Inland river water transportation facility monitoring data secure transmission method based on block chain technology

Abstract

The invention discloses an inland river water transportation facility monitoring data secure transmission method based on a block chain technology, and the method comprises the following steps: selecting an optimal sensor for data collection according to the geographic position and operation environment of a facility through a dynamic sensor switching mechanism, and eliminating errors caused by equipment faults and environment interference, after the collected data is subjected to time-space aggregation processing and classified according to geographic areas and time windows, based on a block chain node selection algorithm, an optimal node is selected for data transmission, the data is encrypted by adopting a hierarchical encryption strategy, the encrypted data is stored in a block chain network, and the data is transmitted to the block chain network through a threshold-triggered smart contract. And monitoring the stored data in real time, and automatically adjusting a monitoring scheme or starting an emergency mechanism when the stored data exceed a preset threshold value. The invention aims to improve the security and integrity of monitoring data transmission.

Description

Technical Field

[0001] The present invention relates to the field of data security transmission and monitoring technology, and in particular to a method for securely transmitting inland waterway transport facility monitoring data based on blockchain technology. Background Art

[0002] The operating environment of inland waterway transport facilities is complex and dynamic. Their safe and efficient operation depends on real-time and accurate monitoring data. With the rapid development of information technology, the collection, transmission and storage of monitoring data of inland waterway transport facilities face many challenges. Most of the existing monitoring systems rely on traditional centralized architectures. Data is transmitted and stored through fixed communication networks. This approach has problems such as single point failures, insufficient data security and transmission delays. Traditional monitoring systems are usually unable to respond to sudden environmental changes in a timely manner, such as extreme weather, equipment failures, etc., resulting in data collection that is not real-time and accurate enough and cannot meet the needs of efficient emergency management.

[0003] Existing technologies have several significant defects in the transmission and security management of inland waterway facility monitoring data, which limits the system's operating efficiency, security and emergency response capabilities. Most current monitoring systems rely on centralized data transmission and storage architectures. Although this approach can achieve centralized management of data, it also faces the risk of single point failures. Once a problem occurs with the central server, the data transmission and storage of the entire system will be affected, and even data loss may occur. Due to the inherent defects of the centralized architecture, there are hidden dangers in the security of data storage and transmission, especially when transmitting data between multiple nodes or users. There is a risk of data tampering or leakage, and the integrity and authenticity of the data are difficult to guarantee.

[0004] Existing monitoring systems usually use fixed sensors for monitoring. The layout of sensors cannot be dynamically adjusted as the geographical location and operating environment of the facilities change. As a result, in some cases, the sensors cannot accurately capture key environmental parameters, resulting in monitoring blind spots or data deviations. When inland waterway facilities pass through areas with complex environments or emergencies occur, it is difficult to adjust the monitoring plan in a timely manner with fixed sensor layouts, and the accuracy of the data cannot be ensured. Equipment failure or environmental interference may also cause large errors in the monitoring data. Existing technologies lack an effective mechanism to monitor and eliminate these abnormal data in real time, making it difficult to ensure the quality of the collected data.

[0005] In terms of data transmission, most existing technologies rely on fixed communication networks. The transmission path and node selection lack flexibility and cannot dynamically adjust the optimal transmission path according to the network status. When the network delay or node load is too high, the data transmission efficiency is affected, and it may even cause data transmission failure or loss. Especially in the face of large-scale data transmission tasks, the fluctuation of network performance will have an adverse effect on the reliability of transmission. The encryption processing methods in the data transmission process are often relatively simple, and the encryption method cannot be flexibly selected according to the importance and sensitivity of the data. The existing system usually adopts a fixed encryption algorithm and cannot dynamically switch between lightweight encryption and high-intensity encryption, which not only increases the overhead of data transmission, but also fails to Provide adequate security protection for highly sensitive data; the emergency response mechanism of the existing monitoring system is relatively lagging and relies more on manual intervention; once an abnormality or emergency is detected, it is usually necessary to take corresponding measures after manual analysis, resulting in a long response time and an inability to respond to emergencies quickly. This manual emergency response method is particularly insufficient in the inland waterway facility management scenario where the environment changes rapidly and emergencies occur frequently; in actual operation, due to delays in emergency handling, the best time for handling may be missed, resulting in serious economic losses and facility operation risks, and the existing automated emergency response system is not mature enough in terms of data processing speed and intelligent response, making it difficult to adjust monitoring plans and emergency handling in real time.

[0006] In terms of data storage, centralized storage methods are mostly used, which limits the security and reliability of the data storage system to a certain extent. Centralized storage not only increases the risk of data tampering and attack, but also causes a large amount of data to be lost or inaccessible when a storage node fails. The redundancy of data storage is insufficient, and the level of storage backup cannot be dynamically adjusted according to the importance of the data and the reliability of the node. This inflexible storage strategy has great hidden dangers in practical applications, especially in the monitoring scenario of inland waterway facilities with large data volume and high sensitivity. It is difficult to meet the data management needs of high security and high reliability.

[0007] Therefore, how to provide a secure transmission method for inland waterway transport facility monitoring data based on blockchain technology is an issue that technical personnel in this field urgently need to solve. Summary of the invention

[0008] One purpose of the present invention is to propose a method for secure transmission of inland waterway facility monitoring data based on blockchain technology. The present invention fully combines the decentralized characteristics of blockchain, dynamic sensor switching mechanism, smart contracts and hierarchical encryption strategy, and describes in detail the entire process from data collection, transmission to storage, ensuring the secure transmission and real-time monitoring of monitoring data in a multi-node network. Through the multi-node distributed storage of blockchain, dynamic selection of the optimal transmission path and real-time monitoring of environmental changes, it has the advantages of high security, high transmission efficiency and strong real-time emergency response capabilities.

