. Its physical carrier is usually a high-performance server or computer terminal deployed in the background, which has special permissions to authorize network members to access the network.

[0048] Preferably, the system in this embodiment uses a private consortium blockchain built on the Hyperledger Fabric framework as its underlying network. This framework was chosen because it features high performance, modularity, and controllable permissions, meeting the low-latency requirements of real-time collaboration among multiple robots. Furthermore, to ensure stable network operation even with partial node failures, the system employs the Raft consensus algorithm. This algorithm elects a dominant node to generate new blocks and process transactions. When the dominant node fails, the remaining subordinate nodes automatically initiate a new election to quickly produce a new dominant node, ensuring the entire blockchain network can continue to operate correctly even when facing no more than (N-1) / 2 node failures (where N is the total number of consensus nodes). This design eliminates the single-point-of-failure risk of traditional centralized scheduling systems, significantly improving system reliability.

[0049] To further ensure network security and manageability, this system employs a rigorous node access control process. Specifically, each cleaning robot, during its manufacturing phase, has a unique public-private key pair and an initial digital certificate issued by the device manufacturer or system operator pre-programmed into its internal secure computing module. When a new cleaning robot first starts up and attempts to connect to the system, it broadcasts a request to join the network to any known node. This request includes the cleaning robot's initial digital certificate. Upon receiving this request, the network's management node first verifies the validity of the digital certificate, including checking the legitimacy of the issuing authority and the certificate's validity period. After successful verification, the management node invokes a member management smart contract deployed on the blockchain to record the robot's public key, unique device identifier, and other identity information into the on-chain ledger, and assigns it a unique on-chain address within the network. At this point, the cleaning robot officially obtains network node status and can participate in all subsequent on-chain interactions. This automated access control mechanism based on digital certificates and smart contracts not only ensures that only legitimate devices can join the network, but also provides the system with efficient and scalable management capabilities to handle the access of large-scale robot clusters, avoiding the tediousness and potential errors of manual configuration.

[0050] In another preferred embodiment of the present invention, to balance the immutability of the blockchain with storage efficiency, this embodiment adopts a design of separating on-chain and off-chain data storage in terms of the system's data processing strategy. Key data that determines the system state and has high-value verification requirements is stored on-chain. This on-chain data includes: the identity information of each node, metadata of the non-fungible tokens (NFTs) representing the cleaning task, bidding records of all nodes, the final bidding results of the task, the status markers of task completion, token transaction records used for system incentives, and the final result hash value of the "Proof-of-Work (PoC) for cleaning," which will be detailed later. Because of their small size and high value, storing this data on-chain ensures its transparency, immutability, and traceability.

[0051] Meanwhile, for large volumes of raw data used for verification, off-chain storage is employed. This off-chain data primarily includes high-resolution raw images taken by the cleaning robot before and after performing its tasks, point cloud data from LiDAR, or detailed time-series readings from dirt sensors. In this embodiment, this off-chain data is stored in an IPFS (InterPlanetary File System) network. IPFS is a peer-to-peer distributed file system capable of generating a unique hash address (CID) for each data item based on its content. After the robot generates this raw data, it uploads it to the IPFS network and obtains a corresponding CID. Subsequently, the robot only needs to record this short CID as a data index on the blockchain. When the raw data needs to be verified, any authorized node can retrieve and verify it from the IPFS network using the on-chain CID. This separate storage strategy significantly reduces the storage burden on the main blockchain, ensuring the efficient operation of the blockchain network. Simultaneously, content addressing ensures a strong correlation and tamper-proof nature between off-chain data and on-chain records, providing a practical technical guarantee for achieving large-scale collaborative operations of robot clusters.

[0052] Through the collaborative design of the overall system architecture, network construction method, node access control process, and data hierarchical storage strategy described above, this embodiment successfully constructs a robust, secure, and scalable decentralized underlying platform. This platform eliminates dependence on a central server, enabling the entire cleaning robot cluster to maintain its core task allocation and collaboration capabilities even if some robots malfunction or the network experiences partial outages in practical industrial applications, demonstrating reliability far exceeding that of traditional centralized systems.

[0053] like Figure 2 As shown, after constructing the aforementioned decentralized blockchain network platform, this invention further includes an innovative method for task lifecycle management and market-based allocation. This method transforms abstract cleaning tasks into tradable digital assets and allocates them through a fully automated smart contract market, thereby solving the technical problems of inefficiency, unfair allocation, and slow system response as the system scales up in traditional scheduling methods.

