Digital integrated application system based on intelligent highway management and maintenance

Abstract

The invention discloses a digital integrated application system based on intelligent highway management and maintenance, and the system comprises a data collection and sensing module, a data transmission and communication module, a digital integrated application module, an intelligent highway management and maintenance module, a data storage and management module, and a user monitoring and decision module. The data transmission and communication module is used for data transmission and communication. The digital integration application module is used for data fusion processing and analysis. The road intelligent management and maintenance module is used for road state detection and preventive maintenance. The data storage and management module is used for data storage and management. The user monitoring and decision-making module is used for providing a user interface and decision-making support. The invention provides a digital integrated application system based on intelligent road management and maintenance, provides a road information extraction algorithm based on multi-modal data fusion for multi-modal data fusion and information extraction, and provides a road maintenance strategy optimization algorithm based on a genetic algorithm for making and optimizing a road maintenance strategy.

Description

Technical Field

[0001] The present invention relates to the fields of multimodal data fusion, intelligent information extraction and genetic algorithm, and specifically to a digital integrated application system based on intelligent highway maintenance. Background Art

[0002] Multimodal data fusion and intelligent information extraction technology is a multimodal data processing and information extraction method that aims to solve the problems of multimodal data processing and information extraction in sensor networks. Multimodal data fusion technology fuses multimodal data from different sensors, and intelligent information extraction technology can intelligently extract the most valuable information for highway status monitoring and maintenance. Through the effective combination of the two technologies, the system can more comprehensively and accurately grasp the real-time status of the highway, providing a solid data foundation for intelligent highway management and maintenance.

[0003] Genetic algorithm is an optimization algorithm based on evolutionary theory, which aims to solve the problems of highway maintenance demand prediction and maintenance strategy optimization. By analyzing historical data and current status indicators, genetic algorithm can predict future highway maintenance needs and formulate and optimize highway maintenance strategies. The use of genetic algorithm can significantly improve maintenance effects and resource utilization efficiency, ensure that the highway operates in the optimal state, extend its service life and reduce maintenance costs.

[0004] However, the existing digital integrated application system based on intelligent highway maintenance lacks effective methods in multimodal data processing and information extraction, resulting in low data utilization efficiency and incomplete information extraction. Secondly, the system lacks advanced algorithm support in highway maintenance demand prediction and maintenance strategy optimization, and cannot accurately predict future maintenance needs, resulting in unscientific maintenance strategies and low resource utilization efficiency. Summary of the invention

[0005] The purpose of the present invention is to provide a digital integrated application system based on intelligent highway management and maintenance, so as to solve the problems existing in the existing digital integrated application system based on intelligent highway management and maintenance proposed in the above background technology, namely, the lack of effective methods in multimodal data processing and information extraction, resulting in low data utilization efficiency and incomplete information extraction, and the system lacks advanced algorithm support in highway maintenance demand prediction and maintenance strategy optimization, resulting in the inability to accurately predict future maintenance needs, thereby causing the maintenance strategy to be unscientific and low resource utilization efficiency.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a digital integrated application system based on intelligent highway management and maintenance, including a data acquisition and sensing module, a data transmission and communication module, a digital integrated application module, an intelligent highway management and maintenance module, a data storage and management module, and a user monitoring and decision-making module, characterized in that: the data acquisition and sensing module is used to collect highway status data in real time through a sensor network, including temperature, humidity, pressure and vibration information; the data transmission and communication module is used to transmit the collected data through a wireless network and 5G technology and communicate with a central server; the digital integrated application module includes a data fusion processing unit and an intelligent analysis and application unit; the data fusion processing unit proposes a highway information extraction algorithm based on multimodal data fusion for performing data fusion processing on multimodal data from the sensor network and intelligently extracting effective information; the intelligent The analysis and application unit is used to perform intelligent analysis on the effective information extracted from the fused data and apply the analysis results to highway status prediction, maintenance strategy optimization and decision support. The highway intelligent management and maintenance module includes a highway status monitoring unit and a maintenance strategy optimization unit. The highway status monitoring unit is used to monitor the specific status indicators of the highway in real time, including road surface temperature, humidity, crack width, settlement height and traffic flow. The maintenance strategy optimization unit proposes a highway maintenance strategy optimization algorithm based on genetic algorithm to formulate and optimize highway maintenance strategies to improve maintenance effects and resource utilization efficiency. The data storage and management module is used to centrally store and manage the collected data and processed analysis data to ensure data security and availability. The user monitoring and decision-making module is used to provide users with a real-time monitoring interface and decision support functions, including data visualization and early warning notifications as well as decision control functions.

[0007] Preferably, the data acquisition and sensing module collects multimodal data of the highway in real time through a sensor network constructed on the highway, including temperature sensors, humidity sensors, pressure sensors and vibration sensors, to ensure that the highway status information can be obtained in real time, providing a basis for subsequent data processing and analysis.

[0008] Preferably, the data transmission and communication module transmits the collected data to the central server in a timely manner by utilizing wireless networks and 5G communication technology, ensuring the real-time and accuracy of the data to support immediate decision-making and response.

[0009] Preferably, the digital integrated application module includes a data fusion processing unit, which proposes a highway information extraction algorithm based on multimodal data fusion, integrates and processes multimodal data from different sensors, ensures data consistency and integrity, and extracts effective information and utilization value of multimodal data.