[0009] According to an embodiment of the present invention, a method for securely transmitting inland waterway transport facility monitoring data based on blockchain technology comprises the following steps:

[0010] S1. Collect monitoring data of inland waterway facilities through a dynamic sensor switching mechanism, select the optimal sensor for data collection based on facility location and operating environment changes, and eliminate data errors caused by equipment failure and environmental interference;

[0011] S2. Preprocess the collected monitoring data, adopt a phased data aggregation strategy, merge the monitoring data in each collection period in time and space before data transmission, divide and aggregate the same type of data by geographical area and time window, retain key data features, and form aggregated monitoring data;

[0012] S3. Apply the real-time blockchain node selection algorithm to the aggregated monitoring data, and select the optimal transmission node for data transmission according to the real-time status of each node in the blockchain network. The transmission process is based on the shortest path priority principle.

[0013] S4. In the transmitted data, the transmission data is encrypted based on the hierarchical encryption strategy of the usage scenario. According to the different daily monitoring or key scenarios, a lightweight encryption algorithm or a high-intensity encryption algorithm is applied respectively to form encrypted monitoring data;

[0014] S5. Store the encrypted monitoring data in the blockchain network, adjust the strategy based on the dynamic redundancy level, dynamically set the number and location of redundant backups according to the reliability of the storage nodes and the importance of the data, and realize distributed data storage;

[0015] S6. Monitor the stored data through the threshold-triggered smart contract algorithm. According to the preset threshold of the water transport environment, when the environmental data exceeds the set range, the smart contract is triggered to execute an automatic response, adjust the monitoring plan or start the emergency mechanism;

[0016] S7. Combine historical data analysis with real-time feedback data to dynamically adjust monitoring strategies, filter currently collected data based on historical data patterns, and adjust sensor sensitivity and collection frequency based on real-time monitoring conditions to optimize the data collection process;

[0017] S8. Through the access control mechanism based on the data life cycle, the access rights of each node are dynamically adjusted according to the data generation time, storage time and usage frequency to achieve data security management and access control.

[0018] Optionally, the S1 specifically includes:

[0019] S11. Setting up the sensor network N＝ { n1,n2,,nm},in m To monitor the number of sensors, ni For the i Sensors to monitor multiple key parameters of inland waterway facilities;

[0020] S12. Obtain the current geographical location L of the inland waterway transport facility t and operating environment parameters E t , where L t =

[0021] xt,yt,zt ) represents the three-dimensional coordinate position of the facility, Et＝ { e1,e2,,ek} is the current environmental data set, which contains k real-time monitoring environmental parameters;

[0022] S13, according to L t and E t Trigger the dynamic sensor switching mechanism and use the sensor selection optimization algorithm S opt , select the best sensor n from the sensor network N opt , adapting it to the current facility location and operating environment:

[0023]

[0024] Among them, L(n i ) indicates sensor n i The position of ||L t L(n i )|| indicates the facility location L t With the sensor position L(n i ) distance, R(n i ,e j ) indicates sensor n i Environmental parameters e j The responsiveness of j is the environmental parameter e jThe standard deviation of , α1 and α2 are the weight coefficients for controlling position adaptability and environmental responsiveness;

[0025] S14. In the sensor network N, monitor the real-time status of the sensor. When a sensor n f When equipment failure occurs and the monitoring data deviation exceeds the threshold ∈ due to environmental interference, the fault elimination mechanism is triggered and the suboptimal sensor n is switched to next Continue to collect:

[0026]

[0027] Among them, D t (n i ) indicates sensor n i The actual data collected at time t, Indicates sensor n i The expected data value of ,∈ is the tolerance threshold for fault judgment;

[0028] S15, switch to suboptimal sensor n next After that, continue to collect monitoring data and use the data correction module to correct the collected data D t Perform error correction to eliminate data anomalies caused by faults and interference to form the final monitoring data D final .

[0029] Optionally, the S2 specifically includes:

[0030] S21, generate the final monitoring data set D final ={D final,1 ,D final,2 ,,D final,n} as input data, where D final,i Represents the monitoring data generated in each collection cycle, forming a preliminary data set D pre ;

[0031] S22, for D pre Each data D final,i According to the geographical area R and time window T of inland waterway facilities, the same type of data is grouped and processed according to geographical location and time period to generate a spatiotemporal merged dataset D grouped ;

[0032] S23, for the generated D grouped The dataset applies a phased data aggregation strategy to further aggregate similar data after spatiotemporal merging, using the data aggregation function F agg , combining geographic and temporal features, the aggregated dataset is represented as D agg :

[0033] Dagg =F agg (D grouped );

[0034] S24, to D agg Perform key feature extraction, extract the important monitoring features to be retained, and generate the final aggregated monitoring data D final_pre .