[0054] In this embodiment, the entire lifecycle of a cleaning task, from its discovery to its final assignment, is managed through the blockchain. The core of this process lies in the task tokenization creation process. When a task publishing node detects an area to be cleaned using its built-in visual analysis algorithm, it does not report to any central server but interacts directly with a task factory smart contract deployed on the blockchain. The function of this task factory smart contract is to mint the task information into a non-fungible token (NFT) conforming to the ERC-721 standard, based on preset standards.

[0055] Specifically, the task publishing node first collects and formats the task's key information. This key information forms a standardized task NFT metadata structure, organized in JSON format, and includes the following key fields:

[0056] task_id, a unique task identifier across the entire network;

[0057] location_coordinates: The precise coordinates and range of the area to be cleaned on the indoor map;

[0058] stain_type, the type of stain determined by image recognition;

[0059] estimated_area, the estimated area covered by the stain;

[0060] priority_level, the priority level of the task;

[0061] deadline_timestamp, the latest timestamp at which the task must be completed;

[0062] reward_amount is the number of internal tokens that can be obtained after completing this task.

[0063] Furthermore, the task publishing node packages the aforementioned JSON-formatted metadata and calls the `createTask()` function in the task factory smart contract. Once triggered, this function generates a new, unique task NFT on the blockchain, recording the aforementioned metadata as an attribute of the NFT. After minting, ownership of the task NFT is automatically transferred to another core smart contract—the task marketplace smart contract—and a "new task available" event is broadcast to the entire blockchain network. By tokenizing each cleaning task into a unique NFT, this system endows tasks with clear, verifiable, and transferable digital asset attributes, which is the technical prerequisite for subsequent market-based and automated allocation.

[0064] Subsequently, the task NFT enters the "Task Marketplace" smart contract, thus initiating a smart contract-based task auction market mechanism. This market is a bidding platform that is entirely driven by code logic and is fair and open to all robot nodes. All idle cleaning robot nodes continuously listen for "new task available" events in the network. When a new task is detected, each robot immediately starts a cost evaluation algorithm locally to calculate its overall cost of performing the task.

[0065] In a preferred embodiment, the cost evaluation function is designed as follows:

[0066] ;

[0067] In this function, Distance is the path distance from the robot's current position to the task target point, obtained through its own SLAM (Simultaneous Localization and Mapping) system. That is the robot's maximum cruising speed. It is the estimated energy consumption calculated based on the estimated area of ​​the task and the type of stain. This is the robot's current remaining battery percentage. This is a penalty term; if the cleaning tool currently equipped by the robot is not suitable for handling the `stain_type` of the task, this term is a preset large positive value; otherwise, it is zero. `w1`, `w2`, and `w3` are weighting coefficients used to adjust the proportion of distance, energy consumption, and tool matching in the total cost. In this embodiment, the weighting coefficients can be configured according to the operational strategy. A typical configuration is `w1=0.5`, `w2=0.3`, and `w3=0.2`, which prioritizes selecting the nearest robot to ensure rapid response. For estimated energy consumption... It can be further specified by a linear function:

[0068] = k1 * estimated_area + k2;

[0069] Where k1 is the energy consumption coefficient per unit area related to the type of stain, and k2 is the fixed base energy consumption for robot start-up and shutdown. This localized cost assessment algorithm enables each robot to make bidding decisions based on its actual condition and capabilities.

[0070] To ensure fairness in the bidding process, this system employs a sealed bidding and tendering process. Specifically, during the bidding phase, after calculating the Cost value, the robot generates a random number (nonce), then concatenates the Cost value and the nonce, and calculates its hash value H_bid = Hash(Cost, nonce). Subsequently, the robot calls the submitBid(task_id, H_bid) function of the "task market contract" to submit only this hash value to the blockchain. Due to the one-way nature of hash functions, other nodes cannot deduce the original Cost value from H_bid, thus ensuring the privacy of the bidding process.

[0071] Once the pre-set bidding window of the "Task Market Contract" closes, the system automatically enters the bidding reveal phase. At this time, all participating bots must call the contract's `revealBid(task_id, Cost, nonce)` function to publicly submit their original Cost value and nonce, which they previously used to calculate the hash, onto the blockchain. The smart contract immediately verifies this internally, recalculating `Hash(Cost, nonce)` and comparing it with the `H_bid` submitted by the bot during the bidding phase. Only if both are completely identical is the bid considered valid.

[0072] Finally, after all bids have been revealed and verified, the task marketplace contract automatically executes its internal winner selection logic. This logic is encapsulated in the `selectWinner(task_id)` function, which iterates through all confirmed valid bids and finds the robot address with the lowest Cost value. Subsequently, the contract automatically transfers ownership of the task NFT corresponding to `task_id` from the task marketplace contract itself to the winning robot's on-chain address and records the bidding result on-chain, including the winner ID and the final transaction Cost value.

