Intelligent networking distributed storage interaction system and method based on multi-cabin cooperation

Abstract

The invention discloses an intelligent networking distributed storage interaction system based on multi-cabin cooperation and a method thereof, and belongs to the technical field of camping intelligent cabin interaction, and the method comprises the following steps: taking each intelligent cabin as an independent node, initializing and accessing an intelligent networking distributed system, and enabling the nodes of the intelligent cabins to establish initial connection with other nodes in the system; each intelligent cabin collects user data, environment data and equipment state data through a built-in sensor and stores the collected data in a local storage unit in a classified mode. According to the method, the data synchronization among the nodes of the intelligent cabin is realized through a distributed database consistency algorithm, the data consistency under the condition of network delay or interruption is ensured, the data conflict and damage are prevented, and the system reliability is improved. Meanwhile, resource allocation is dynamically optimized based on an intelligent scheduling algorithm of machine learning, different load requirements are met, resources are saved, and the system efficiency and the intelligent level are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of camping smart cabin interaction, and in particular to an intelligent networked distributed storage interaction system based on multi-cabin collaboration and a method thereof. Background Art

[0002] The multi-cabin collaborative intelligent networking distributed storage interaction system in the camping smart cabin is a network system based on multiple independent smart cabins. Each smart cabin forms a linkage with other smart cabins through built-in sensors, IoT devices and communication modules. The core of this multi-cabin collaborative system is that it can collect, process and share environmental information, user data and equipment status from different cabins to form a distributed data network. Each cabin can not only operate independently, but also collaborate with each other through data synchronization and intelligent algorithms to provide a richer and more personalized user experience, such as adjusting ambient temperature, monitoring safety information, optimizing energy management, etc.

[0003] The intelligent networked distributed storage and interaction system is designed to address the storage and interaction needs of data between smart cabins. Each cabin acts as an independent storage node, responsible for storing the local data it collects, such as user preferences, usage records, environmental status, etc. These data are shared and updated between cabins through encrypted transmission, forming a distributed storage network. The system uses intelligent algorithms to efficiently manage distributed data, achieve rapid data access and synchronization, and ensure that data and services can continue to be provided in the event of partial network interruption or failure of individual cabins.

[0004] In the actual application of intelligent networked distributed storage interaction systems, although most interaction problems are predictable and common, some rare problems may have a serious impact on the system, especially at the data synchronization and collaboration level. When network delays or inter-node communication interruptions cause different nodes to store multiple versions of the same data, making it impossible for the system to determine which version is the latest or correct, this data inconsistency problem will not only lead to system decision deviations, but in severe cases may also cause data corruption or permanent loss, affecting the reliability and overall operation of the system.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide an intelligent networked distributed storage interaction system based on multi-cabin collaboration and its method, which can achieve efficient data synchronization between smart cabin nodes through a distributed database consistency algorithm, and ensure data consistency even in the case of network delays or communication interruptions, resolve multi-version data conflicts, prevent decision-making bias, data damage or loss, and improve system reliability and security. In addition, the intelligent scheduling algorithm based on machine learning dynamically optimizes resource allocation and collaborative operations by analyzing the data load, environmental status and user needs of the smart cabin, ensuring reasonable allocation of resources under high load, energy saving under low load, and self-learning and optimization capabilities, significantly improving the efficiency and intelligence level of the system to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: an intelligent networked distributed storage interaction method based on multi-cabin collaboration, comprising the following steps:

[0008] Treat each smart cabin as an independent node, initialize and connect it to the intelligent networked distributed system, and establish a preliminary connection between the smart cabin node and other nodes in the system;

[0009] Each smart cabin collects user data, environmental data, and equipment status data through built-in sensors, and stores the collected data in a local storage unit. The data is classified and prioritized through predefined algorithms to ensure that key data is processed and transmitted first in the event of network delays or interruptions.

[0010] Before transmitting data to other smart cabin nodes or central servers, advanced compression algorithms are used to compress data, and encryption algorithms are used to ensure the security of data transmission;

[0011] A distributed database consistency algorithm is used to synchronize data between smart cabins to ensure data consistency in the event of network delays or communication interruptions. When multiple versions of the same data are detected, the system resolves data conflicts through timestamps, version control, and node priority determination mechanisms to ensure that the latest and correct data is synchronized.

[0012] The storage nodes of each smart cabin are coordinated through a distributed consensus algorithm. When communication problems occur in some nodes, the final data version is determined based on the time sequence or the importance of the node when a conflict is detected through the timestamp and node priority mechanism;

[0013] According to the data analysis results of each smart cabin in the system, an intelligent scheduling algorithm based on machine learning is adopted to automatically adjust and optimize the collaborative interaction between smart cabins. By analyzing the data load, environmental status and user needs of each node, intelligent operation of multi-cabin collaboration is realized.

[0014] Preferably, when each smart cabin node is first connected to the system, it obtains a unique network identifier through the network access protocol and node management algorithm to ensure that the system can identify different smart cabins in a wide-area distribution. The initialization process also includes setting the hardware and communication configuration of each node.