[0035] Optionally, the S3 specifically includes:

[0036] S31, generate the aggregated monitoring data set D final_pre ={D final_pre,1 ,…,D final_pre,n} as input data and enter the data transmission preparation stage, where D final_pre,i Represents the aggregated monitoring data in each geographic area and time window, forming a data set D for transmission trans ;

[0037] S32. Evaluate each node N according to the real-time status of each node in the blockchain network. i The transmission priority of the node is combined with the delay, load and reliability of the node, and the node priority formula P(N i )Calculate the optimal node:

[0038]

[0039] Among them, Delay(N i ) represents node N i Network delay, Reliability (N i ) represents node N i The historical transmission reliability of w1 and w2 are weight coefficients;

[0040] S33, based on node priority P(N i ) Select the optimal transmission node N opt ,The transmission process is comprehensively evaluated by combining node priority and path length;

[0041] S34, by selecting the optimal node N opt Perform data transmission and transfer data set D trans Transmit via the optimal path;

[0042] S35. During the transmission process, real-time monitoring of node status is performed. If the current node N opt If the state change of causes its priority to decrease, it will dynamically switch to the suboptimal node N next Continue data transfer.

[0043] Optionally, the S4 specifically includes:

[0044] S41, the generated transmission data set D trans ={D trans,1 ,D trans,2 ,,D trans,n} as encrypted input data, where each data D trans,i Indicates data packets that need to be encrypted for transmission;

[0045] S42, for transmitting data set D trans According to the sensitivity of monitoring data, it is classified into basic monitoring data D base and high sensitivity monitoring data D high , using a multi-level quantum encryption algorithm to encrypt the data to form an encrypted data set D enc ;

[0046] S43. Basic monitoring data D base Applying quantum-safe encryption algorithm E quantum , the algorithm is based on quantum key distribution and quantum entanglement technology:

[0047] C base =E quantum (D base ,K q );

[0048] Among them, C base is the encrypted basic monitoring data, E quantum It is a quantum-safe encryption algorithm, combined with quantum key distribution K q Implement key generation and encryption;

[0049] S44, for highly sensitive monitoring data D high Dynamic encryption algorithm E on application chain dynamic , the algorithm utilizes the multi-node collaborative characteristics of the blockchain network and combines the data transmission path and node status to generate encryption keys in real time:

[0050] C high =E dynamic (D high ,K dyn (N path ));

[0051] Among them, C high For encrypted high-sensitivity data, E dynamic K is the dynamic encryption algorithm on the chain. dyn (N path ) is the node N according to the blockchain transmission path path Dynamic encryption keys generated in real time;

[0052] S45, the encrypted basic monitoring data Cbase and high sensitivity monitoring data C high Integrate data packets to form encrypted data packets D enc ={C base ,C high};

[0053] S46. During the transmission process, the distributed storage and consensus mechanism of the blockchain is used to process encrypted data packets. enc , to achieve secure data transmission between multiple nodes.

[0054] Optionally, the S6 specifically includes:

[0055] S61. Set the environmental monitoring data set E for water transport facilities env ={e1,e2,…,e k}, where e i Indicates multiple key environmental parameters and real-time monitoring data E real Distributed nodes stored in the blockchain network;

[0056] S62, defining a preset threshold function T for the water transport environment env (e i ), which performs dynamic calculations based on fluctuations in environmental conditions and historical data:

[0057] T env (e i )=λ i ·(exp(μ i ·e i ));

[0058] Among them, λ i is the adjustment factor under environmental conditions, μ i is the fitting coefficient between historical data and the current environment. i Exceeding the threshold T env (e i ), the threshold-triggered smart contract is triggered;

[0059] S63, when real-time monitoring data e i Exceeding the threshold T env (e i ), start the threshold-triggered smart contract S contract , monitor changes in environmental data and automatically respond:

[0060]

[0061] S64. After the emergency response is initiated, an emergency monitoring data set D is generated. emerg, and distribute data to relevant nodes through the smart contract module of the blockchain network, adjust the monitoring equipment, and modify the monitoring strategy or operating status;

[0062] S65. Record all steps of the smart contract execution process and emergency response into the blockchain distributed ledger and generate an operation log L log .

[0063] Optionally, the S8 specifically includes:

[0064] S81, generate the emergency monitoring data set D emerg and encrypted stored dataset D enc , combined with the historical behavior data H stored in the blockchain network hist , generate user behavior feature dataset B user ={b1,b2,,b m}, where b i Represents the behavioral characteristics of each user;

[0065] S82, generate the user behavior feature data set B user Perform feature aggregation, classify and group feature data according to time windows and behavior patterns, and form spatiotemporal aggregated behavior feature data B grouped ;

[0066] S83, aggregated user behavior feature data B grouped Apply the feature fusion algorithm based on topological quantum field to calculate the key feature weights:

[0067]

[0068] Among them, P fused is the behavioral feature weight obtained by feature fusion, w i is the temporal and spatial weight of the behavior feature, λ i To adjust the parameters;

[0069] S84, according to the transmission data D trans Combined with the generated emergency monitoring data D emerg and the aggregated user behavior feature weight P fused , generate interest tags T label ;

[0070] S85, generate the interest tag T label Updated in real time through the blockchain network and combined with emergency data emerg , continuously optimize the matching degree between tags and user behaviors, and dynamically update interest tags based on changes in users’ current behaviors through smart contract mechanisms.

[0071] The beneficial effects of the present invention are:

[0072] The present invention uses a dynamic sensor switching mechanism to automatically select the optimal sensor for data collection according to the location of the facility and changes in the operating environment, effectively avoiding the monitoring blind spots and data error problems existing in traditional fixed sensor systems; through a phased data aggregation strategy, the present invention performs spatiotemporal merging and key feature extraction on the collected monitoring data, solving the problem of low data processing efficiency in traditional monitoring systems. The strategy can adaptively group and aggregate data according to geographic areas and time windows, making data processing more orderly and efficient, thereby reducing the storage and transmission burden of redundant data.