[0073] The entire task allocation process, from the release of task NFTs and the robots' autonomous evaluation and cryptographic bidding to the automatic bidding and adjudication by smart contracts, is executed automatically on the blockchain without any human intervention. This market-based allocation method ensures that tasks are always assigned to the robot with the best overall cost, achieving Pareto optimal resource allocation for the entire robot cluster. More importantly, because all rules are executed by immutable smart contract code, the potential for human bias or scheduling delays in centralized scheduling is eliminated. As the number of robots increases, the system's overall task processing capacity also increases linearly, because each robot is an independent decision-making unit, eliminating central performance bottlenecks and thus solving the system's scalability problem.

[0074] After completing task allocation through the aforementioned market-based mechanism, this embodiment further provides an innovative "Proof of Clean (PoC)" verification method. Combined with... Figure 3 As shown, this method aims to address the core technical challenge of lacking objective and reliable mechanisms to verify the effectiveness of physical world operations in decentralized collaborative systems. By quantifying the physical cleanliness state into verifiable digital features on the blockchain, a fully automated and reliable task completion verification closed loop is constructed.

[0075] In this embodiment, the core of the PoC verification method lies in providing smart contracts with a means to process and judge the effectiveness of physical cleaning. This is achieved through a standardized process of cleaning status feature extraction, PoC data packet generation and submission, and automated on-chain smart contract adjudication.

[0076] First, to enable smart contracts to quantify and evaluate cleaning effectiveness, this system defines a standard method for extracting cleaning status features. After the winning cleaning robot arrives at the designated task area, it performs a pre-cleaning status scan before starting the cleaning operation. In one specific implementation, this scanning process utilizes a downward-facing camera mounted on the robot's chassis. The robot captures a grayscale image covering the task area. Subsequently, its internal image processing unit performs Otsu's method binarization on the image. Otsu's method is an adaptive thresholding algorithm that automatically finds an optimal threshold based on the image's grayscale histogram, dividing the image into foreground (stains) and background (clean floor). After processing, stained areas in the image are represented as black pixels, while clean floors are represented as white pixels. Next, the processing unit calculates the percentage of black pixels out of the total number of pixels in the image and defines this percentage as Stain_Percentage. This Stain_Percentage value is a quantified numerical feature of the current floor cleanliness status. After the cleaning operation is completed, the robot performs the exact same scanning and calculation process on the same area again to obtain a Stain_Percentage value after cleaning.

[0077] To ensure the credibility of the proof-of-work data, this system employs a standard PoC data packet generation and submission mechanism. The robot not only needs to record state feature values ​​before and after cleaning, but also must strongly bind these features with spatiotemporal information. Specifically, after completing two state scans before and after cleaning, the robot generates a structured PoC data packet. This packet contains the following fields: task_id (ID of the task being performed); robot_id (ID of the robot performing the scan); feature_before (Stain_Percentage value before cleaning); feature_after (Stain_Percentage value after cleaning); location_stamp (precise location coordinates provided by the robot's SLAM system during the scan); and timestamp (current timestamp obtained from a trusted network time source).

[0078] Furthermore, after generating this data packet, the robot uses its unique private key, stored in the security module, to digitally sign the entire data packet, generating a signature field which is appended to the end of the packet. This signature ensures the integrity of the data packet (i.e., the data has not been tampered with during transmission) and non-repudiation (i.e., the data packet was indeed generated and submitted by the robot corresponding to that robot_id). After encapsulation, the robot submits this complete PoC data packet as a transaction to the blockchain network.

[0079] Subsequently, the data packet is uploaded to the blockchain, and its verification is automatically executed by a specially deployed "PoC verification" smart contract. This smart contract internally contains a rigorous set of adjudication logic, requiring no manual intervention. When the smart contract receives a PoC data packet, its internal verifyPoC() function performs the following verification steps in sequence:

[0080] The first step is signature verification. The contract first extracts the robot_id from the data packet and retrieves the public key corresponding to that ID from the on-chain member management contract. Then, it uses this public key to decrypt and verify the signature of the data packet. If the verification fails, it means there is a problem with the origin or integrity of the data packet, and the contract will directly reject this proof and record a verification failure event.

[0081] The second step is spatiotemporal verification. After successful signature verification, the contract reads the `location_stamp` field from the data packet and compares it with the `location_coordinates` recorded in the task NFT metadata corresponding to that `task_id`. Verification can only continue if the coordinates of the `location_stamp` fall within the geographical area specified by the task. This step ensures that the proof-of-work matches the physical location specified by the task.