[0015] Preferably, the data is classified and prioritized by a predefined algorithm to ensure that critical data is processed and transmitted first in the event of network delays or interruptions. The specific steps are as follows:

[0016] The collected data is divided into three categories: user data, environmental data and device status data, and stored in the local storage unit. Each type of data is identified according to the collection source and target function, and a classification mark is set. The formula for the classification mark is: C i =α i U+β i E+γ i ·S, where C i is the classification mark of the i-th type of data, U is the importance weight of user data, E is the importance weight of environmental data, S is the importance weight of device status data, α i , β i , γ i are the importance coefficients of each data type;

[0017] The classified data is further identified to determine the key data. The key data is judged by a threshold function. The judgment expression is: In the formula, F(x) is the evaluation function of whether data x is key data, T is the evaluation period, and f(C i , P i , L i ) is based on the data classification mark C i , priority weight P i and data loss cost L i The evaluation function, P i is the priority weight, P i is the cost of data loss, which indicates the potential risk impact of data loss on the system. When F(x) exceeds the set threshold, the data is considered critical data;

[0018] For key data, we sort them according to the priority weight to ensure priority processing in the case of network delay or interruption. The sorting algorithm uses a weighted sorting function, and the calculation expression is: R(i) = λ·P i +μ·L i +v·T i , where T i is the time sensitivity of the data, R(i) is the priority ranking value of data i, λ, μ, ν are the priority weights P respectively i, data loss cost L i And time sensitivity T i The weighting coefficient is used to adjust the priority weight P i , data loss cost L i And time sensitivity T i The impact of

[0019] After the priority sorting is completed, the system processes and transmits the data according to the priority sorting value R(i) in the case of network delay or interruption, and uses the transmission selection function to determine the data to be transmitted first. The expression of the transmission selection function is: T trans =max(R(i))where i∈{1, 2, …, n}, where T trans is the data with the highest priority for transmission under the current network status, and n is the total amount of data.

[0020] Preferably, the advanced compression algorithm is one of LZ77, Huffman coding and Brotli, and the encryption algorithm is one of AES, RSA and elliptic curve encryption algorithm.

[0021] Preferably, when multiple versions of the same data are detected, the system resolves data conflicts through timestamps, version control, and node priority determination mechanisms to ensure that the latest and correct data is synchronized. The specific steps are as follows:

[0022] When the system detects multiple versions of the same data, it first uses hash verification or unique identifiers to determine whether there is a conflict. The conflict judgment formula is: Where D conflict Indicates whether there is a conflict, H(D i ) is data D i The hash value of ref is the reference data, i.e. the expected standard version, δ(*) conflict detection function, when H(D i )≠H(D ref ), δ(*)=1, indicating a conflict. If D conflict >0, it is confirmed that there is a conflict and proceed to the next step;

[0023] For multiple versions of conflicting data detected, the latest data is determined by the timestamp, and the conflicts are resolved using the timestamp priority principle. The specific formula is: max =max(T1, T2, ..., T N ), where T max is the largest timestamp in the conflicting data, that is, the timestamp of the latest data, T j The timestamp of the jth version of data, where N is the number of conflicting versions;

[0024] If the timestamps are the same, the version control mechanism is used to further determine the correctness of the data through the version number. The version number mechanism is based on the historical records of data updates and gives priority to data with larger version numbers. The specific expression of the version number mechanism is: V max =max(V1, V2, ..., V N ), where V max is the largest version number in the conflicting data, V j is the version number of the j-th version data;

[0025] If the timestamp and version number are the same, the data priority is determined by the node priority. Each smart cabin node is assigned a different priority according to its importance in the system, and data from high-priority nodes is selected first. The node priority determination formula is: P max =max(P1, P2, ..., P M ), where P max represents the node with the highest priority, P k is the priority of the kth node, and M is the number of nodes with conflicting data;

[0026] Taking into account the timestamp, version number and node priority, a comprehensive scoring formula is used to perform the final data conflict resolution and synchronization judgment. The expression of the comprehensive scoring formula is: S j =α·T τ +β·V τ +γ·P τ , where T τ , V τ , P τ They are timestamp, version number, node priority, S j is the comprehensive score of the j-th version data, α, β, and γ are the weight coefficients of timestamp, version number, and node priority, respectively.

[0027] Preferably, the storage nodes of each smart cabin are coordinated through a distributed consistency algorithm. When communication problems occur in some nodes, the specific steps for determining the final data version to be adopted are as follows:

[0028] Step 1: The system continuously monitors the data synchronization status between the nodes of each smart cabin and records the storage update log of each node. Once it detects that some nodes cannot communicate with other nodes, it marks the node as a potential "communication loss node". In this case, it compares the data hash value or unique identifier to detect whether there are different versions of the data. When multiple nodes return different versions of the same data, the system identifies it as a data conflict.

[0029] Step 2: After detecting data conflicts, the system first sorts the conflicting data through the timestamp mechanism. When writing data stored in each node, a timestamp is attached. The timestamp represents the specific time when the data was generated or last updated. When a conflict occurs, the system first sorts the data in chronological order. The data with the later timestamp represents the latest update. The system regards the version with the largest timestamp as the latest data and gives priority to synchronizing the content of this version.

[0030] Step 3: If the timestamps of the conflicting data are the same, the system will further refer to the priority of the node. Nodes are given different priorities according to their importance or functional role in the system.

[0031] Step 4: Based on the dual judgment mechanism of timestamp and priority, a distributed consistency protocol is used to make a final decision on the conflicting data.

[0032] Preferably, when the communication problem is resolved and the lost node is back online, the system automatically triggers the data synchronization mechanism to synchronize the previously selected final version data with the data of the lost node. The system will determine the timestamp and node priority of steps 2 and 3 to ensure that the lost node receives the latest and correct data.

[0033] Preferably, according to the data analysis results of each smart cabin in the system, an intelligent scheduling algorithm based on machine learning is used to automatically adjust and optimize the collaborative interaction between smart cabins. The specific steps are as follows:

[0034] Collect various types of data from each smart cabin node, perform feature extraction, and convert raw data into feature vectors that can be used for analysis;

[0035] After completing data collection and feature extraction, the machine learning model is trained using historical data. The goal of the model is to predict the future load of the smart cabin, changes in user demand, and trends in environmental conditions.