[0073] The present invention adopts the node selection algorithm of the blockchain network, and dynamically selects the transmission node according to the real-time status of the node, ensuring the efficiency and stability of data transmission. This algorithm avoids the single point failure risk of the traditional centralized transmission method, improves the fault tolerance and reliability of data transmission, and ensures that the monitoring data can be stably transmitted in a multi-node environment; adopts a hierarchical encryption strategy, and dynamically selects lightweight encryption or high-intensity quantum encryption according to the sensitivity of the data, which not only ensures the rapid transmission of daily monitoring data, but also provides higher security for highly sensitive data. Compared with the traditional single encryption method, this encryption strategy can significantly improve the security of data without increasing the system load.

[0074] Through the threshold-triggered smart contract mechanism, the present invention realizes real-time monitoring and automatic emergency response of monitoring data. When the monitoring data exceeds the preset threshold, the smart contract automatically executes the emergency response or adjusts the monitoring plan, which solves the problem of emergency response delay caused by manual intervention lag in traditional systems, and greatly improves the system's processing speed and accuracy for emergencies. Combined with historical data analysis and real-time feedback, the present invention can optimize the currently collected data according to the historical data pattern, and dynamically adjust the sensitivity and collection frequency of the sensor. This innovation effectively reduces the collection of invalid data and improves the quality of data and the intelligence level of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0076] Figure 1 This is a flow chart of the method for secure transmission of inland waterway transport facility monitoring data based on blockchain technology proposed by the present invention;

[0077] Figure 2 is a schematic diagram of the dynamic sensor switching mechanism;

[0078] Figure 3The transmission flow chart of the blockchain node selection algorithm. DETAILED DESCRIPTION

[0079] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0080] refer to Figure 1-3 , a method for secure transmission of inland waterway transport facility monitoring data based on blockchain technology, comprising the following steps:

[0081] S1. Collect monitoring data of inland waterway facilities through a dynamic sensor switching mechanism, select the optimal sensor for data collection based on facility location and operating environment changes, and eliminate data errors caused by equipment failure and environmental interference;

[0082] S2. Preprocess the collected monitoring data, adopt a phased data aggregation strategy, merge the monitoring data in each collection period in time and space before data transmission, divide and aggregate the same type of data by geographical area and time window, retain key data features, and form aggregated monitoring data;

[0083] S3. Apply the real-time blockchain node selection algorithm to the aggregated monitoring data, and select the optimal transmission node for data transmission according to the real-time status of each node in the blockchain network. The transmission process is based on the shortest path priority principle.

[0084] S4. In the transmitted data, the transmission data is encrypted based on the hierarchical encryption strategy of the usage scenario. According to the different daily monitoring or key scenarios, a lightweight encryption algorithm or a high-intensity encryption algorithm is applied respectively to form encrypted monitoring data;

[0085] S5. Store the encrypted monitoring data in the blockchain network, adjust the strategy based on the dynamic redundancy level, dynamically set the number and location of redundant backups according to the reliability of the storage nodes and the importance of the data, and realize distributed data storage;

[0086] S6. Monitor the stored data through the threshold-triggered smart contract algorithm. According to the preset threshold of the water transport environment, when the environmental data exceeds the set range, the smart contract is triggered to execute an automatic response, adjust the monitoring plan or start the emergency mechanism;

[0087] S7. Combine historical data analysis with real-time feedback data to dynamically adjust monitoring strategies, filter currently collected data based on historical data patterns, and adjust sensor sensitivity and collection frequency based on real-time monitoring conditions to optimize the data collection process;

[0088] S8. Through the access control mechanism based on the data life cycle, the access rights of each node are dynamically adjusted according to the data generation time, storage time and usage frequency to achieve data security management and access control.

[0089] In this implementation, S1 specifically includes:

[0090] S11, set the sensor network N = {n1,n2,,n m}, where m is the number of monitoring sensors, n i The i-th sensor monitors multiple key parameters of inland waterway transport facilities;

[0091] S12. Obtain the current geographical location L of the inland waterway transport facility t and operating environment parameters E t , where L t =

[0092] x t ,y t ,z t ) represents the three-dimensional coordinate position of the facility, E t ={e1,e2,,e k} is the current environmental data set, which contains k real-time monitoring environmental parameters;

[0093] S13, according to L t and E t Trigger the dynamic sensor switching mechanism and use the sensor selection optimization algorithm S opt , select the best sensor n from the sensor network N opt , adapting it to the current facility location and operating environment:

[0094]

[0095] Among them, L(n i ) indicates sensor n i The position of ||L t L(n i )|| indicates the facility location L t With the sensor position L(n i ) distance, R(n i ,e j ) indicates sensor n i Environmental parameters e j The responsiveness of j is the environmental parameter e j The standard deviation of , α1 and α2 are the weight coefficients for controlling position adaptability and environmental responsiveness;

[0096] S14. In the sensor network N, monitor the real-time status of the sensor. When a sensor nf When equipment failure occurs and the monitoring data deviation exceeds the threshold ∈ due to environmental interference, the fault elimination mechanism is triggered and the suboptimal sensor n is switched to next Continue to collect:

[0097]

[0098] Among them, D t (n i ) indicates sensor n i The actual data collected at time t, Indicates sensor n i The expected data value of ,∈ is the tolerance threshold for fault judgment;

[0099] S15, switch to suboptimal sensor n next After that, continue to collect monitoring data and use the data correction module to correct the collected data D t Perform error correction to eliminate data anomalies caused by faults and interference to form the final monitoring data D final .