[0082] The third step is effect verification. This is the most crucial step, where the contract logically judges the state feature values ​​before and after cleaning. In a preferred embodiment of the invention, the judgment conditions are: the value of feature_after must be less than the product of feature_before and a preset cleaning coefficient (e.g., 0.05), and the value of feature_before must be greater than a preset minimum stain threshold. The former condition ensures that the cleaning effect meets a significant standard, while the latter condition adds a valid operational prerequisite constraint to the verification logic. Preferably, the minimum stain threshold is set to 1%. This value takes into account the background noise of the sensor under different lighting and floor materials, aiming to effectively filter out misjudgments caused by visual noise or inherent floor texture, ensuring that only cleaning work performed in areas where stains actually exist is considered effective.

[0083] The verifyPoC() function returns "TRUE" only when all the above verification steps pass, and associates this result with task_id, writing it as a permanent record into the blockchain ledger. Conversely, if any verification step fails, the function will return "FALSE".

[0084] Through this complete "cleaning workload proof" method, this invention successfully transforms a physical world task that is difficult to standardize into an objective digital proof process that can be automatically adjudicated by code. The introduction of this mechanism establishes a solid trust foundation for the entire decentralized system, making subsequent automatic reward distribution or penalty execution verifiable, thereby solving the trust problem in multi-party collaboration and contributing to the realization of an autonomous, self-settling robot service ecosystem.

[0085] like Figure 4 As shown, based on the aforementioned automated task allocation and reliable workload verification mechanism, this embodiment further designs an internal economic incentive and system self-driving method. This method constructs a complete economic closed loop by introducing an internal utility token. This design aims to provide an intrinsic driving force for the continuous and efficient operation of the robot swarm, enabling the entire system to achieve the long-term goal of self-sustaining and self-developing, thus solving the fundamental problems of lacking incentive mechanisms or relying on manual settlement in traditional systems.

[0086] In this embodiment, the core of the economic incentive is the design and issuance of the system token. This token is a digital asset that conforms to the ERC-20 standard, and is named "CLEAN-Token" in this embodiment. ERC-20 is a widely used technical standard that ensures that the token has a unified interface and behavioral specification in terms of issuance, transfer, and query, facilitating interaction between nodes within the system.

[0087] Preferably, regarding the issuance mechanism, to ensure the stability and predictability of the system's economic model, this system adopts a one-time total issuance model. During the initial deployment of the blockchain network, a smart contract at a specific address, namely the "System Vault" contract, is triggered and mints a fixed total amount of CLEAN-Tokens in one go. This total amount is pre-set and permanently recorded in the genesis block, and cannot be increased. This design ensures that the tokens will not experience unlimited inflation, providing a foundation for their value stability. All newly minted tokens are initially held by the "System Vault" contract, awaiting subsequent distribution according to the rules.

[0088] After the token is issued, its value and utility are embodied in a carefully designed closed-loop process of acquisition and consumption. This process ensures the continuous and orderly circulation of the token within the system's ecosystem, thereby driving the behavior of each node.

[0089] Specifically, tokens are primarily acquired by successfully completing cleaning tasks. This system employs an automated reward distribution process. A trigger-linked relationship exists between the aforementioned "PoC Verification" smart contract and the "System Vault" contract. When the "PoC Verification" contract completes all verification steps for a PoC data packet with a given task_id and ultimately returns a "TRUE" result, it immediately triggers the `releaseReward()` function in the "System Vault" contract. This function reads the `reward_amount` field from the task NFT metadata associated with that task_id and then automatically executes a token transfer operation, transferring the corresponding number of CLEAN-Tokens from the address of the "System Vault" contract to the on-chain address of the cleaning robot node that completed the task. The entire reward distribution process is fully automated, with instant payment, and each transaction is recorded immutably on the blockchain, ensuring the fairness, transparency, and timeliness of the rewards.

[0090] Meanwhile, the consumption of tokens is a key aspect supporting its value, primarily manifested in the service payment process. Cleaning robots inevitably consume electricity and physical consumables during operation, requiring interaction with service nodes. In this embodiment, when a cleaning robot node's remaining power falls below a preset threshold, it automatically plans a path to the nearest service node (i.e., a smart charging station). Upon arrival, the robot initiates a "charging request" on-chain transaction to the charging station node's on-chain address. After receiving this request, the charging station node calculates the total amount of CLEAN-Tokens required for the charging service based on the estimated charging amount and the preset unit electricity price, and returns this price information to the robot. Subsequently, the cleaning robot node initiates a CLEAN-Token transfer transaction of the corresponding amount from its own address to the charging station node's address. The charging station node, by monitoring the blockchain, activates its physical control system once the transfer transaction is successfully recorded on-chain, beginning to charge the robot. Similarly, the exact same service payment process is followed when adding cleaning fluid or replacing the roller brush at the consumables replenishment station.