[0036] After the system predicts the future load of each smart cabin, the scheduling algorithm will further analyze the resource utilization in the current system and make the best resource allocation decision based on the user needs and environmental status of each smart cabin;

[0037] Based on the analysis results, the system will use intelligent scheduling algorithms to make real-time adjustments to each smart cabin, dynamically adjusting the resource configuration and working status of each smart cabin according to system load, user needs and environmental changes;

[0038] After the intelligent scheduling algorithm is executed, the system will continuously monitor the operating status of each smart cabin and collect the effect data of scheduling execution. Through real-time monitoring, it can identify potential problems in the execution of scheduling strategies.

[0039] By analyzing historical data collected from each cabin through a deep learning model, the system automatically predicts user needs and environmental changes and adjusts the smart cabin in advance.

[0040] An intelligent networked distributed storage interactive system based on multi-cabin collaboration, including a node initialization module, a data collection and classification module, a data compression and encryption module, a data synchronization and conflict resolution module, a communication problem processing and version control module, and an intelligent scheduling and resource optimization module;

[0041] The node initialization module treats each smart cabin as an independent node, initializes and connects it to the intelligent networked distributed system, and establishes a preliminary connection between the smart cabin node and other nodes in the system;

[0042] Data collection and classification module: Each smart cabin collects user data, environmental data, and equipment status data through built-in sensors, and stores the collected data in a local storage unit. The data is classified and prioritized through a predefined algorithm to ensure that key data is processed and transmitted first in the event of network delays or interruptions.

[0043] The data compression and encryption module uses advanced compression algorithms to compress data before transmitting it to other smart cabin nodes or the central server, and uses encryption algorithms to ensure the security of data transmission;

[0044] The data synchronization and conflict resolution module uses a distributed database consistency algorithm to synchronize data between smart cabins to ensure data consistency in the event of network delays or communication interruptions. When multiple versions of the same data are detected, the system resolves data conflicts through timestamps, version control, and node priority determination mechanisms to ensure that the latest and correct data is synchronized;

[0045] The communication problem handling and version control module coordinates the storage nodes of each smart cabin through a distributed consistency algorithm. When communication problems occur in some nodes, the timestamp and node priority mechanism are used to determine the final data version based on the time sequence or the importance of the node when a conflict is detected;

[0046] The intelligent scheduling and resource optimization module uses a machine learning-based intelligent scheduling algorithm based on the data analysis results of each smart cabin in the system to automatically adjust and optimize the collaborative interaction between smart cabins. By analyzing the data load, environmental status and user needs of each node, it realizes intelligent operation of multi-cabin collaboration.

[0047] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0048] The present invention achieves efficient data synchronization between smart cabin nodes by adopting a distributed database consistency algorithm. Even in the event of network delays or communication interruptions, it can ensure that the same data versions stored in different nodes remain consistent. The system resolves data conflicts through timestamps, version control, and node priority determination mechanisms. It can effectively prevent decision-making deviations, data corruption, or loss caused by the existence of multiple versions of data, greatly improves the consistency of data storage and transmission, ensures that the system can still operate stably in complex network environments, and improves overall reliability and data security.

[0049] The intelligent scheduling algorithm of the present invention is based on machine learning. By analyzing the data load, environmental status and user needs of each smart cabin, it can dynamically adjust the allocation of resources and optimize collaborative operations. The intelligent scheduling mechanism can not only ensure the reasonable allocation of key resources during high-load periods, but also reduce resource consumption under low load conditions to achieve energy-saving effects. The self-learning and optimization characteristics of the algorithm enable the system to adapt to changing user needs and environmental conditions at any time, thereby improving the efficiency of multi-cabin collaborative operations, ensuring that system resources are maximized, and further enhancing the overall performance and intelligence level of the smart cabin network. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0051] Figure 1 It is a schematic diagram of the overall structure of the present invention.

[0052] Figure 2 It is a module schematic diagram of the intelligent networked distributed storage interaction system based on multi-cabin collaboration of the present invention. DETAILED DESCRIPTION

[0053] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0054] The present invention provides Figure 1 The intelligent networked distributed storage interaction method based on multi-cabin collaboration shown includes the following steps:

[0055] Treat each smart cabin as an independent node, initialize and connect it to the intelligent networked distributed system, and establish a preliminary connection between the smart cabin node and other nodes in the system;

[0056] When each smart cabin node is first connected to the system, it obtains a unique network identifier (such as MAC address or UUID) through the network access protocol and node management algorithm to ensure that the system can identify different smart cabins in a wide-area distribution. The initialization process also includes setting the hardware and communication configuration of each node, such as wireless communication frequency, encryption protocol, etc.

[0057] Each smart cabin collects user data, environmental data, and equipment status data through built-in sensors, and stores the collected data in a local storage unit. The data is classified and prioritized through predefined algorithms to ensure that key data is processed and transmitted first in the event of network delays or interruptions.

[0058] The intelligent classification algorithm performs hierarchical processing based on data types (such as temperature, humidity, user preferences, etc.) and uses dynamic priority queues to manage data flows. Among the raw data collected by the sensors, high-priority data such as security monitoring data will be stored and transmitted first, ensuring that the system can quickly process critical information even in abnormal situations. This step ensures effective data management and enables the system to maintain a stable operating state under high load.