[0100] In this implementation, S2 specifically includes:

[0101] S21, generate the final monitoring data set D final ={D final,1 ,D final,2 ,,D final,n} as input data, where D final,i Represents the monitoring data generated in each collection cycle, forming a preliminary data set D pre ;

[0102] S22, for D pre Each data D final,i According to the geographical area R and time window T of inland waterway facilities, the same type of data is grouped and processed according to geographical location and time period to generate a spatiotemporal merged dataset D grouped ;

[0103] S23, for the generated D grouped The dataset applies a phased data aggregation strategy to further aggregate similar data after spatiotemporal merging, using the data aggregation function F agg , combining geographic and temporal features, the aggregated dataset is represented as D agg :

[0104] D agg =F agg (D grouped );

[0105] S24, to D aggPerform key feature extraction, extract the important monitoring features to be retained, and generate the final aggregated monitoring data D final_pre .

[0106] In this implementation, S3 specifically includes:

[0107] S31, generate the aggregated monitoring data set D final_pre ={D final_pre,1 ,…,D final_pre,n} as input data and enter the data transmission preparation stage, where D final_pre,i Represents the aggregated monitoring data in each geographic area and time window, forming a data set D for transmission trans ;

[0108] S32. Evaluate each node N according to the real-time status of each node in the blockchain network. i The transmission priority of the node is combined with the delay, load and reliability of the node, and the node priority formula P(N i )Calculate the optimal node:

[0109]

[0110] Among them, Delay(N i ) represents node N i Network delay, Reliability (N i ) represents node N i The historical transmission reliability of w1 and w2 are weight coefficients;

[0111] S33, based on node priority P(N i ) Select the optimal transmission node N opt ,The transmission process is comprehensively evaluated by combining node priority and path length;

[0112] S34, by selecting the optimal node N opt Perform data transmission and transfer data set D trans Transmit via the optimal path;

[0113] S35. During the transmission process, real-time monitoring of node status is performed. If the current node N opt If the state change of causes its priority to decrease, it will dynamically switch to the suboptimal node N next Continue data transfer.

[0114] In this implementation, S4 specifically includes:

[0115] S41, the generated transmission data set D trans ={D trans,1 ,D trans,2 ,,Dtrans,n} as encrypted input data, where each data D trans,i Indicates data packets that need to be encrypted for transmission;

[0116] S42, for transmitting data set D trans According to the sensitivity of monitoring data, it is classified into basic monitoring data D base and high sensitivity monitoring data D high , using a multi-level quantum encryption algorithm to encrypt the data to form an encrypted data set D enc ;

[0117] S43. Basic monitoring data D base Applying quantum-safe encryption algorithm E quantum , the algorithm is based on quantum key distribution and quantum entanglement technology:

[0118] C base =E quantum (D base ,K q );

[0119] Among them, C base is the encrypted basic monitoring data, E quantum It is a quantum-safe encryption algorithm, combined with quantum key distribution K q Implement key generation and encryption;

[0120] S44, for highly sensitive monitoring data D high Dynamic encryption algorithm E on application chain dynamic , the algorithm utilizes the multi-node collaborative characteristics of the blockchain network and combines the data transmission path and node status to generate encryption keys in real time:

[0121] C high =E dynamic (D high ,K dyn (N path ));

[0122] Among them, C high For encrypted high-sensitivity data, E dynamic K is the dynamic encryption algorithm on the chain. dyn (N path ) is the node N according to the blockchain transmission path path Dynamic encryption keys generated in real time;

[0123] S45, the encrypted basic monitoring data C base and high sensitivity monitoring data C high Integrate data packets to form encrypted data packets D enc＝ { Cbase, C high};

[0124] S46. During the transmission process, the distributed storage and consensus mechanism of the blockchain is used to process encrypted data packets. enc , to achieve secure data transmission between multiple nodes.

[0125] In this implementation manner, S6 specifically includes:

[0126] S61. Set the environmental monitoring data set E for water transport facilities env ={e1,e2,…,e k}, where e i Indicates multiple key environmental parameters and real-time monitoring data E real Distributed nodes stored in the blockchain network;

[0127] S62, defining a preset threshold function T for the water transport environment env (e i ), which performs dynamic calculations based on fluctuations in environmental conditions and historical data:

[0128] T env (e i )=λ i ·(exp(μ i ·e i ));

[0129] Among them, λ i is the adjustment factor under environmental conditions, μ i is the fitting coefficient between historical data and the current environment. i Exceeding the threshold T env (e i ), the threshold-triggered smart contract is triggered;

[0130] S63, when real-time monitoring data e i Exceeding the threshold T env (e i ), start the threshold-triggered smart contract S contract , monitor changes in environmental data and automatically respond:

[0131]

[0132] S64. After the emergency response is initiated, an emergency monitoring data set D is generated. emerg , and distribute data to relevant nodes through the smart contract module of the blockchain network, adjust the monitoring equipment, and modify the monitoring strategy or operating status;

[0133] S65. Record all steps of the smart contract execution process and emergency response into the blockchain distributed ledger and generate an operation log L log .