[0091] Through this closed-loop process of "work-earn-consume-work again," CLEAN-Token becomes the core element driving the system's operation. Cleaning robots have a continuous intrinsic motivation to actively participate in task bidding and complete tasks with high quality in order to obtain tokens for charging and maintenance. Service nodes, on the other hand, earn tokens by providing services, maintaining their own operations. This self-driven mechanism based on deterministic rules allows the entire robot swarm to spontaneously maintain high activity and efficiency without external coercive commands, achieving self-management and sustainable operation of the system.

[0092] Furthermore, in another optional embodiment of the invention, the system may introduce a task staking mechanism to enhance the reliability of task execution. For some high-priority or high-reward tasks, the "task market contract" may require participating robots to stake a certain amount of CLEAN-Tokens first. If the robot wins the bid and successfully completes the task, the staked tokens will be fully returned; however, if it fails to complete the task before the deadline_timestamp, or if the submitted PoC verification fails, the contract will automatically forfeit some or all of its staked tokens. This design further strengthens the economic constraints on robot behavior, ensuring the completion rate of critical tasks.

[0093] In summary, the internal economic incentive and self-driving method of this invention, by combining token economics with robot physical operations, successfully constructs a decentralized collaborative ecosystem capable of self-regulation and self-motivation. This not only fundamentally improves the automation level and management efficiency of multi-robot systems, but also provides a solid and feasible technical paradigm for building larger-scale and more complex cross-entity robot service networks in the future.

[0094] Furthermore, to enhance the system's collaborative processing capabilities in handling large-scale or complex pollution situations, this invention, based on the aforementioned task market mechanism, also provides a method for fragmenting and crowdsourcing collaboration of task NFTs. This method aims to address the technical challenge of response delays or even a lack of bidders for large-scale tasks due to exceeding the capabilities of a single robot or excessive costs. By introducing an automated task decomposition and reorganization mechanism, this method transforms a large task into multiple easily manageable sub-tasks, thereby achieving decentralized crowdsourcing solutions for complex tasks and significantly enhancing the system's task processing flexibility and overall operational efficiency.

[0095] Specifically, the core of this method lies in extending the functionality of the "Task Marketplace" smart contract, giving it the ability to dynamically fragment task NFTs under specific conditions. In a preferred embodiment, when the "Task Factory" smart contract creates a task NFT, in addition to regular metadata, a boolean field `is_fragmentable` (fragmentable) and a numeric field `min_fragment_area` (minimum fragment area) are added. When the estimated_area (estimated area) of a task exceeds a preset "large task threshold," its `is_fragmentable` field is automatically set to TRUE.

[0096] When a task NFT with `is_fragmentable` set to `TRUE` enters the "Task Marketplace," its auction mechanism changes. In addition to allowing bots to bid on the entire task NFT, the contract also provides a `claimFragment()` function. Any idle bot can call this function, passing in one parameter: the area `claimed_area` it wishes to claim and process. Preferably, this `claimed_area` must be greater than or equal to `min_fragment_area` defined in the task NFT's metadata.

[0097] When a bot calls the `claimFragment()` function, the "Task Marketplace Contract" immediately executes a series of automated operations. First, the contract uses a preset region segmentation algorithm (such as rectangular cutting or grid partitioning based on geometric center) to segment a region with an area equal to the original task NFT's `location_coordinates`. The sub-region. Then, the contract performs an "atomic casting and update" operation. This operation consists of two steps and is designed to be completed in a single transaction to ensure data consistency:

[0098] The first step is to create the Fragment Task: The contract calls the "Task Factory" contract to instantly generate a new, independent task NFT, called the "Fragment Task NFT." The location_coordinates of this Fragment Task NFT are the sub-regions that were just segmented, and its estimated_area equals its claimed_area. Its reward_amount is calculated proportionally, i.e.:

[0099] original reward_amount * ((claimed_area) / (original estimated_area))

[0100] Meanwhile, this new fragment task NFT inherits other attributes such as the original task's stain_type and priority_level, and directly grants ownership to the bot that initiated the claim.

[0101] The second step is to update the original task: the contract synchronously updates the metadata of the original large task NFT. Its estimated_area is subtracted from claimed_area, and its reward_amount is also subtracted from the corresponding allocated portion. Its location_coordinates is then updated to the remaining unclaimed areas.