[0059] Data is classified and prioritized through predefined algorithms to ensure that critical data is processed and transmitted first in the event of network delays or interruptions. The specific steps are as follows:

[0060] The collected data is divided into three categories: user data, environmental data, and device status data, and stored in the local storage unit. The core of data classification is to make a preliminary division based on the importance of the data type. Each type of data is identified according to the collection source and target function, and a classification mark is set. The formula for the classification mark is: C i =α i U+β i E+γ i ·S, where C i is the classification mark of the i-th type of data, U is the importance weight of user data, E is the importance weight of environmental data, S is the importance weight of device status data, α i , β i , γ i They are the importance coefficients of each data type, which are pre-defined according to system requirements;

[0061] The classified data is further identified to determine which data is key data. The key data is judged by a threshold function. The judgment expression is: In the formula, F(x) is the evaluation function of whether data x is key data, T is the evaluation period, and f(C i , P i , L i ) is based on the data classification mark C i , priority weight P i and data loss cost L i The evaluation function, P i is the priority weight, which is set according to the impact of the data on the system decision. i is the cost of data loss, which indicates the potential risk impact of data loss on the system. When F(x) exceeds the set threshold, the data is considered critical data;

[0062] For key data, we sort them according to the priority weight to ensure priority processing in the case of network delay or interruption. The sorting algorithm uses a weighted sorting function, and the calculation expression is: R(i) = λ·P i +μ·L i +v·T i , where T i is the time sensitivity of the data, that is, the time urgency with which the data needs to be transmitted or processed. The more urgent the time, the higher the priority. R(i) is the priority ranking value of data i. λ, μ, and ν are the priority weights P respectively. i , data loss cost L i and time sensitivity T i The weighting coefficient is used to adjust the priority weight P i , data loss cost L i And time sensitivity T i The impact of

[0063] After the priority sorting is completed, the system processes and transmits the data according to the priority sorting value R(i) in the case of network delay or interruption, and uses the transmission selection function to determine the data to be transmitted first. The expression of the transmission selection function is: T trans =max(R(i))where i∈{1, 2, …, n}, where T trans is the data with the highest priority for transmission under the current network status, and n is the total amount of data.

[0064] Before transmitting data to other smart cabin nodes or central servers, advanced compression algorithms are used to compress data, and encryption algorithms are used to ensure the security of data transmission;

[0065] Advanced compression algorithms are mainly used to reduce the bandwidth and storage space required for data transmission while maintaining the integrity and validity of the data. Commonly used advanced compression algorithms in distributed systems include LZ77, Huffman coding, and Brotli. LZ77 is a compression algorithm based on finding duplicate data. It achieves compression by replacing similar data sequences with pointers to previous data locations. Huffman coding achieves data compression by assigning shorter codes to high-frequency data, while assigning longer codes to low-frequency data. Brotli, as a modern compression algorithm, combines LZ77 and Huffman coding and optimizes performance, and excels in compression efficiency and speed. Advanced compression algorithms can significantly reduce the amount of data transmitted, thereby improving the overall transmission efficiency of the system.

[0066] Encryption algorithms are used to ensure that data cannot be stolen or tampered with during transmission. Common encryption algorithms include AES (Advanced Encryption Standard), RSA, and Elliptic Curve Cryptography (ECC). AES is a symmetric encryption algorithm with high encryption speed and strong security, and is widely used in scenarios that require large amounts of data encryption; RSA is an asymmetric encryption algorithm that uses public key encryption and private key decryption, and is suitable for key exchange before data transmission. ECC is a more efficient asymmetric encryption algorithm that can provide the same security with a smaller key length than RSA, and is particularly suitable for resource-constrained IoT devices. These encryption algorithms encrypt data to ensure that even if the data is intercepted during transmission, attackers cannot decipher the content, thereby ensuring the security of data transmission.

[0067] This step effectively improves data transmission efficiency and ensures data security in an insecure network environment. By selecting an appropriate compression algorithm, the amount of data can be reduced and transmission delays can be reduced. Combined with symmetric and asymmetric encryption algorithms, it ensures that data is not stolen or tampered with by a third party when transmitted in the network. At the same time, based on the type of transmission channel (such as Wi-Fi, LTE, or LoRa), the system can dynamically select appropriate compression and encryption strategies to ensure the efficiency and security of data transmission in different network environments.

[0068] A distributed database consistency algorithm is used to synchronize data between smart cabins to ensure data consistency in the event of network delays or communication interruptions. When multiple versions of the same data are detected, the system resolves data conflicts through timestamps, version control, and node priority determination mechanisms to ensure that the latest and correct data is synchronized.

[0069] When multiple versions of the same data are detected, the system resolves data conflicts through timestamps, version control, and node priority determination mechanisms to ensure that the latest and correct data is synchronized. The specific steps are as follows:

[0070] When the system detects multiple versions of the same data, it first uses hash verification or unique identifiers to determine whether there is a conflict. The conflict judgment formula is: Where D conflict Indicates whether there is a conflict, H(D i ) is data D i The hash value of ref is the reference data, i.e. the expected standard version, δ(*) conflict detection function, when H(D i )≠H(D ref ), δ(*)=1, indicating a conflict. If D conflict >0, it is confirmed that there is a conflict and proceed to the next step;

[0071] For multiple versions of conflicting data detected, first use the timestamp to determine which data is the latest. The larger the timestamp, the newer the data. The timestamp priority principle is used to resolve conflicts. The specific formula is: T max =max(T1, T2, ..., T N ), where T max is the largest timestamp in the conflicting data, that is, the timestamp of the latest data, T j The timestamp of the jth version of data, where N is the number of conflicting versions;

[0072] If the timestamps are the same, the version control mechanism is used to further determine the correctness of the data through the version number. The version number mechanism is based on the historical record of data updates. The larger the version number, the more times the data has been updated. Data with a larger version number is preferred. The specific expression of the version number mechanism is: V max =max(V1, V2, ..., V N ), where V max is the largest version number in the conflicting data, V j is the version number of the j-th version data;

[0073] If the timestamp and version number are the same, the data priority is determined by the node priority. Each smart cabin node is assigned a different priority according to its importance in the system, and data from high-priority nodes is selected first. The node priority determination formula is: P max =max(P1, P2, ..., P M ), where P max represents the node with the highest priority, P k is the priority of the kth node, and M is the number of nodes with conflicting data;

[0074] Taking into account the timestamp, version number and node priority, a comprehensive scoring formula is used to perform the final data conflict resolution and synchronization judgment. The expression of the comprehensive scoring formula is: S j =α·T τ +β·V τ +γ·P τ , where T τ , V τ , P τ They are timestamp, version number, node priority, S j is the comprehensive score of the j-th version data, α, β, and γ are the weight coefficients of timestamp, version number, and node priority, respectively, which are set according to system requirements.