[0134] In this implementation manner, S8 specifically includes:

[0135] S81, generate the emergency monitoring data set D emerg and encrypted stored dataset D enc , combined with the historical behavior data H stored in the blockchain network hist , generate user behavior feature dataset B user ={b1,b2,,b m}, where b i Represents the behavioral characteristics of each user;

[0136] S82, generate the user behavior feature data set B user Perform feature aggregation, classify and group feature data according to time windows and behavior patterns, and form spatiotemporal aggregated behavior feature data B grouped ;

[0137] S83, aggregated user behavior feature data B grouped Apply the feature fusion algorithm based on topological quantum field to calculate the key feature weights:

[0138]

[0139] Among them, P fused is the behavioral feature weight obtained by feature fusion, w i is the temporal and spatial weight of the behavior feature, λ i To adjust the parameters;

[0140] S84, according to the transmission data D trans Combined with the generated emergency monitoring data D emerg and the aggregated user behavior feature weight P fused , generate interest tags T label ;

[0141] S85, generate the interest tag T label Updated in real time through the blockchain network and combined with emergency data emerg , continuously optimize the matching degree between tags and user behaviors, and dynamically update interest tags based on changes in users’ current behaviors through smart contract mechanisms.

[0142] Embodiment 1:

[0143] In order to verify the feasibility of the present invention in implementation, the present invention is applied to a certain inland waterway transport hub port A, which undertakes the tasks of a large number of ships entering and exiting and cargo transportation. The water flow conditions are complex and the climate is changeable. Especially during the flood season, the water level fluctuates frequently, and the stability of ship scheduling and port facilities faces great challenges. In order to ensure the safe and efficient operation of the water transport facilities of port A in a complex environment, the management department decides to apply the inland waterway transport facility monitoring data security transmission method based on blockchain technology proposed in the present invention, and improve the port management efficiency by monitoring the water level, flow rate, ship location and facility operation status.

[0144] In this scenario, Port A uses a dynamic sensor switching mechanism and deploys multiple monitoring sensors to monitor water levels, ship locations, operating status of port equipment, and environmental changes. Since Port A is located at the intersection of rivers, water flow often fluctuates significantly due to factors such as water release from upstream reservoirs and heavy rains. Traditional monitoring systems cannot adjust sensors in real time according to these changes, resulting in distorted data collection and large delays. Through the dynamic sensor switching mechanism of the present invention, the sensor selects the optimal sensor according to real-time environmental changes. When the water level sensor near the port is disturbed by strong currents, the system automatically switches to the sensor located upstream to continue collecting data, avoiding the problem of inaccurate data collection.

[0145] During the data processing and transmission process, a blockchain-based node selection algorithm is adopted. Due to the large amount of data in Port A, the traditional data transmission method cannot quickly respond to the real-time changes of water levels and ships, and there is a certain risk of data loss. Through the blockchain node selection mechanism of the present invention, the system dynamically selects the optimal node for data transmission based on the delay, load and reliability of the transmission node. During a large-flow ship scheduling process, because the port server load was too high, the system automatically selected a low-load backup server node for data transmission, ensuring the efficient transmission of scheduling data and avoiding data delays caused by server congestion.

[0146] In terms of encryption and data security, the monitoring data of Port A is encrypted in a hierarchical manner according to its importance. A lightweight encryption algorithm is used for routine monitoring data, while quantum encryption technology is applied to sensitive data related to equipment operating status and ship scheduling to ensure the security of data during multi-node transmission. Previously, port management had encountered external network attacks due to failure to encrypt data in a timely manner. The hierarchical encryption strategy of the present invention significantly improves data security. After testing the encryption performance, the port data center found that sensitive data using quantum encryption did not leak or tamper with data during transmission, ensuring the safe operation of the monitoring system.

[0147] The present invention uses a threshold-triggered smart contract to monitor environmental data in real time and automatically trigger an emergency response mechanism. During a rainstorm, the water level at the port rose rapidly and exceeded the preset safety threshold. The traditional manual monitoring method requires on-site personnel to confirm and issue an alarm. Delays may cause equipment damage or even personnel safety issues. After adopting the present invention, the system detects that the water level exceeds the safety threshold, immediately triggers the smart contract to execute the emergency plan, automatically adjusts the operating status of the port facilities, and sends alarms to relevant departments in real time through the blockchain network, dispatching rescue ships to high-risk areas for safety inspections. The trigger time of this emergency response is within 1 second, which is 95% faster than traditional manual intervention.

[0148] Within three months after the application of the monitoring system of the present invention, the monitoring accuracy and data transmission efficiency of Port A were significantly improved. After comparative analysis, it was found that the collection accuracy of water level data was increased from the original 85% to 99.5%, and the delay time of ship scheduling data was reduced from an average of 500 milliseconds to 50 milliseconds. The system conducted an in-depth analysis of historical data and automatically adjusted the sensitivity and collection frequency of the sensor according to the past water flow fluctuation patterns, which reduced the equipment failure rate by 15% and avoided the problem of mis-scheduling caused by inaccurate data.

[0149] In the month from July 1 to July 31, Port A handled more than 5,000 ship dispatches, and each dispatch was carried out through data transmission through the blockchain node selection mechanism, with a data packet loss rate of 0% during the transmission process; the port's water level monitoring data collection cycle was shortened from once every 10 minutes to once every 1 minute, greatly improving the real-time and effectiveness of the data; the emergency response mechanism was triggered 3 times, namely in heavy rain, typhoon and sudden floods, and each trigger was completed within 1 second, causing no damage to any facilities or injuries to personnel.