[0102] In this way, a large task is partially claimed by a bot. This bot can immediately proceed to execute this moderately sized fragment task and submit a Proof of Concept (PoC) according to the standard process to receive the reward. The original large task NFT, as a "remaining task" with reduced area and reward, remains in the "task market," awaiting the next bot to claim the next fragment, or for a bot to bid on the entire remaining area. This process can continue until the estimated_area of ​​the original task NFT is less than min_(fragment_area), at which point its is_fragmentable field automatically becomes FALSE, and the remaining portion can only be bid on as a whole.

[0103] This fragmented and crowdsourcing collaboration mechanism can produce the following beneficial technical effects:

[0104] First, this mechanism significantly lowers the barrier for robots to participate in large-scale tasks. A robot that cannot undertake the entire large task due to insufficient power, limited consumables, or long distance can now selectively complete a small portion within its capabilities. This allows large tasks that might otherwise be ignored to be quickly responded to and completed collaboratively by multiple robots, effectively resolving the deadlock of resource and task mismatch.

[0105] Secondly, this mechanism enables parallel operation and significantly increases efficiency. In the traditional model, a large task can only be completed linearly by a single robot. However, under this mechanism, multiple robots can simultaneously claim and enter different sub-areas of the same large contaminated area from different directions to perform operations, achieving true parallel cleaning. This changes the total time required to complete the entire large task from depending on the speed of a single robot to depending on the number of robots participating in the collaboration, thereby significantly improving response speed and processing efficiency when dealing with sudden large-scale contamination events.

[0106] Finally, this design enhances the system's robustness and fault tolerance. Even if a robot that has claimed a fragment malfunctions during execution, only that small sub-area is affected. The fragment task can be reissued by the system without affecting other robots' cleaning work on other sub-areas. The progress of the entire large task will not be completely halted by a single point of failure. This approach of dynamically decoupling a macro-level task into multiple independent micro-level tasks at the execution level disperses risk and ensures that the system can robustly advance towards its overall goal even in the face of uncertainty.

[0107] In summary, the fragmentation and crowdsourcing collaboration method of task NFTs, by introducing a decomposition and reorganization logic at the smart contract level, transforms large and complex physical world tasks into a distributed process that can be completed in parallel, asynchronously, and collaboratively by multiple independent entities. This is an important technical achievement of the present invention in improving the collaborative capabilities of multiple robots and the resilience of the system.

[0108] In another preferred embodiment of the present invention, in addition to implementing the aforementioned decentralized task allocation, verification, and incentive mechanisms, a method for predicting pollution hotspots based on publicly available on-chain bidding data is further provided. This method aims to utilize information on "failed bids," typically considered invalid, accumulated in the blockchain network, to achieve low-cost, high-efficiency prediction of potential pollution hotspots in indoor environments through data mining and spatiotemporal correlation analysis. This method upgrades the system from a purely passive, reactive cleaning system to an intelligent system with proactive sensing and forward-looking maintenance capabilities, thereby intervening before problems occur and significantly improving the overall cleanliness and resource utilization efficiency of the environment.

[0109] Specifically, the implementation of this method relies on secondary analysis of the data recorded by the task market smart contract. In the aforementioned embodiments, to ensure the fairness of the bidding process, the smart contract records all valid bidding information after the bidding is announced, including both winning and unsuccessful bids. In traditional applications, only winning bids are considered valuable, while a large number of unsuccessful bids are ignored. The core of this method is to reuse this ignored unsuccessful bid information.

[0110] In one specific implementation, the executor of this method can be a backend analytics service with read access to the blockchain ledger, typically deployed on a management node. This service periodically and automatically executes the following data processing flow:

[0111] First, the analytics service extracts complete data on all task auction events that occurred within a specific time window from the blockchain ledger. For each task auction event, the service not only focuses on the winning bid, but also extracts all valid bid records associated with that task NFT, including the ID of each bidding bot, its submitted bid cost, and the location coordinates and timestamp of the task NFT.

[0112] Secondly, the service performs spatiotemporal aggregation processing on the extracted data. This step rasterizes the indoor map, dividing it into multiple equally sized geographic cells. Then, the service iterates through all bidding records and maps each bidding action to its corresponding geographic cell and time period. Based on this, the service calculates a quantified "Bidding Heat Index" (BHI) for each geographic cell.

[0113] Preferably, for geographic cell i and time window t, its bidding popularity index BHI(i, t) is calculated using the following formula:

[0114] ;

[0115] in, It represents the total number of newly generated task NFTs in cell i within this time window, directly reflecting the frequency of task occurrence.

[0116] This represents the average number of valid bids for all tasks in cell i within that time window, quantifying the level of bidding participation attracted by each task. α and β are configurable weighting coefficients that satisfy α + β = 1, used to balance the importance of task frequency and bidding participation. A typical configuration is α = 0.4 and β = 0.6.