[0010] Specifically, the highway information extraction algorithm based on multimodal data fusion is as follows: First, assume the organizational structure of a data unit. The data unit constitutes the basic unit of the overall multimodal data fusion algorithm. Each data unit contains an information core, a strategy set, and a data pipeline set corresponding to the strategy. The specific formula of the organizational structure of the data unit is expressed as follows:

[0011] U={C,S,O}

[0012] Among them, U represents data unit, C represents information core, S represents strategy set, O represents data pipeline set, information core C is the core component of data unit, carrying key information for data intelligence implementation, strategy set S includes input and output strategy, data standardization strategy and mathematical logic operation strategy, data pipeline O is the channel for information transmission and communication, corresponding to strategy one by one, and new information core is generated through the association of data pipeline to realize the hierarchical structure of data unit, then, the initial data unit is constructed to be directly associated with the database, including the construction of initial information core, initial strategy and initial data pipeline. The initial information core is the field name and technical name extracted from the database, which is the common attribute of data. The initial strategy set includes basic strategy and advanced strategy. The basic strategy involves data input and output, standardization and basic mathematical logic operation. The initial data pipeline corresponds to strategy one by one, and data pipeline is defined through strategy for data transmission and communication. The specific formula is expressed as follows:

[0013] U i ={C i ,S(C i ),O(C i ,S(C i ))}

[0014] Among them, U i Represented as the i-th initial data unit, C i Represented as the i-th information core, the key information extracted from the database, S(C i ) is expressed as information core C i The strategy function, O(C i ,S(C i )) is represented as a data pipeline function. The data pipeline is generated according to the information core and strategy. i is represented as the index of the data unit. The specific formula of the data standardization strategy is expressed as follows:

[0015]

[0016] Among them, S norm (C i ) is represented as applied to the information core C i The data standardization strategy function, norm represents the standardization operation, Represented as information core C i The mean of Represented as information core C i The standard deviation of , secondly, randomly associate the data pipelines, and continuously generate the information core of the high-level data unit containing new information to construct the high-level data unit, and form the data pipeline set of the initial data unit through all the data pipelines of the initial data unit, and on this basis, randomly associate the data pipelines to generate the information core of the binary combination data unit. The specific formula is expressed as:

[0017]

[0018] Among them, R represents the set of random association results of the data pipeline, r(O p ,O q ) is represented as a data pipeline O p and O q The correlation results of O p Represented as the pth element in the data pipeline, O q represents the qth element in the data pipeline, p and q represent the index of the element in the data pipeline, It is expressed as a full quantitative operation. Then, by constructing a reward and punishment model and training the model by simulating the manual scoring method of information cores, the intelligent scoring and storage of the fused multimodal data can be realized. By automatically screening out information cores with practical significance, high-quality data information is provided for the digital integrated application system based on smart highway management and maintenance. The specific formula of the reward and punishment model is expressed as:

[0019]

[0020] Among them, Loss(Θ) represents the loss function of the reward and punishment model, Θ represents the parameter set of the reward and punishment model, x represents the data sample of the combined data unit, X represents the set of training samples, and r Θ (x) represents the prediction of the reward and punishment model for the data sample x according to the parameter set Θ, ∈ represents the regularization parameter, Represents the actual label of the data sample x, σ represents the Sigmoid function, and log represents the logarithmic function. Finally, a task-driven intelligent information extraction method is constructed. The actual task request is used as the information input. The topic model of natural language understanding is used to decompose the task request into a set of topic titles for retrieving information cores. Then, the information core set of the combined data units related to the task request is generated through random association of the data pipeline. Through the training of the constructed reward and punishment model, the intelligent sorting of the information core is realized, and the optimal matching information core is stored for a given task request. The specific formula is expressed as follows:

[0021]

[0022] Among them, Loss Request (Θ) represents the loss function of the reward and punishment model in the actual task requirement Request, and Request represents the actual task requirement. It is expressed as an expected value operation, which means that all possible task requests and information checks are expected to be performed. β Represented as the βth information core, C φ is represented as the information core of the φth node, β and φ are the indexes of the information core, D is the set of training samples, and each sample is a triple (Request, c β ,c φ ), from the pairing of task request and information core, Rank Θ (Request,c β ) and Rank Θ (Request,c φ ) respectively represent the information core c under a given task request Request β and c φ The ranking in the sorted list is determined by minimizing the loss function Loss Request (Θ) is used to optimize the model parameters Θ, so that the model can realize the intelligent and autonomous extraction of the fused multimodal data information.

[0023] Preferably, the digital integrated application module includes an intelligent analysis and application unit, which performs in-depth analysis on the processed data through the effective information of the multimodal data extracted by the data fusion processing unit to ensure the generation of valuable application solutions and provide intelligent support for highway management.

[0024] Preferably, the smart highway maintenance module includes a highway status monitoring unit, which ensures that potential problems and risks can be discovered in a timely manner by real-time monitoring of various highway status data, including road surface temperature, humidity, crack width, settlement height and traffic flow.

[0025] Preferably, the intelligent highway management and maintenance module includes a maintenance strategy optimization unit, which proposes a highway maintenance strategy optimization algorithm based on a genetic algorithm, predicts future highway maintenance needs by analyzing historical data and current status indicators, and ensures that reasonable maintenance strategies can be formulated in advance to improve the resource utilization efficiency of the highway.

[0026] Specifically, the highway maintenance strategy optimization algorithm based on genetic algorithm is as follows: First, a mathematical model of objective function is constructed to quantify the decision problem of highway maintenance, and the decision variable y is defined. iIt indicates whether the ith road is selected as the maintenance object. When the value is 1, it means that maintenance is selected, and when the value is 0, it means that no maintenance is performed. The specific formula of the objective function mathematical model is expressed as follows:

[0027]

[0028] Where F is the objective function, which aims to minimize the total maintenance cost while maximizing the maintenance quality, min is the minimization function, and c i is the maintenance cost of the ith road, which indicates the economic investment required to maintain the ith road. i is the road index, N is the total number of roads, st is the conditional function, Quality is the sum of the maintenance quality, which is used to reflect the overall quality effect of the maintenance strategy, max is the maximization function, and b i It is expressed as a measure of highway maintenance quality. The actual maintenance demand is converted into a mathematical expression solved by the algorithm through the constructed objective function mathematical model, ensuring that the model can effectively reflect the economy and efficiency of the highway maintenance strategy. Then, it provides a starting point for the iterative search process of the genetic algorithm to generate an initial solution set with sufficient diversity to ensure that the genetic algorithm can cover a wide area of ​​the solution space. The specific formula is expressed as follows:

[0029] S(0)=y1(0),y2(0),…,y P (0) = {y i (0)|i=1,2…,P}

[0030] Among them, S(0) represents the initial population, which includes the P solutions at the beginning of the genetic algorithm, y1(0), y2(0),…, y P (0) represents an independent solution in the P initial population, y i (0) represents the i-th solution in the initial population, i represents the index of the number of populations, ranging from 1 to P, P represents the population size, representing the total number of solutions in the initial population. By initializing the population, a set of initial solutions based on heuristic rules are generated. The initial solutions constitute the first generation population of the genetic algorithm. The diversity of the population directly affects the search ability of the algorithm and the quality of the final solution. The constructed good initial population helps the algorithm to effectively explore the solution space and avoid falling into the local optimal solution. Secondly, the fitness of each solution is quantitatively evaluated to determine the quality of the solution and select the solution that contributes more to the optimization goal as the candidate for subsequent iterations. The specific formula is expressed as:

[0031] f(y)=w1·Cost(y)+w2·QUL(y)

[0032] Among them, f(y) is represented by the fitness function, which is used to evaluate the overall performance of solution y, y is represented by an independent solution in the population, w1 is represented by the cost weight coefficient, which is used to adjust the influence of maintenance cost in the fitness function, Cost(y) is represented by the maintenance cost function, which represents the total maintenance cost calculated according to solution y, w2 is represented by the quality weight coefficient, which is used to adjust the influence of maintenance quality in the fitness function, QUL(y) is represented by the maintenance quality function, which represents the maintenance quality calculated according to solution y. Through the fitness function f(y), the genetic algorithm can find a balance point so that the total cost is minimized while the maintenance quality reaches a standard level. Then, through the selection mechanism, excellent individual solutions are selected from the current population according to the fitness. The excellent individual solutions will serve as the parents of the next generation of the genetic algorithm. The specific formula is expressed as follows:

[0033]

[0034] Among them, Probability(y j ) is represented as the individual solution y j The probability of being selected represents a relative measure based on individual fitness and is used in the roulette selection mechanism. j represents the jth individual solution in the current population, j represents the index of the individual solution in the current population, f(y j ) represents individual y j The fitness function of the algorithm is based on the principle of fitness selection, and the individual solution with higher fitness has a high probability of selection, so that the algorithm can select excellent solutions in the iterative process while maintaining the diversity of the overall algorithm. Finally, new genetic characteristics are introduced into the population through crossover and mutation operations to increase the diversity of the population and improve the probability of finding the global optimal solution. The specific formula is expressed as:

[0035] Offspring=Crossover(y a ,y b )

[0036] y Offspring,变异a =Mutate(y a )

[0037] y Offspring,变异b =Mutate(y b )

[0038] Among them, Offspring represents the newly generated offspring individuals, representing the result of the crossover operation, Crossover(y a ,y b ) is represented as a crossover operation, which is used to solve y from two parent individuals. a and bexchange genetic information to produce new offspring, y a With y b represents the parent individual solution, a and b represent the parent individual solution index, y Offspring,变异1 With y Offspring,变异2 It is represented as the offspring individual solution after mutation operation, Mutate(y a ) and Mutate(y b ) is expressed as a mutation operation function. New genetic features are introduced through mutation operation to prevent premature convergence of individual solutions. Crossover operation creates offspring by combining the features of two parents. Mutation operation introduces new genetic diversity by randomly changing the features of offspring. By combining crossover operation and mutation operation, the algorithm can better search the solution space and find the optimal solution to the problem.

[0039] Preferably, the data storage and management module ensures data security and availability by centrally storing and managing the collected multimodal data, while supporting the storage and retrieval of big data, providing query and analysis functions for historical data, and providing data support for the long-term management and planning of highways.

[0040] Preferably, the user monitoring and decision-making module helps managers make correct decisions by providing an intuitive user interface and decision support functions, including data visualization tools and report generation functions, while ensuring that highway managers can conveniently use the system to query data, view analysis results and receive warning information.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. Data fusion processing unit A highway information extraction algorithm based on multimodal data fusion is proposed. The algorithm realizes the fusion and intelligent extraction of multimodal data by constructing a data unit containing an information core, a strategy set and a data pipeline set. The organizational structure of each data unit includes an information core carrying key information, a strategy set including input and output strategies, data standardization strategies and mathematical logic operation strategies, and a data pipeline set for information transmission and communication, thereby ensuring efficient processing and utilization of data. First, the initial data unit is directly associated with the database. The initial information core is constructed by extracting the field name and technology name in the database, and the data pipeline is defined in combination with basic and advanced strategies to ensure the consistency and integrity of the data. Based on these initial data units, the system can effectively standardize and process the data, making the data comparable and operable between different sources. Through the data standardization strategy, the system can eliminate the deviation between different data sources and ensure the accuracy and reliability of the data. Next, through the random association data pipeline, the system generates a high-level data unit information core to achieve deep fusion of multi-source data. This method not only improves the utilization efficiency of data, but also enables the system to extract valuable information cores from a large amount of data, thereby supporting smarter decision-making and prediction. The randomly associated data pipeline can construct The system constructs a multi-combination data unit, further enriching the data hierarchy of the system and providing strong data support for smart highway maintenance. In addition, based on the task-driven intelligent information extraction method, the actual task request is decomposed into a set of subject titles for retrieving information cores through the topic model of natural language understanding, and a set of combined data unit information cores related to the task request is generated through the random association of the data pipeline. By constructing a reward and punishment model and simulating the manual scoring method, the system can realize intelligent scoring and sorting of the fused multimodal data, ensuring that the extracted information cores are of practical significance and high quality. The intelligent information extraction method can improve the response speed of the system. It also significantly enhances the flexibility and accuracy of the system in responding to complex task requests. In general, the highway information extraction algorithm based on multimodal data fusion provides strong technical support for smart highway management and maintenance by constructing data units, realizing data fusion and intelligent extraction. While ensuring efficient data processing and utilization, the algorithm significantly improves the intelligence level of the system and meets the needs for high-quality data and intelligent decision-making in highway management and maintenance. Through the highway information extraction algorithm based on multimodal data fusion, the highway smart management and maintenance system can better realize real-time monitoring, predictive maintenance and optimal resource allocation, thereby improving the management efficiency and maintenance effect of highways.