[0150] Table 1 Port A monitoring data analysis table

[0151]

[0152]

[0153] It can be seen from the above table data that after applying the monitoring system of the present invention, the number of ship dispatches in Port A remains at a relatively stable level, indicating that the system can efficiently cope with large-scale daily ship dispatch needs, and the delay in data transmission is maintained at about 50 milliseconds during the entire monitoring period, indicating that the blockchain node selection algorithm effectively improves the data transmission efficiency and ensures the reliability of real-time data transmission. The data packet loss rate is 0, which proves that in practical applications, the integrity of data transmission is guaranteed and no data loss occurs. The water level monitoring acquisition cycle is stable at 1 minute, which shows that the system can quickly update data according to environmental changes and ensure the real-time nature of water level monitoring. In terms of emergency response, the table shows the triggering of three emergency events. After each triggering of the smart contract, the response time is completed within 1 second, which fully reflects the automatic response advantage of the smart contract in emergencies. The application of the present invention effectively improves the monitoring accuracy, data transmission efficiency and emergency response speed of Port A, solves the lag and insecurity problems existing in traditional systems, and further improves the management efficiency and operation safety of the port.

[0154] In summary, the present invention solves the problems of inflexible monitoring data collection, insecure transmission, delayed emergency response, etc. in the prior art through innovative technologies such as dynamic sensor switching, blockchain node selection, hierarchical encryption, smart contracts and data aggregation, and greatly improves the safety, transmission efficiency and real-time emergency response capability of the inland waterway transport facility monitoring system.

[0155] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for secure transmission of inland waterway transport facility monitoring data based on blockchain technology, characterized in that: The steps include: S1. Collect monitoring data of inland waterway facilities through a dynamic sensor switching mechanism, select the optimal sensor for data collection based on facility location and operating environment changes, and eliminate data errors caused by equipment failure and environmental interference; S2. Preprocess the collected monitoring data, adopt a phased data aggregation strategy, merge the monitoring data in each collection cycle in time and space before data transmission, divide and aggregate the same type of data by geographical area and time window, retain key data features, and form aggregated monitoring data; S3. Apply the real-time blockchain node selection algorithm to the aggregated monitoring data, and select the optimal transmission node for data transmission according to the real-time status of each node in the blockchain network. The transmission process is based on the shortest path priority principle. S4. In the transmitted data, the transmission data is encrypted based on the hierarchical encryption strategy of the usage scenario. According to the different daily monitoring or key scenarios, a lightweight encryption algorithm or a high-intensity encryption algorithm is applied respectively to form encrypted monitoring data; S5. Store the encrypted monitoring data in the blockchain network, adjust the strategy based on the dynamic redundancy level, dynamically set the number and location of redundant backups according to the reliability of the storage nodes and the importance of the data, and realize distributed data storage; S6. Monitor the stored data through the threshold-triggered smart contract algorithm. According to the preset threshold of the water transport environment, when the environmental data exceeds the set range, the smart contract is triggered to execute an automatic response, adjust the monitoring plan or start the emergency mechanism; S7. Combine historical data analysis with real-time feedback data to dynamically adjust monitoring strategies, filter currently collected data based on historical data patterns, and adjust sensor sensitivity and collection frequency based on real-time monitoring conditions to optimize the data collection process; S8. Through the access control mechanism based on the data life cycle, the access rights of each node are dynamically adjusted according to the data generation time, storage time and usage frequency to achieve data security management and access control.

2. According to claim 1, the method for secure transmission of inland waterway transport facility monitoring data based on blockchain technology is characterized in that: The S1 specifically includes: S11, set the sensor network N = {n1,n2,,n m }, where m is the number of monitoring sensors, n i The i-th sensor monitors multiple key parameters of inland waterway transport facilities; S12. Obtain the current geographical location L of the inland waterway transport facility t and operating environment parameters E t , where L t =(x t ,y t , z t ) represents the three-dimensional coordinate position of the facility, E t ={e1, e2, e k } is the current environmental data set, which contains k real-time monitoring environmental parameters; S13, according to L t and E t Trigger the dynamic sensor switching mechanism and use the sensor selection optimization algorithm S opt , select the best sensor n from the sensor network N opt , adapting it to the current facility location and operating environment: Among them, L(n i ) indicates sensor n i The position of ||L t L(n i )|| indicates the facility location L t With the sensor position L(n i ) distance, R(n i , e j ) indicates sensor n i Environmental parameters e j The responsiveness of j is the environmental parameter e j The standard deviation of , α1 and α2 are the weight coefficients for controlling position adaptability and environmental responsiveness; S14. In the sensor network N, monitor the real-time status of the sensor. When a sensor n f When equipment failure occurs and the monitoring data deviation exceeds the threshold ∈ due to environmental interference, the fault elimination mechanism is triggered and the suboptimal sensor n is switched to next Continue to collect: Among them, D t (n i ) indicates sensor n i The actual data collected at time t, Indicates sensor n i The expected data value of ,∈ is the tolerance threshold for fault judgment; S15, switch to suboptimal sensor n next After that, continue to collect monitoring data and use the data correction module to correct the collected data D t Perform error correction to eliminate data anomalies caused by faults and interference to form the final monitoring data D final .

3. According to claim 1, the method for secure transmission of inland waterway transport facility monitoring data based on blockchain technology is characterized in that: The S2 specifically includes: S21, generate the final monitoring data set D final ={D final,1 , D final，2 , ,D final,n } as input data, where D final，i Represents the monitoring data generated in each collection cycle, forming a preliminary data set D pre ; S22, for D pre Each data D final，i According to the geographical area R and time window T of inland waterway facilities, the same type of data is grouped and processed according to geographical location and time period to generate a spatiotemporal merged dataset D grouped ; S23, for the generated D grouped The dataset applies a phased data aggregation strategy to further aggregate similar data after spatiotemporal merging, using the data aggregation function F agg , combining geographic and temporal features, the aggregated dataset is represented as D agg : D agg =F agg (D grouped ); S24, to D agg Perform key feature extraction, extract the important monitoring features to be retained, and generate the final aggregated monitoring data D final_pre .