[0117] Next, the system introduces a dynamic hotspot identification algorithm. This algorithm continuously tracks the time-series changes of the "bidding popularity index" of each geographic cell. In this embodiment, a cell i is identified as a "potential contamination hotspot" at the current time point if it meets one of the following two conditions:

[0118] The first condition is the sustained high popularity condition, which is determined by the following logic: within the past k consecutive time windows, the average bidding popularity index of this cell has consistently exceeded a preset high popularity threshold, Threshold_High. This condition is mainly used to identify long-term, regular sources of pollution.

[0119] The second condition is the surge in popularity condition, which is determined by the following logic: the growth rate of the bidding popularity index in the current time window compared to the previous time window exceeds a preset surge threshold, Threshold_Spike. This condition focuses on capturing contamination caused by sudden events.

[0120] Once a geographic cell is identified and marked as a potential pollution hotspot using the aforementioned algorithm, the system triggers a series of proactive maintenance actions. Optionally, the analytics service can automatically invoke the Task Factory smart contract to generate a special, low-reward preventative patrol task NFT for the hotspot area. This task does not require the robot to perform vigorous cleaning but rather guides it to patrol the area in advance, using its sensors to monitor environmental conditions so that pollution can be addressed before it truly scales up. Alternatively, the system can update the hotspot information to a shared, dynamic risk map, where all robots are algorithmically guided during autonomous patrol path planning, increasing the frequency of visits to these hotspot areas.

[0121] This method of using information from failed bids to predict hotspots can produce the following beneficial technical effects:

[0122] First, it achieves extremely low prediction costs. The data required by this method is entirely endogenous to the normal operation of the system. Every bid made by each robot, regardless of success or failure, objectively becomes an information probe of the environmental state. The system does not require the deployment of expensive additional sensor networks; it achieves high-value prediction functions simply by mining this existing on-chain data, demonstrating extremely high information utilization efficiency.

[0123] Second, this method has a high degree of dynamic adaptability. The flow and activity patterns of people in indoor environments are dynamic and changing, and pollution sources may also migrate accordingly. The predictive model based on bidding intensity can reflect the spatial shift of the robot swarm's focus in real time, thereby dynamically and accurately tracking changes in pollution patterns.

[0124] Third, it improves the overall cleaning efficiency of the system. By shifting from post-treatment to pre-emptive warning, the system diverts a significant amount of cleaning resources from dealing with large-scale, difficult-to-clean contamination that has already formed, to addressing smaller, easier-to-clean contamination in its early stages. This not only reduces the cost and difficulty of each cleaning session, but more importantly, it maintains the average cleanliness level of the entire environment at a higher baseline.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for multi-machine collaborative operation and task allocation of indoor floor cleaning machines, characterized in that, Includes the following steps: In response to the task publishing node calling the task factory smart contract deployed on the blockchain network, the task information to be cleaned, including location coordinates and task description, is minted into a task non-fungible token (NFT) to uniquely identify the task to be cleaned. The task NFT is published to a task marketplace controlled by a task marketplace smart contract, and the multiple cleaning robot nodes calculate the bidding cost based on their respective local states and submit encrypted bids corresponding to the bidding cost to the task marketplace smart contract. After the preset bidding window closes, the task marketplace smart contract automatically executes the bidding and verification process, and grants ownership of the task NFT to a winning cleaning robot node based on the bidding cost. The winning cleaning robot node collects and generates a Proof-of-Work (PoC) data packet containing pre-cleaning and post-cleaning state features before and after performing cleaning operations, and submits the PoC data packet to the PoC verification smart contract for automated verification. The PoC verification smart contract verifies the PoC data packet according to a preset adjudication logic, and triggers the economic incentive mechanism within the system after successful verification.

2. The method for multi-machine collaborative operation and task allocation of indoor floor cleaning machines according to claim 1, characterized in that, The specific steps for each cleaning robot node to calculate the bidding cost based on its local status include: The cleaning robot node obtains the path distance between its current position and the position coordinates recorded in the task NFT, its remaining power, and the matching degree between its cleaning tools and the stain type recorded in the task NFT. The bidding cost is calculated using a preset cost evaluation function that includes the path distance, the remaining battery power, and the tool matching degree. In the cost evaluation function, the greater the path distance, the lower the remaining battery power, or the worse the tool matching degree, the higher the calculated bidding cost.

3. The method for multi-machine collaborative operation and task allocation of indoor floor cleaning machines according to claim 1, characterized in that, The step of submitting an encrypted bid to the task market smart contract specifically includes: The cleaning robot node concatenates the calculated bid cost with a random number and calculates a bid hash value. The cleaning robot node submits the bid hash value as an encrypted bid to the task market smart contract; After the bidding window closes, the cleaning robot node then publicly submits the original bidding cost and the random number to the task market smart contract for comparison and verification.