[0043] 2. The maintenance strategy optimization unit proposes a highway maintenance strategy optimization algorithm based on genetic algorithm. First, the algorithm quantifies the decision-making problem of highway maintenance by constructing a mathematical model of the objective function to ensure that the model can effectively reflect the economy and efficiency of the maintenance strategy. The objective function aims to minimize the total maintenance cost while maximizing the maintenance quality. By converting the actual maintenance needs into mathematical expressions solved by the algorithm, the decision is ensured to be more scientific and reasonable. Secondly, the initial population generation step of the genetic algorithm ensures a wide coverage of the understanding space. By generating an initial solution set with sufficient diversity, the algorithm can explore different maintenance strategies from multiple perspectives to avoid falling into local optimal solutions, thereby improving the algorithm's global search capability and the quality of the final solution. Then, the fitness evaluation mechanism quantifies the performance of each solution and selects solutions that contribute more to the optimization goal. The fitness function comprehensively considers the maintenance cost and maintenance quality, so that the algorithm can balance these two key factors during the iteration process, ensuring that both the cost can be controlled and the maintenance quality meets the requirements. In terms of the selection mechanism, the fitness-based selection principle makes individual solutions with high fitness have a higher probability of being selected. This mechanism can not only select excellent solutions, but also maintain the diversity of the population, prevent the algorithm from converging prematurely, and thus increase the probability of finding the global optimal solution. Finally, through crossover and mutation operations, new genetic characteristics are introduced to further increase the diversity of the population. The crossover operation creates offspring by combining the characteristics of two parents, and the mutation operation introduces new genetic diversity by randomly changing the characteristics of the offspring. The combination of these two operations enables the algorithm to better explore the solution space and find the optimal maintenance strategy. In summary, the highway maintenance strategy optimization algorithm based on genetic algorithm provides efficient and intelligent highway maintenance decision support through scientific modeling and optimization methods in a digital integrated application system based on highway intelligent management and maintenance, which not only improves the scientificity and rationality of decision-making, reduces maintenance costs, but also improves maintenance quality, and ultimately realizes the intelligent management of highway maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The invention is further illustrated by the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0045] Figure 1 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] See also Figure 1 The present invention provides a digital integrated application system based on intelligent highway management and maintenance, including a data acquisition and sensing module, a data transmission and communication module, a digital integrated application module, an intelligent highway management and maintenance module, a data storage and management module, and a user monitoring and decision-making module, characterized in that: the data acquisition and sensing module is used to collect highway status data in real time through a sensor network, including temperature, humidity, pressure and vibration information; the data transmission and communication module is used to transmit the collected data through a wireless network and 5G technology and communicate with a central server; the digital integrated application module includes a data fusion processing unit and an intelligent analysis and application unit; the data fusion processing unit proposes a highway information extraction algorithm based on multimodal data fusion for performing data fusion processing on multimodal data from the sensor network and intelligently extracting effective information; the intelligent analysis and application unit It is used to intelligently analyze the effective information extracted from the fused data and apply the analysis results to highway status prediction, maintenance strategy optimization and decision support. The highway intelligent management and maintenance module includes a highway status monitoring unit and a maintenance strategy optimization unit. The highway status monitoring unit is used to monitor the specific status indicators of the highway in real time, including road surface temperature, humidity, crack width, settlement height and traffic flow. The maintenance strategy optimization unit proposes a highway maintenance strategy optimization algorithm based on genetic algorithm to formulate and optimize highway maintenance strategies to improve maintenance effects and resource utilization efficiency. The data storage and management module is used to centrally store and manage the collected data and processed analysis data to ensure data security and availability. The user monitoring and decision-making module is used to provide users with a real-time monitoring interface and decision support functions, including data visualization and early warning notifications as well as decision control functions.

[0048] See also Figure 1 Furthermore, the data acquisition and sensing module collects multimodal data of the highway in real time through a sensor network constructed on the highway, including temperature sensors, humidity sensors, pressure sensors and vibration sensors, to ensure that the highway status information can be obtained in real time, providing a basis for subsequent data processing and analysis.

[0049] See also Figure 1,Furthermore, the data transmission and communication module transmits the collected data to the central server in a timely manner by utilizing wireless networks and 5G communication technology, ensuring the real-time and accuracy of the data to support immediate decision-making and response.

[0050] See also Figure 1 Furthermore, the digital integrated application module includes a data fusion processing unit, which proposes a highway information extraction algorithm based on multimodal data fusion, integrates and processes multimodal data from different sensors, ensures data consistency and integrity, and extracts effective information and utilization value of multimodal data.

[0051] See also Figure 1 ,Furthermore, the highway information extraction algorithm based on multimodal data fusion is specifically as follows: First, assuming the organizational structure of a data unit, the data unit constitutes the basic unit of the overall multimodal data fusion algorithm, each data unit contains an information core, a strategy set and a data pipeline set corresponding to the strategy, and the specific formula of the organizational structure of the data unit is expressed as:

[0052] U={C,S,O}

[0053] Among them, U represents data unit, C represents information core, S represents strategy set, O represents data pipeline set, information core C is the core component of data unit, carrying key information for data intelligence implementation, strategy set S includes input and output strategy, data standardization strategy and mathematical logic operation strategy, data pipeline O is the channel for information transmission and communication, corresponding to strategy one by one, and new information core is generated through the association of data pipeline to realize the hierarchical structure of data unit, then, the initial data unit is constructed to be directly associated with the database, including the construction of initial information core, initial strategy and initial data pipeline. The initial information core is the field name and technical name extracted from the database, which is the common attribute of data. The initial strategy set includes basic strategy and advanced strategy. The basic strategy involves data input and output, standardization and basic mathematical logic operation. The initial data pipeline corresponds to strategy one by one, and data pipeline is defined through strategy for data transmission and communication. The specific formula is expressed as follows:

[0054] U i ={C i ,S(C i ),O(C i ,S(C i ))}

[0055] Among them, U i Represented as the i-th initial data unit, C i Represented as the i-th information core, the key information extracted from the database, S(C i) is expressed as information core C i The strategy function, O(C i ,S(C i )) is represented as a data pipeline function. The data pipeline is generated according to the information core and strategy. i is represented as the index of the data unit. The specific formula of the data standardization strategy is expressed as follows:

[0056]

[0057] Among them, S norm (C i ) is represented as applied to the information core C i The data standardization strategy function, norm represents the standardization operation, Represented as information core C i The mean of Represented as information core C i The standard deviation of , secondly, randomly associate the data pipelines, and continuously generate the information core of the high-level data unit containing new information to construct the high-level data unit, and form the data pipeline set of the initial data unit through all the data pipelines of the initial data unit, and on this basis, randomly associate the data pipelines to generate the information core of the binary combination data unit. The specific formula is expressed as:

[0058]

[0059] Among them, R represents the set of random association results of the data pipeline, r(O p ,O p ) is represented as a data pipeline O p and O q The correlation results of O p Represented as the pth element in the data pipeline, O q represents the qth element in the data pipeline, p and q represent the index of the element in the data pipeline, It is expressed as a full quantitative operation. Then, by constructing a reward and punishment model and training the model by simulating the manual scoring method of information cores, the intelligent scoring and storage of the fused multimodal data can be realized. By automatically screening out information cores with practical significance, high-quality data information is provided for the digital integrated application system based on smart highway management and maintenance. The specific formula of the reward and punishment model is expressed as:

[0060]

[0061] Among them, Loss(Θ) represents the loss function of the reward and punishment model, Θ represents the parameter set of the reward and punishment model, x represents the data sample of the combined data unit, X represents the set of training samples, and r Θ(x) represents the prediction of the reward and punishment model for the data sample x according to the parameter set Θ, ∈ represents the regularization parameter, Represents the actual label of the data sample x, σ represents the Sigmoid function, and log represents the logarithmic function. Finally, a task-driven intelligent information extraction method is constructed. The actual task request is used as the information input. The topic model of natural language understanding is used to decompose the task request into a set of topic titles for retrieving information cores. Then, the information core set of the combined data units related to the task request is generated through random association of the data pipeline. Through the training of the constructed reward and punishment model, the intelligent sorting of the information core is realized, and the optimal matching information core is stored for a given task request. The specific formula is expressed as follows:

[0062]

[0063] Among them, Loss Request (Θ) represents the loss function of the reward and punishment model in the actual task requirement Request, and Request represents the actual task requirement. It is expressed as an expected value operation, which means that all possible task requests and information checks are expected to be performed. β Represented as the βth information core, C φ is represented as the information core of the φth node, β and φ are the indexes of the information core, D is the set of training samples, and each sample is a triple (Request, c β ,c φ ), from the pairing of task request and information core, Rank Θ (Request,c β ) and Rank Θ (Request,c φ ) respectively represent the information core c under a given task request Request β and c φ The ranking in the sorted list is determined by minimizing the loss function Loss Request (Θ) is used to optimize the model parameters Θ, so that the model can realize the intelligent and autonomous extraction of the fused multimodal data information.

[0064] See also Figure 1 Furthermore, the digital integrated application module includes an intelligent analysis and application unit, which performs in-depth analysis on the processed data through the effective information of the multimodal data extracted by the data fusion processing unit to ensure the generation of valuable application solutions and provide intelligent support for highway management.

[0065] See also Figure 1Furthermore, the highway intelligent management and maintenance module includes a highway status monitoring unit, which ensures that potential problems and risks can be discovered in time by real-time monitoring of various highway status data, including road surface temperature, humidity, crack width, settlement height and traffic flow.

[0066] See also Figure 1 Furthermore, the highway intelligent management and maintenance module includes a maintenance strategy optimization unit. The maintenance strategy optimization unit proposes a highway maintenance strategy optimization algorithm based on a genetic algorithm. By analyzing historical data and current status indicators, it predicts future highway maintenance needs and ensures that reasonable maintenance strategies can be formulated in advance to improve the resource utilization efficiency of the highway.

[0067] See also Figure 1 Furthermore, the highway maintenance strategy optimization algorithm based on genetic algorithm is as follows: First, a mathematical model of objective function is constructed to quantify the decision problem of highway maintenance, and the decision variable y is defined. i It indicates whether the ith road is selected as the maintenance object. When the value is 1, it means that maintenance is selected, and when the value is 0, it means that no maintenance is performed. The specific formula of the objective function mathematical model is expressed as follows:

[0068]

[0069] Where F is the objective function, which aims to minimize the total maintenance cost while maximizing the maintenance quality, min is the minimization function, and c i is the maintenance cost of the ith road, which indicates the economic investment required to maintain the ith road. i is the road index, N is the total number of roads, st is the conditional function, Quality is the sum of the maintenance quality, which is used to reflect the overall quality effect of the maintenance strategy, max is the maximization function, and b i It is expressed as a measure of highway maintenance quality. The actual maintenance demand is converted into a mathematical expression solved by the algorithm through the constructed objective function mathematical model, ensuring that the model can effectively reflect the economy and efficiency of the highway maintenance strategy. Then, it provides a starting point for the iterative search process of the genetic algorithm to generate an initial solution set with sufficient diversity to ensure that the genetic algorithm can cover a wide area of ​​the solution space. The specific formula is expressed as follows:

[0070] S(0)=y1(0),y2(0),…,y P (0) = {y i (0)|i=1,2…,P}

[0071] Among them, S(0) represents the initial population, which includes the P solutions at the beginning of the genetic algorithm, y1(0), y2(0),…, y P(0) represents an independent solution in the P initial population, y i (0) represents the i-th solution in the initial population, i represents the index of the number of populations, ranging from 1 to P, P represents the population size, representing the total number of solutions in the initial population. By initializing the population, a set of initial solutions based on heuristic rules are generated. The initial solutions constitute the first generation population of the genetic algorithm. The diversity of the population directly affects the search ability of the algorithm and the quality of the final solution. The constructed good initial population helps the algorithm to effectively explore the solution space and avoid falling into the local optimal solution. Secondly, the fitness of each solution is quantitatively evaluated to determine the quality of the solution and select the solution that contributes more to the optimization goal as the candidate for subsequent iterations. The specific formula is expressed as:

[0072] f(y)=w1·Cost(y)+w2·QUL(y)

[0073] Among them, f(y) is represented by the fitness function, which is used to evaluate the overall performance of solution y, y is represented by an independent solution in the population, w1 is represented by the cost weight coefficient, which is used to adjust the influence of maintenance cost in the fitness function, Cost(y) is represented by the maintenance cost function, which represents the total maintenance cost calculated according to solution y, w2 is represented by the quality weight coefficient, which is used to adjust the influence of maintenance quality in the fitness function, QUL(y) is represented by the maintenance quality function, which represents the maintenance quality calculated according to solution y. Through the fitness function f(y), the genetic algorithm can find a balance point so that the total cost is minimized while the maintenance quality reaches a standard level. Then, through the selection mechanism, excellent individual solutions are selected from the current population according to the fitness. The excellent individual solutions will serve as the parents of the next generation of the genetic algorithm. The specific formula is expressed as follows:

[0074]

[0075] Among them, Probability(y j ) is represented as the individual solution y j The probability of being selected represents a relative measure based on individual fitness and is used in the roulette selection mechanism. j represents the jth individual solution in the current population, j represents the index of the individual solution in the current population, f(y j ) represents individual y j The fitness function of the algorithm is based on the principle of fitness selection, and the individual solution with higher fitness has a high probability of selection, so that the algorithm can select excellent solutions in the iterative process while maintaining the diversity of the overall algorithm. Finally, new genetic characteristics are introduced into the population through crossover and mutation operations to increase the diversity of the population and improve the probability of finding the global optimal solution. The specific formula is expressed as:

[0076] Offspring=Crossover(y a ,y b )

[0077] y Offspring,变异a =Mutate(y a )

[0078] y Offspring,变异b =Mutate(y b )

[0079] Among them, Offspring represents the newly generated offspring individuals, representing the result of the crossover operation, Crossover(y a ,y b ) is represented as a crossover operation, which is used to solve y from two parent individuals. a and b exchange genetic information to produce new offspring, y a With y b represents the parent individual solution, a and b represent the parent individual solution index, y Offspring,变异1 With y Offspring,变异2 It is represented as the offspring individual solution after mutation operation, Mutate(y a ) and Mutate(y b ) is expressed as a mutation operation function. New genetic features are introduced through mutation operation to prevent premature convergence of individual solutions. Crossover operation creates offspring by combining the features of two parents. Mutation operation introduces new genetic diversity by randomly changing the features of offspring. By combining crossover operation and mutation operation, the algorithm can better search the solution space and find the optimal solution to the problem.

[0080] See also Figure 1 ,Furthermore, the data storage and management module ensures the security and availability of ,the data by centrally storing and managing the collected multimodal data, ,while supporting the storage and retrieval of big data, providing ,historical data query and analysis functions, and providing data support for the ,long-term management and planning of highways.

[0081] See also Figure 1 ,Furthermore, the user monitoring and decision making module helps managers make correct ,decisions by providing an intuitive user interface and decision support ,functions, including data visualization tools and report generation functions, while ensuring that ,highway managers can conveniently use the system for data ,query, analysis results and receive warning information.

[0082] When used specifically, first, the data acquisition and sensing module is used to collect the status data of the highway in real time through the sensor network, including temperature, humidity, pressure and vibration information. The data transmission and communication module is used to transmit the collected data through wireless networks and 5G technology and communicate with the central server. Then, the digital integrated application module includes a data fusion processing unit and an intelligent analysis and application unit. The data fusion processing unit proposes a highway information extraction algorithm based on multimodal data fusion to perform data fusion processing on the multimodal data from the sensor network and intelligently extract effective information. The intelligent analysis and application unit is used to perform intelligent analysis on the effective information extracted from the fused data and apply the analysis results to highway status prediction and maintenance strategies. The highway intelligent management and maintenance module includes a highway status monitoring unit and a maintenance strategy optimization unit. The highway status monitoring unit is used to monitor the specific status indicators of the highway in real time, including road surface temperature, humidity, crack width, settlement height and traffic flow. The maintenance strategy optimization unit proposes a highway maintenance strategy optimization algorithm based on genetic algorithm to formulate and optimize highway maintenance strategies to improve maintenance effects and resource utilization efficiency. Finally, the data storage and management module is used to centrally store and manage the collected data and processed analysis data to ensure data security and availability. The user monitoring and decision-making module is used to provide users with a real-time monitoring interface and decision support functions, including data visualization, early warning notifications and decision control functions.

[0083] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A digital integrated application system based on intelligent highway management and maintenance, including a data acquisition and sensing module, a data transmission and communication module, a digital integrated application module, an intelligent highway management and maintenance module, a data storage and management module, and a user monitoring and decision-making module, characterized in that: The data acquisition and sensing module is used to collect the status data of the highway in real time through the sensor network, including temperature, humidity, pressure and vibration information. The data transmission and communication module is used to transmit the collected data through wireless networks and 5G technology and communicate with the central server. The digital integrated application module includes a data fusion processing unit and an intelligent analysis and application unit. The data fusion processing unit proposes a highway information extraction algorithm based on multimodal data fusion for data fusion processing of multimodal data from the sensor network and intelligent extraction of effective information. The intelligent analysis and application unit is used to intelligently analyze the effective information extracted from the fused data and apply the analysis results to highway status prediction and maintenance strategy optimization. and decision support, the highway intelligent management and maintenance module includes a highway status monitoring unit and a maintenance strategy optimization unit, the highway status monitoring unit is used to monitor the specific status indicators of the highway in real time, including road surface temperature, humidity, crack width, settlement height and traffic flow, the maintenance strategy optimization unit proposes a highway maintenance strategy optimization algorithm based on genetic algorithm for formulating and optimizing highway maintenance strategies to improve maintenance effects and resource utilization efficiency, the data storage and management module is used to centrally store and manage collected data and processed analysis data to ensure data security and availability, the user monitoring and decision-making module is used to provide users with a real-time monitoring interface and decision support functions, including data visualization and early warning notifications as well as decision control functions.