[0075] Final selection score S j The lowest data is synchronized to ensure that the system synchronizes the latest, correct data from high-priority nodes.

[0076] Through the distributed consistency algorithm, the system can coordinate the storage nodes of each smart cabin to ensure data consistency even when some nodes have communication problems. The timestamp and node priority mechanism can determine the final data version to be adopted based on the chronological order or the importance of the node when a conflict is detected. In addition, in extreme cases, the system can use blockchain technology to record the history of data operations to prevent malicious tampering.

[0077] The storage nodes of each smart cabin are coordinated through a distributed consensus algorithm. When communication problems occur in some nodes, the final data version is determined based on the time sequence or the importance of the node when a conflict is detected through the timestamp and node priority mechanism;

[0078] When a node is disconnected or unavailable, the system automatically triggers the data recovery mechanism to redundantly store important data among other healthy nodes. This mechanism uses replica storage and erasure coding algorithms to ensure that even if some data is lost, it can be recovered through other replicas. After the failed node is restored, the system will synchronize the differential data generated during the recovery back to the original node through the incremental synchronization algorithm to ensure seamless data connection when the node rejoins the system.

[0079] The storage nodes of each smart cabin are coordinated through a distributed consensus algorithm. When communication problems occur in some nodes, the specific steps to determine the final data version are as follows:

[0080] Step 1: The system continuously monitors the data synchronization status between the nodes of each smart cabin and records the storage update log of each node. Once it detects that some nodes cannot communicate with other nodes, it marks the node as a potential "communication loss node". In this case, it compares the data hash value or unique identifier to detect whether there are different versions of the data. When multiple nodes return different versions of the same data, the system identifies it as a data conflict.

[0081] The key to this step is continuous monitoring and rapid detection of anomalies, ensuring that even in the event of a communication interruption, the system can detect and resolve potential problems as early as possible.

[0082] Step 2: After detecting data conflicts, the system first sorts the conflicting data through the timestamp mechanism. When writing data stored in each node, a timestamp is attached. The timestamp represents the specific time when the data was generated or last updated. When a conflict occurs, the system first sorts the data in chronological order. The data with the later timestamp represents the latest update. The system regards the version with the largest timestamp as the latest data and gives priority to synchronizing the content of this version.

[0083] This step ensures that the system can quickly resolve conflicts and select the latest valid data in most cases through a simple and efficient time sorting method.

[0084] Step 3: If the timestamps of the conflicting data are the same, the system will further refer to the priority of the node. Nodes are given different priorities according to their importance or functional role in the system.

[0085] For example, some nodes are responsible for processing critical data or performing core functions and have a higher priority, while other auxiliary nodes have a lower priority. In the event of a conflict, data from high-priority nodes is given priority, which means that even if the timestamps of the data are the same, the system will still choose the data version provided by the node with a higher priority. This mechanism ensures that when conflicts occur at key nodes, data consistency will not be affected, and important business data can be prioritized.

[0086] Step 4: Based on the dual decision mechanism of timestamp and priority, a distributed consensus protocol (such as Paxos or Raft protocol) is used to make a final decision on the conflicting data;

[0087] The core of these consistency protocols is to ensure that when communication problems occur, even if some nodes cannot work properly, most nodes in the system can still reach a consensus and jointly confirm the final correct data version. For example, the system can send confirmation requests to multiple nodes to determine the version that most nodes consider valid, and select the final synchronized data through the consensus algorithm. This step ensures that the system can still guarantee data consistency and reliability even when part of the network fails or some nodes lose connection.

[0088] Step 5: When the communication problem is resolved and the lost node is back online, the system automatically triggers the data synchronization mechanism to synchronize the previously selected final version data with the data of the lost node. The system will determine the timestamp and node priority of steps 2 and 3 to ensure that the lost node receives the latest and correct data.

[0089] At the same time, the system will also record all logs during the recovery process to ensure that the system can respond faster the next time a communication problem occurs. This step ensures that after communication is restored, all nodes in the system maintain data consistency and no longer have data splits or conflicts due to previous loss of connection or interruption.

[0090] According to the data analysis results of each smart cabin in the system, an intelligent scheduling algorithm based on machine learning is used to automatically adjust and optimize the collaborative interaction between smart cabins. By analyzing the data load, environmental status and user needs of each node, intelligent operation of multi-cabin collaboration is realized;

[0091] According to the data analysis results of each smart cabin in the system, an intelligent scheduling algorithm based on machine learning is used to automatically adjust and optimize the collaborative interaction between smart cabins. The specific steps are as follows:

[0092] Various types of data are collected from each smart cabin node, including but not limited to device status, user behavior, environmental changes, cabin load, and system resource utilization. These data are collected in real time through built-in sensors and system logs and transmitted to the central scheduling module. In order for the machine learning model to effectively process these data, the system will perform feature extraction and convert the raw data into feature vectors that can be used for analysis. For example, user behavior data may include frequency of use and operation duration, while environmental data may include temperature, humidity, etc. The extracted features must not only consider the current status, but also cover time series data to ensure that the scheduling algorithm can make predictions and adjustments based on historical data.