4. According to claim 1, the method for secure transmission of inland waterway transport facility monitoring data based on blockchain technology is characterized in that: The S3 specifically includes: S31, generate the aggregated monitoring data set D final_pre ={D final_pre,1 , ..., D final_pre,n } as input data and enter the data transmission preparation stage, where D final_pre，i Represents the aggregated monitoring data in each geographic area and time window, forming a data set D for transmission trans ; S32. Evaluate each node N according to the real-time status of each node in the blockchain network. i The transmission priority of the node is combined with the delay, load and reliability of the node, and the node priority formula P(N i )Calculate the optimal node: Among them, Delay(N i ) represents node N i Network delay, Reliability (N i ) represents node N i The historical transmission reliability of w1 and w2 are weight coefficients; S33, based on node priority P(N i ) Select the optimal transmission node N opt ,The transmission process is comprehensively evaluated by combining node priority and path length; S34, by selecting the optimal node N opt Perform data transmission and transfer data set D trans Transmit via the optimal path; S35. During the transmission process, real-time monitoring of node status is performed. If the current node N opt If the state change of causes its priority to decrease, it will dynamically switch to the suboptimal node N next Continue data transfer.

5. According to claim 1, the method for secure transmission of inland waterway transport facility monitoring data based on blockchain technology is characterized in that: The S4 specifically includes: S41, the generated transmission data set D trans ={D trans,1 , D trans,2 , ,D trans,n } as encrypted input data, where each data D trans，i Indicates data packets that need to be encrypted for transmission; S42, for transmitting data set D trans According to the sensitivity of monitoring data, it is classified into basic monitoring data D base and high sensitivity monitoring data D high , using a multi-level quantum encryption algorithm to encrypt the data to form an encrypted data set D enc ; S43. Basic monitoring data D base Applying quantum-safe encryption algorithms E quantum , the algorithm is based on quantum key distribution and quantum entanglement technology: C base =E quantum (D base ,K q ); Among them, C base is the encrypted basic monitoring data, E quantum It is a quantum-safe encryption algorithm, combined with quantum key distribution K q Implement key generation and encryption; S44, for highly sensitive monitoring data D high Dynamic encryption algorithm E on application chain dynamic , the algorithm utilizes the multi-node collaborative characteristics of the blockchain network and combines the data transmission path and node status to generate encryption keys in real time: C high =E dynamic (D high ,K dyn (N path )); Among them, C high For encrypted high-sensitivity data, E dynamic K is the dynamic encryption algorithm on the chain. dyn (N path ) is the node N according to the blockchain transmission path path Dynamic encryption keys generated in real time; S45, the encrypted basic monitoring data C base and high sensitivity monitoring data C high Integrate data packets to form encrypted data packets D enc ={C base , C high }; S46. During the transmission process, the distributed storage and consensus mechanism of the blockchain is used to process encrypted data packets. enc , to achieve secure data transmission between multiple nodes.

6. The method for secure transmission of inland waterway transport facility monitoring data based on blockchain technology according to claim 1 is characterized in that: The S6 specifically includes: S61. Set the environmental monitoring data set E for water transport facilities env ={e1, e2, ..., e k }, where e i Indicates multiple key environmental parameters and real-time monitoring data E real Distributed nodes stored in the blockchain network; S62, defining a preset threshold function T for the water transport environment env (e i ), which performs dynamic calculations based on fluctuations in environmental conditions and historical data: T env (and i )=λ i ·(exp(μ i ·and i )); Among them, λ i is the adjustment factor under environmental conditions, μ i is the fitting coefficient between historical data and the current environment. i Exceeding the threshold T env (e i ), triggers the threshold-triggered smart contract; S63, when real-time monitoring data e i Exceeding the threshold T env (e i ), start the threshold-triggered smart contract S contract , monitor changes in environmental data and automatically respond: S64. After the emergency response is initiated, an emergency monitoring data set D is generated. emerg , and distribute data to relevant nodes through the smart contract module of the blockchain network, adjust the monitoring equipment, and modify the monitoring strategy or operating status; S65. Record all steps of the smart contract execution process and emergency response into the blockchain distributed ledger and generate an operation log L log .

7. The method for secure transmission of inland waterway transport facility monitoring data based on blockchain technology according to claim 1 is characterized in that: The S8 specifically includes: S81, generate the emergency monitoring data set D emerg and encrypted stored dataset D enc , combined with the historical behavior data H stored in the blockchain network hist , generate user behavior feature dataset B user = {b1, b2, b m }, where b i Represents the behavioral characteristics of each user; S82, generate the user behavior feature data set B user Perform feature aggregation, classify and group feature data according to time windows and behavior patterns, and form spatiotemporal aggregated behavior feature data B grouped ; S83, aggregated user behavior feature data B grouped Apply the feature fusion algorithm based on topological quantum field to calculate the key feature weights: Among them, P fused is the behavioral feature weight obtained by feature fusion, w i is the temporal and spatial weight of the behavior feature, λ i To adjust the parameters; S84, according to the transmission data D trans Combined with the generated emergency monitoring data D emerg and the aggregated user behavior feature weight P fused , generate interest tags T label ; S85, generate the interest tag T label Updated in real time through the blockchain network and combined with emergency data emerg , continuously optimize the matching degree between tags and user behaviors, and dynamically update interest tags based on changes in users’ current behaviors through smart contract mechanisms.

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