2. According to claim 1, a digital integrated application system based on intelligent highway management and maintenance is characterized by: The data acquisition and sensing module collects multimodal data of the highway in real time through a sensor network constructed on the highway, including temperature sensors, humidity sensors, pressure sensors and vibration sensors, to ensure that the highway status information can be obtained in real time, providing a basis for subsequent data processing and analysis.

3. According to claim 1, a digital integrated application system based on intelligent highway management and maintenance is characterized by: The data transmission and communication module utilizes wireless networks and 5G communication technology to transmit the collected data to the central server in a timely manner, ensuring the real-time and accuracy of the data to support immediate decision-making and response.

4. According to claim 1, a digital integrated application system based on intelligent highway management and maintenance is characterized in that: The digital integrated application module includes a data fusion processing unit and an intelligent analysis and application unit. The data fusion processing unit proposes a highway information extraction algorithm based on multimodal data fusion, integrates and processes multimodal data from different sensors, ensures data consistency and integrity, and extracts effective information and utilization value of multimodal data. The intelligent analysis and application unit performs in-depth analysis on the processed data through the effective information of the multi-modal data extracted by the data fusion processing unit, ensuring the generation of valuable application solutions and providing intelligent support for highway management.

5. According to claim 4, a digital integrated application system based on intelligent highway management and maintenance is characterized by: First, assume the organizational structure of a data unit. The data unit constitutes the basic unit of the overall multimodal data fusion algorithm. Each data unit contains an information core, a strategy set, and a data pipeline set corresponding to the strategy. The specific formula of the organizational structure of the data unit is expressed as: U={C,S,O} Among them, U represents data unit, C represents information core, S represents strategy set, O represents data pipeline set, information core C is the core component of data unit, carrying key information for data intelligence implementation, strategy set S includes input and output strategy, data standardization strategy and mathematical logic operation strategy, data pipeline O is the channel for information transmission and communication, corresponding to strategy one by one, and new information core is generated through the association of data pipeline to realize the hierarchical structure of data unit, then, the initial data unit is constructed to be directly associated with the database, including the construction of initial information core, initial strategy and initial data pipeline. The initial information core is the field name and technical name extracted from the database, which is the common attribute of data. The initial strategy set includes basic strategy and advanced strategy. The basic strategy involves data input and output, standardization and basic mathematical logic operation. The initial data pipeline corresponds to strategy one by one, and data pipeline is defined through strategy for data transmission and communication. The specific formula is expressed as follows: U i ={C i ,S(C i ),O(C i ,S(C i ))} Among them, U i Represented as the i-th initial data unit, C i Represented as the i-th information core, the key information extracted from the database, S(C i ) is expressed as information core C i The strategy function, O(C i ,S(C i )) is represented as a data pipeline function. The data pipeline is generated according to the information core and strategy. i is represented as the index of the data unit. The specific formula of the data standardization strategy is expressed as follows: Among them, S norm (C i ) is applied to the information core C i The data standardization strategy function, norm represents the standardization operation, Represented as information core C i The mean of Represented as information core C i The standard deviation of , secondly, randomly associate the data pipelines, and continuously generate the information core of the high-level data unit containing new information to construct the high-level data unit, and form the data pipeline set of the initial data unit through all the data pipelines of the initial data unit, and on this basis, randomly associate the data pipelines to generate the information core of the binary combination data unit. The specific formula is expressed as: Among them, R represents the set of random association results of the data pipeline, r(O p , O q ) is represented as a data pipeline O p and O q The correlation results of O p Represented as the pth element in the data pipeline, O q represents the qth element in the data pipeline, p and q represent the index of the element in the data pipeline, It is expressed as a full quantitative operation. Then, by constructing a reward and punishment model and training the model by simulating the manual scoring method of information cores, the intelligent scoring and storage of the fused multimodal data can be realized. By automatically screening out information cores with practical significance, high-quality data information is provided for the digital integrated application system based on smart highway management and maintenance. The specific formula of the reward and punishment model is expressed as: Among them, Loss(Θ) represents the loss function of the reward and punishment model, Θ represents the parameter set of the reward and punishment model, x represents the data sample of the combined data unit, X represents the set of training samples, and r Θ (x) represents the prediction of the reward and punishment model for the data sample x according to the parameter set Θ, ∈ represents the regularization parameter, Represents the actual label of the data sample x, σ represents the Sigmoid function, and log represents the logarithmic function. Finally, a task-driven intelligent information extraction method is constructed. The actual task request is used as the information input. The topic model of natural language understanding is used to decompose the task request into a set of topic titles for retrieving information cores. Then, the information core set of the combined data units related to the task request is generated through random association of the data pipeline. Through the training of the constructed reward and punishment model, the intelligent sorting of the information core is realized, and the optimal matching information core is stored for a given task request. The specific formula is expressed as follows: Among them, Loss Request (Θ) represents the loss function of the reward and punishment model in the actual task requirement Request, and Request represents the actual task requirement. It is expressed as an expected value operation, which means that all possible task requests and information checks are expected to be performed. β Represented as the βth information core, C φ is represented as the information core of the φth node, β and φ are the indexes of the information core, D is the set of training samples, and each sample is a triple (Request, c β , c φ ), from the pairing of task request and information core, Rank Θ (Request,c β ) and Rank Θ (Request,c φ ) respectively represent the information core c under a given task request Request β and c φ The ranking in the sorted list is determined by minimizing the loss function Loss Request (Θ) is used to optimize the model parameters Θ, so that the model can realize the intelligent and autonomous extraction of the fused multimodal data information.