[0093] After completing data collection and feature extraction, the system will use historical data to train the machine learning model. The goal of the model is to predict the future load of the smart cabin, changes in user demand, and trends in environmental conditions. Commonly used machine learning models may include regression models, neural network models, or time series-based prediction algorithms (such as LSTM). These models predict the load demand of each smart cabin in the next period of time by analyzing the collected multi-dimensional data features. For example, the system can predict the user's demand for the air-conditioning system of a cabin in hot weather, so as to adjust resource allocation in advance. The trained model can be dynamically adjusted according to real-time data to ensure the accuracy and effectiveness of the prediction.

[0094] After the system predicts the future load of each smart cabin, the scheduling algorithm will further analyze the resource utilization within the current system. At this point, the system will consider factors such as computing resources, storage space, network bandwidth, etc. of each cabin, and combine the user needs and environmental conditions of each smart cabin to make the best resource allocation decision. For example, some smart cabins are under high load, while other smart cabins are under low load. The system can transfer part of the load from busy cabins to idle cabins through distributed computing or data migration. In addition, the system can also optimize the data transmission path according to the physical location and communication delay of different cabins to ensure efficient use of resources.

[0095] Based on the analysis results in the previous steps, the system will use an intelligent scheduling algorithm to make real-time adjustments to each smart cabin. The core of scheduling is to dynamically adjust the resource configuration and working status of each smart cabin according to system load, user needs and environmental changes. For example, in certain high-load situations, the system can temporarily increase computing resources or expand network bandwidth to meet user needs; in low-load periods, the system can actively reduce resource input, reduce energy consumption, and improve the overall efficiency of the system. This scheduling process can not only balance the load of each smart cabin in the system, but also optimize the collaborative interaction of the entire system by continuously adjusting the resource allocation strategy. The scheduling algorithm continuously learns through feedback loops and gradually improves the accuracy of its decisions.

[0096] After the intelligent scheduling algorithm is executed, the system will continue to monitor the operating status of each smart cabin and collect the effect data of scheduling execution. Through real-time monitoring, the system can identify potential problems in the execution of scheduling strategies, such as insufficient or over-allocation of resources. At the same time, the system will feed these monitoring results back to the machine learning model to help the model self-adjust and optimize. This feedback loop enables the scheduling algorithm to gradually improve its response speed and accuracy, so as to make more intelligent and efficient decisions in future scheduling. As the system runs longer, the optimization effect of the model will become more significant, and the intelligence level of scheduling will also increase accordingly, ultimately realizing intelligent and automated operation of multi-cabin collaboration.

[0097] By analyzing historical data collected from each cabin through deep learning models (such as LSTM, CNN, etc.), the system can automatically predict user needs and environmental changes, so as to adjust the smart cabin in advance. For example, the system can predict upcoming weather changes, optimize temperature control or adjust the operating status of equipment in advance, so as to achieve the best state of multi-cabin collaboration. The intelligent scheduling algorithm can also automatically balance traffic according to the data transmission load of each cabin to prevent data bottlenecks.

[0098] The present invention achieves efficient data synchronization between smart cabin nodes by adopting a distributed database consistency algorithm. Even in the event of network delays or communication interruptions, it can ensure that the same data versions stored in different nodes remain consistent. The system resolves data conflicts through timestamps, version control, and node priority determination mechanisms. It can effectively prevent decision-making deviations, data corruption, or loss caused by the existence of multiple versions of data, greatly improves the consistency of data storage and transmission, ensures that the system can still operate stably in complex network environments, and improves overall reliability and data security.

[0099] The intelligent scheduling algorithm of the present invention is based on machine learning. By analyzing the data load, environmental status and user needs of each smart cabin, it can dynamically adjust the allocation of resources and optimize collaborative operations. The intelligent scheduling mechanism can not only ensure the reasonable allocation of key resources during high-load periods, but also reduce resource consumption under low load conditions to achieve energy-saving effects. The self-learning and optimization characteristics of the algorithm enable the system to adapt to changing user needs and environmental conditions at any time, thereby improving the efficiency of multi-cabin collaborative operations, ensuring that system resources are maximized, and further enhancing the overall performance and intelligence level of the smart cabin network.

[0100] The present invention provides Figure 2 The intelligent networked distributed storage interactive system based on multi-cabin collaboration shown includes a node initialization module, a data collection and classification module, a data compression and encryption module, a data synchronization and conflict resolution module, a communication problem processing and version control module, and an intelligent scheduling and resource optimization module;

[0101] The node initialization module treats each smart cabin as an independent node, initializes and connects it to the intelligent networked distributed system, and establishes a preliminary connection between the smart cabin node and other nodes in the system;

[0102] Data collection and classification module: Each smart cabin collects user data, environmental data, and equipment status data through built-in sensors, and stores the collected data in a local storage unit. The data is classified and prioritized through a predefined algorithm to ensure that key data is processed and transmitted first in the event of network delays or interruptions.

[0103] The data compression and encryption module uses advanced compression algorithms to compress data before transmitting it to other smart cabin nodes or the central server, and uses encryption algorithms to ensure the security of data transmission;

[0104] The data synchronization and conflict resolution module uses a distributed database consistency algorithm to synchronize data between smart cabins to ensure data consistency in the event of network delays or communication interruptions. When multiple versions of the same data are detected, the system resolves data conflicts through timestamps, version control, and node priority determination mechanisms to ensure that the latest and correct data is synchronized;

[0105] The communication problem handling and version control module coordinates the storage nodes of each smart cabin through a distributed consistency algorithm. When communication problems occur in some nodes, the timestamp and node priority mechanism are used to determine the final data version based on the time sequence or the importance of the node when a conflict is detected;

[0106] The intelligent scheduling and resource optimization module uses a machine learning-based intelligent scheduling algorithm based on the data analysis results of each smart cabin in the system to automatically adjust and optimize the collaborative interaction between smart cabins. By analyzing the data load, environmental status and user needs of each node, it realizes intelligent operation of multi-cabin collaboration.