4. The method for multi-machine collaborative operation and task allocation of indoor floor cleaning machines according to claim 1, characterized in that, The steps for generating the Proof-of-Work (PoC) data packet specifically include: The winning cleaning robot node uses its downward-facing camera to capture images of the area to be cleaned, and uses an image binarization algorithm to quantify the percentage of dirt pixels in the image to the total number of pixels, and uses this percentage value as the pre-cleaning state feature and the post-cleaning state feature, respectively. The pre-cleaning state features and post-cleaning state features, along with the position coordinates and timestamp during the scan, are encapsulated together. The encapsulated data is then digitally signed using the private key of the winning cleaning robot node to generate the PoC data packet.

5. The method for multi-machine collaborative operation and task allocation of indoor floor cleaning machines according to claim 1, characterized in that, The steps of the PoC verification smart contract verifying the PoC data packet according to the preset adjudication logic include: The PoC data packet is subjected to signature verification, spatiotemporal verification, and effect verification. The logic for verifying the effect is as follows: determine whether the post-cleaning state feature is less than the product of the pre-cleaning state feature and a preset cleaning coefficient, and determine whether the pre-cleaning state feature is greater than a preset minimum stain threshold.

6. The method for multi-machine collaborative operation and task allocation of indoor floor cleaning machines according to claim 1, characterized in that, The economic incentive mechanisms include: After the PoC verification smart contract is verified, the system vault smart contract is automatically triggered, and the number of tokens corresponding to the reward amount recorded in the task NFT metadata is transferred from the system internal tokens held by the vault to the on-chain address of the winning cleaning robot node. The cleaning robot node uses the token it has obtained to pay the service fees required for charging or replenishing consumables to the service nodes deployed in the blockchain network.

7. The method for multi-machine collaborative operation and task allocation of indoor floor cleaning machines according to claim 1, characterized in that, The method also includes steps for fragmenting task NFTs and crowdsourcing collaboration: When the estimated area of ​​a task NFT exceeds the preset large task threshold, the task market smart contract allows any cleaning robot node to call the claim fragment function to claim and process a sub-region within the area corresponding to the task NFT. Upon receiving a claim request, the task market smart contract automatically mints a fragment task NFT corresponding to the sub-region and grants ownership of the fragment task NFT to the cleaning robot node that initiated the claim. At the same time, it updates the estimated area and reward amount of the original task NFT proportionally.

8. The method for multi-machine collaborative operation and task allocation of indoor floor cleaning machines according to claim 1, characterized in that, The method also includes a pollution hotspot prediction step based on publicly available on-chain bidding data: A background analytics service periodically extracts and aggregates bidding records for all tasks in the blockchain network, including information on successful and unsuccessful bids. For each geographic cell in the indoor map, a bidding popularity index is calculated based on the frequency of task occurrence and average bidding participation within a specific time window. When the bidding popularity index meets the preset conditions of sustained high popularity or sudden increase in popularity, the corresponding geographic cell will be identified and marked as a potential pollution hotspot, and a proactive maintenance action will be triggered.

9. A multi-machine collaborative operation and task allocation system for an indoor floor cleaning machine used to implement the method described in any one of claims 1 to 8, characterized in that, include: Multiple cleaning robot nodes, one or more task issuing nodes, and a set of smart contracts deployed on a private consortium blockchain network consisting of said nodes; The smart contract includes: The task factory smart contract is used to receive requests initiated by the task publishing node and to mint the task information to be cleaned into a task non-fungible token (NFT). The task marketplace smart contract is used to manage the auction of the task NFT, receive encrypted bids calculated and submitted by the multiple cleaning robot nodes according to their respective local states, and after the preset bidding window closes, grant ownership of the task NFT to a winning cleaning robot node according to the bid. The PoC verification smart contract is used to receive the proof-of-work (PoC) data packet, which contains pre-cleaning state features and post-cleaning state features, collected and generated by the winning cleaning robot node before and after the cleaning operation, and to automatically verify the PoC data packet according to the preset adjudication logic.

10. The multi-machine collaborative operation and task allocation system for indoor floor cleaning machines according to claim 9, characterized in that, The smart contract also includes: The system vault smart contract is used to store the utility tokens within the system, and automatically issues a preset token reward to the winning cleaning robot node when the PoC verification smart contract passes the verification. Furthermore, the system also includes a service node as a network node, which is used to receive service fees paid by the cleaning robot node using the token, in order to enable charging or replenishment of consumables.

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