6. According to claim 1, a digital integrated application system based on intelligent highway management and maintenance is characterized in that: The highway intelligent management and maintenance module includes a highway status monitoring unit and a maintenance strategy optimization unit. The highway status monitoring unit ensures that potential problems and risks can be discovered in time by real-time monitoring of various highway status data, including road surface temperature, humidity, crack width, settlement height and traffic flow; the maintenance strategy optimization unit proposes a highway maintenance strategy optimization algorithm based on genetic algorithm, which predicts future highway maintenance needs by analyzing historical data and current status indicators, ensuring that reasonable maintenance strategies can be formulated in advance to improve the resource utilization efficiency of the highway.

7. A digital integrated application system based on intelligent highway management and maintenance according to claim 6, characterized in that: First, an objective function mathematical model is constructed to quantify the decision-making problem of highway maintenance. The decision variable yi is defined to indicate whether the ith highway is selected as the maintenance object. When the value is 1, it means maintenance is selected, and when the value is 0, it means no maintenance. The specific formula of the objective function mathematical model is expressed as follows: Where F is the objective function, which aims to minimize the total maintenance cost while maximizing the maintenance quality, min is the minimization function, and c i is the maintenance cost of the ith road, which indicates the economic investment required to maintain the ith road. i is the road index, N is the total number of roads, st is the conditional function, Quality is the sum of the maintenance quality, which is used to reflect the overall quality effect of the maintenance strategy, max is the maximization function, and b i It is expressed as a measure of highway maintenance quality. The actual maintenance demand is converted into a mathematical expression solved by the algorithm through the constructed objective function mathematical model, ensuring that the model can effectively reflect the economy and efficiency of the highway maintenance strategy. Then, it provides a starting point for the iterative search process of the genetic algorithm to generate an initial solution set with sufficient diversity to ensure that the genetic algorithm can cover a wide area of ​​the solution space. The specific formula is expressed as follows: S(0)=y1(0),y2(0),...,y P (0)={y i (0)|i=1,2…,P} Among them, S(0) represents the initial population, which contains the P solutions at the beginning of the genetic algorithm, y1(0), y2(0), ..., y P (0) represents an independent solution in the P initial population, y i (0) represents the i-th solution in the initial population, i represents the index of the number of populations, ranging from 1 to P, P represents the population size, representing the total number of solutions in the initial population. By initializing the population, a set of initial solutions based on heuristic rules are generated. The initial solutions constitute the first generation population of the genetic algorithm. The diversity of the population directly affects the search ability of the algorithm and the quality of the final solution. The constructed good initial population helps the algorithm to effectively explore the solution space and avoid falling into the local optimal solution. Secondly, the fitness of each solution is quantitatively evaluated to determine the quality of the solution and select the solution that contributes more to the optimization goal as the candidate for subsequent iterations. The specific formula is expressed as: f(y)=w1·Cost(y)+w2·QUL(y) Among them, f(y) is represented by the fitness function, which is used to evaluate the overall performance of solution y, y is represented by an independent solution in the population, w1 is represented by the cost weight coefficient, which is used to adjust the influence of maintenance cost in the fitness function, Cost(y) is represented by the maintenance cost function, which represents the total maintenance cost calculated according to solution y, w2 is represented by the quality weight coefficient, which is used to adjust the influence of maintenance quality in the fitness function, QUL(y) is represented by the maintenance quality function, which represents the maintenance quality calculated according to solution y. Through the fitness function f(y), the genetic algorithm can find a balance point so that the total cost is minimized while the maintenance quality reaches a level that meets the standards. Then, through the selection mechanism, excellent individual solutions are selected from the current population according to the fitness. The excellent individual solutions will serve as the parents of the next generation of the genetic algorithm. The specific formula is expressed as follows: Among them, Probability(y j ) is represented as the individual solution y j The probability of being selected represents a relative measure based on individual fitness and is used in the roulette selection mechanism. j represents the jth individual solution in the current population, j represents the index of the individual solution in the current population, f(y j ) represents individual y j The fitness function of the algorithm is based on the principle of fitness selection, and the individual solution with higher fitness has a high probability of selection, so that the algorithm can select excellent solutions in the iterative process while maintaining the diversity of the overall algorithm. Finally, new genetic characteristics are introduced into the population through crossover and mutation operations to increase the diversity of the population and improve the probability of finding the global optimal solution. The specific formula is expressed as: Offspring=Crossover(y a ,y b ) and Offspring,变异a =Mutate(and a ) and Offspring,，变异b =Mutate(and b ) Among them, Offspring represents the newly generated offspring individuals, representing the result of the crossover operation, Crossover(y a ,y b ) is represented as a crossover operation, which is used to solve y from two parent individuals. a and b exchange genetic information to produce new offspring, y a With y b represents the parent individual solution, a and b represent the parent individual solution index, y Offspring,变异1 With y Offspring,变异2 It is represented as the offspring individual solution after mutation operation, Mutate(y a ) and Mutate(y b ) is expressed as a mutation operation function. New genetic features are introduced through mutation operation to prevent premature convergence of individual solutions. Crossover operation creates offspring by combining the features of two parents. Mutation operation introduces new genetic diversity by randomly changing the features of offspring. By combining crossover operation and mutation operation, the algorithm can better search the solution space and find the optimal solution to the problem.

8. According to claim 1, a digital integrated application system based on intelligent highway management and maintenance is characterized in that: The data storage and management module ensures data security and availability by centrally storing and managing the collected multimodal data, while supporting the storage and retrieval of big data, providing historical data query and analysis functions, and providing data support for the long-term management and planning of highways.

9. According to claim 1, a digital integrated application system based on intelligent highway management and maintenance is characterized in that: The user monitoring and decision-making module helps managers make correct decisions by providing an intuitive user interface and decision support functions, including data visualization tools and report generation functions, while ensuring that highway managers can easily use the system to query data, view analysis results and receive warning information.

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