[0107] The intelligent networked distributed storage interaction method based on multi-cabin collaboration provided in an embodiment of the present invention is realized by the above-mentioned intelligent networked distributed storage interaction system based on multi-cabin collaboration. The specific methods and processes of the intelligent networked distributed storage interaction system based on multi-cabin collaboration are detailed in the embodiment of the above-mentioned intelligent networked distributed storage interaction method based on multi-cabin collaboration, which will not be repeated here.

[0108] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. An intelligent networked distributed storage interaction method based on multi-cabin collaboration, characterized in that: The following steps are involved: Treat each smart cabin as an independent node, initialize and connect it to the intelligent networked distributed system, and establish a preliminary connection between the smart cabin node and other nodes in the system; Each smart cabin collects user data, environmental data, and equipment status data through built-in sensors, and stores the collected data in a local storage unit. The data is classified and prioritized through predefined algorithms to ensure that key data is processed and transmitted first in the event of network delays or interruptions. Before transmitting data to other smart cabin nodes or central servers, advanced compression algorithms are used to compress data, and encryption algorithms are used to ensure the security of data transmission; A distributed database consistency algorithm is used to synchronize data between smart cabins to ensure data consistency in the event of network delays or communication interruptions. When multiple versions of the same data are detected, the system resolves data conflicts through timestamps, version control, and node priority determination mechanisms to ensure that the latest and correct data is synchronized. The storage nodes of each smart cabin are coordinated through a distributed consensus algorithm. When communication problems occur in some nodes, the final data version is determined based on the time sequence or the importance of the node when a conflict is detected through the timestamp and node priority mechanism; According to the data analysis results of each smart cabin in the system, an intelligent scheduling algorithm based on machine learning is adopted to automatically adjust and optimize the collaborative interaction between smart cabins. By analyzing the data load, environmental status and user needs of each node, intelligent operation of multi-cabin collaboration is realized.

2. The intelligent networked distributed storage interaction method based on multi-cabin collaboration according to claim 1 is characterized in that: When each smart cabin node is connected to the system for the first time, it obtains a unique network identifier through the network access protocol and node management algorithm to ensure that the system can identify different smart cabins in a wide-area distribution. The initialization process also includes setting the hardware and communication configuration of each node.

3. The intelligent networked distributed storage interaction method based on multi-cabin collaboration according to claim 1 is characterized in that: Data is classified and prioritized through predefined algorithms to ensure that critical data is processed and transmitted first in the event of network delays or interruptions. The specific steps are as follows: The collected data is divided into three categories: user data, environmental data and device status data, and stored in the local storage unit. Each type of data is identified according to the collection source and target function, and a classification mark is set. The formula for the classification mark is: C i =α i U+β i E+γ i ·S, where C i is the classification mark of the i-th type of data, U is the importance weight of user data, E is the importance weight of environmental data, S is the importance weight of device status data, α i , β i , γ i are the importance coefficients of each data type; The classified data is further identified to determine the key data. The key data is judged by a threshold function. The judgment expression is: In the formula, F(x) is the evaluation function of whether data x is key data, T is the evaluation period, and f(C i , P i , L i ) is based on the data classification mark C i , priority weight P i and data loss cost L i The evaluation function, P i is the priority weight, P i is the cost of data loss, which indicates the potential risk impact of data loss on the system. When F(x) exceeds the set threshold, the data is considered critical data; For key data, we sort them according to the priority weight to ensure priority processing in the case of network delay or interruption. The sorting algorithm uses a weighted sorting function, and the calculation expression is: R(i) = λ·P i +μ·L i +v·T i , where T i is the time sensitivity of the data, R(i) is the priority ranking value of data i, λ, μ, and v are the priority weights P respectively. i , data loss cost L i and time sensitivity T i The weighting coefficient is used to adjust the priority weight P i , data loss cost L i and time sensitivity T i The impact of After the priority sorting is completed, the system processes and transmits the data according to the priority sorting value R(i) in the case of network delay or interruption, and uses the transmission selection function to determine the data to be transmitted first. The expression of the transmission selection function is: T trans = max(R(i)) where i∈{1, 2, …, n}, where T trans is the data with the highest priority for transmission under the current network status, and n is the total amount of data.

4. The intelligent networked distributed storage interaction method based on multi-cabin collaboration according to claim 1 is characterized in that: The advanced compression algorithm is one of LZ77, Huffman coding and Brotli, and the encryption algorithm is one of AES, RSA and elliptic curve encryption algorithm.

5. The intelligent networked distributed storage interaction method based on multi-cabin collaboration according to claim 1 is characterized in that: When multiple versions of the same data are detected, the system resolves data conflicts through timestamps, version control, and node priority determination mechanisms to ensure that the latest and correct data is synchronized. The specific steps are as follows: When the system detects multiple versions of the same data, it first uses hash verification or unique identifiers to determine whether there is a conflict. The conflict judgment formula is: Where D conflict Indicates whether there is a conflict, H(D i ) is data D i The hash value of ref is the reference data, i.e. the expected standard version, δ(*) conflict detection function, when H(D i )≠H(D ref ), δ(*)=1, indicating a conflict. If D conflict >0, it is confirmed that there is a conflict and proceed to the next step; For multiple versions of conflicting data detected, the latest data is determined by the timestamp, and the timestamp priority principle is used to resolve the conflict. The specific formula is: T max =max(T1, T2, ..., T N ), where T max is the largest timestamp among the conflicting data, that is, the timestamp of the latest data, T j The timestamp of the jth version of data, where N is the number of conflicting versions; If the timestamps are the same, the version control mechanism is used to further determine the correctness of the data through the version number. The version number mechanism is based on the historical records of data updates and gives priority to data with larger version numbers. The specific expression of the version number mechanism is: V max =max(V1, V2, ..., V N ), where V max is the largest version number in the conflicting data, V j is the version number of the j-th version data; If the timestamp and version number are the same, the data priority is determined by the node priority. Each smart cabin node is assigned a different priority according to its importance in the system, and data from high-priority nodes is selected first. The node priority determination formula is: P max =max(P1, P2, ..., P M ), where P max represents the node with the highest priority, P k is the priority of the kth node, and M is the number of nodes with conflicting data; Taking into account the timestamp, version number and node priority, a comprehensive scoring formula is used to perform the final data conflict resolution and synchronization judgment. The expression of the comprehensive scoring formula is: S j =α·T τ +β·V τ +γ·P τ , where T τ , V τ , P τ They are timestamp, version number, node priority, S j is the comprehensive score of the j-th version data, α, β, and γ are the weight coefficients of timestamp, version number, and node priority, respectively.

6. The intelligent networked distributed storage interaction method based on multi-cabin collaboration according to claim 1 is characterized in that: The storage nodes of each smart cabin are coordinated through a distributed consensus algorithm. When communication problems occur in some nodes, the specific steps to determine the final data version are as follows: Step 1: The system continuously monitors the data synchronization status between the nodes of each smart cabin and records the storage update log of each node. Once it detects that some nodes cannot communicate with other nodes, it marks the node as a potential "communication loss node". In this case, it compares the data hash value or unique identifier to detect whether there are different versions of the data. When multiple nodes return different versions of the same data, the system identifies it as a data conflict. Step 2: After detecting data conflicts, the system first sorts the conflicting data through the timestamp mechanism. When writing data stored in each node, a timestamp is attached. The timestamp represents the specific time when the data was generated or last updated. When a conflict occurs, the system first sorts the data in chronological order. The data with the later timestamp represents the latest update. The system regards the version with the largest timestamp as the latest data and gives priority to synchronizing the content of this version. Step 3: If the timestamps of the conflicting data are the same, the system will further refer to the priority of the node. Nodes are given different priorities according to their importance or functional role in the system. Step 4: Based on the dual judgment mechanism of timestamp and priority, a distributed consistency protocol is used to make a final decision on the conflicting data.

7. The intelligent networked distributed storage interaction method based on multi-cabin collaboration according to claim 6 is characterized in that: When the communication problem is resolved and the lost node is back online, the system automatically triggers the data synchronization mechanism to synchronize the previously selected final version data with the data of the lost node. The system will make judgments based on the timestamps and node priorities of steps 2 and 3 to ensure that the lost node receives the latest and correct data.

8. The intelligent networked distributed storage interaction method based on multi-cabin collaboration according to claim 1 is characterized in that: According to the data analysis results of each smart cabin in the system, an intelligent scheduling algorithm based on machine learning is used to automatically adjust and optimize the collaborative interaction between smart cabins. The specific steps are as follows: Collect various types of data from each smart cabin node, perform feature extraction, and convert raw data into feature vectors that can be used for analysis; After completing data collection and feature extraction, the machine learning model is trained using historical data. The goal of the model is to predict the future load of the smart cabin, changes in user demand, and trends in environmental conditions. After the system predicts the future load of each smart cabin, the scheduling algorithm will further analyze the resource utilization in the current system and make the best resource allocation decision based on the user needs and environmental status of each smart cabin; Based on the analysis results, the system will use intelligent scheduling algorithms to make real-time adjustments to each smart cabin, dynamically adjusting the resource configuration and working status of each smart cabin according to system load, user needs and environmental changes; After the intelligent scheduling algorithm is executed, the system will continuously monitor the operating status of each smart cabin and collect the effect data of scheduling execution. Through real-time monitoring, it can identify potential problems in the execution of scheduling strategies. By analyzing historical data collected from each cabin through a deep learning model, the system automatically predicts user needs and environmental changes and adjusts the smart cabin in advance.

9. An intelligent networked distributed storage interaction system based on multi-cabin collaboration, used to implement the intelligent networked distributed storage interaction method based on multi-cabin collaboration as described in any one of claims 1 to 8, characterized in that: It includes node initialization module, data collection and classification module, data compression and encryption module, data synchronization and conflict resolution module, communication problem processing and version control module, and intelligent scheduling and resource optimization module; The node initialization module treats each smart cabin as an independent node, initializes and connects it to the intelligent networked distributed system, and establishes a preliminary connection between the smart cabin node and other nodes in the system; Data collection and classification module: Each smart cabin collects user data, environmental data, and equipment status data through built-in sensors, and stores the collected data in a local storage unit. The data is classified and prioritized through a predefined algorithm to ensure that key data is processed and transmitted first in the event of network delays or interruptions. The data compression and encryption module uses advanced compression algorithms to compress data before transmitting it to other smart cabin nodes or the central server, and uses encryption algorithms to ensure the security of data transmission; The data synchronization and conflict resolution module uses a distributed database consistency algorithm to synchronize data between smart cabins to ensure data consistency in the event of network delays or communication interruptions. When multiple versions of the same data are detected, the system resolves data conflicts through timestamps, version control, and node priority determination mechanisms to ensure that the latest and correct data is synchronized; The communication problem handling and version control module coordinates the storage nodes of each smart cabin through a distributed consistency algorithm. When communication problems occur in some nodes, the timestamp and node priority mechanism are used to determine the final data version based on the time sequence or the importance of the node when a conflict is detected; The intelligent scheduling and resource optimization module uses a machine learning-based intelligent scheduling algorithm based on the data analysis results of each smart cabin in the system to automatically adjust and optimize the collaborative interaction between smart cabins. By analyzing the data load, environmental status and user needs of each node, it realizes intelligent operation of multi-cabin collaboration.

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