Highway maintenance decision management method based on multi-source data analysis

Abstract

The invention belongs to the technical field of expressway maintenance management, and provides an expressway maintenance decision management method based on multi-source data analysis, which comprises the following steps: S1, acquiring a monitoring video clip in a video monitoring system of an expressway; s2, the expressway images obtained through extraction are screened; s3, performing splicing processing on the expressway image set by using an image splicing method; s4, the maintenance demand of the expressway is judged through the expressway maintenance demand analysis model; s5, according to big data analysis, obtaining duration required by highway maintenance; according to the invention, through integration of the video monitoring system, historical maintenance data, pavement performance detection data, meteorological data and other multi-source data of the expressway, accurate judgment of expressway maintenance demands and accurate prediction of maintenance duration are realized by using an advanced data analysis model and algorithm; therefore, a more scientific and reasonable maintenance plan is made.

Description

Technical Field

[0001] The present invention belongs to the technical field of highway maintenance management, and specifically relates to a highway maintenance decision-making management method based on multi-source data analysis. Background Art

[0002] In highway maintenance management, traditional methods often rely on manual inspections and experience-based judgments. This approach is not only inefficient but also difficult to comprehensively and accurately evaluate the maintenance needs of highways. With the continuous development of information technology, although some maintenance management systems based on a single data source have emerged, these systems often provide only limited information and cannot meet the complex requirements of highway maintenance management. In particular, these systems usually ignore the correlation and complementarity between multi-source data, resulting in the need to improve the scientificity and accuracy of maintenance decisions.

[0003] Therefore, those skilled in the art have proposed a highway maintenance decision-making management method based on multi-source data analysis to solve the problems raised in the background art. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a highway maintenance decision-making management method based on multi-source data analysis to solve the problems in the prior art that the system often provides only limited information and cannot meet the complex requirements of highway maintenance management. In particular, these systems usually ignore the correlation and complementarity between multi-source data, resulting in the need to improve the scientificity and accuracy of maintenance decisions.

[0005] A highway maintenance decision-making management method based on multi-source data analysis includes:

[0006] S1. Obtain surveillance video clips from the video surveillance system of the highway itself, and extract highway images through frame extraction;

[0007] S2. Screen the extracted highway images to generate a highway image set;

[0008] S3. Use the image stitching method to stitch the highway image set to obtain a complete highway image and an original complete highway image;

[0009] S4. Judge the maintenance needs of the highway through a highway maintenance requirement analysis model;

[0010] S5. Obtain the duration required for highway maintenance based on big data analysis;

[0011] S6. Analyze the historical traffic flow of the path in the complete highway image using the video surveillance system of the highway itself;

[0012] S7. Determine the impact value of the maintenance project on traffic based on the historical traffic flow and maintenance project of the path, and make a reminder decision on the maintenance time of the highway.

[0013] Preferably, in step S2, the image screening step includes removing blurred, ghosted or occluded images. In order to more precisely remove blurred, ghosted or occluded images, an image sharpness evaluation formula is used.

[0014] Preferably, in step S3, the image stitching method adopts a feature point-based image registration algorithm to improve the accuracy and efficiency of stitching.

[0015] Preferably, in step S4, the highway maintenance demand analysis model is constructed based on historical maintenance data, pavement performance detection data and meteorological data.

[0016] Preferably, in step S5, the big data analysis includes comprehensive analysis of maintenance cost, maintenance effect and traffic flow data. In the big data analysis step, in order to predict the required duration, maintenance cost and maintenance effect of highway maintenance, a time series analysis prediction model is used.

[0017] Preferably, in step S7, when evaluating the impact value of the maintenance project on traffic, a weighted summation formula is used to evaluate the impact value.

[0018] Preferably, in step S7, a multi-objective optimization model is adopted when making a reminder decision on the maintenance time of the highway to find the optimal solution.

[0019] A highway maintenance decision management system based on multi-source data analysis, using the above-mentioned highway maintenance decision management method based on multi-source data analysis, includes:

[0020] A video acquisition module, configured to acquire video monitoring segments in the highway's own video monitoring system and obtain highway images through frame extraction;

[0021] An image screening module, configured to screen the extracted highway images, remove blurred, ghosted or occluded images, and generate a highway image set. Among them, the image screening module uses an image sharpness evaluation formula for screening;

[0022] An image stitching module, configured to perform stitching processing on the highway image set by using an image stitching method to obtain a complete highway image and an original complete highway image. Among them, the image stitching method adopts a feature point-based image registration algorithm to improve the accuracy and efficiency of stitching;

[0023] Maintenance requirement analysis module, which is used to judge the maintenance requirements of the expressway through the expressway maintenance requirement analysis model. Among them, the expressway maintenance requirement analysis model is constructed based on historical maintenance data, pavement performance detection data and meteorological data;

[0024] Duration analysis module, which is used to obtain the duration required for expressway maintenance based on big data analysis. Among them, the big data analysis includes comprehensive analysis of maintenance costs, maintenance effects and traffic flow data, and uses a time series analysis prediction model for prediction;

[0025] Traffic flow analysis module, which is used to analyze the historical traffic flow of the path in the complete image of the expressway by using the expressway's own video monitoring system;

[0026] Impact value evaluation module, which is used to judge the impact value of the maintenance project on traffic based on the historical traffic flow of the path and the maintenance project, and uses a weighted summation formula for evaluation;

[0027] Decision reminder module, which is used to make a reminder decision on the maintenance time of the expressway, and uses a multi-objective optimization model to find the optimal solution.

[0028] A processor configured to execute a method for expressway maintenance decision management based on multi-source data analysis as described above.

[0029] A computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements a method for expressway maintenance decision management based on multi-source data analysis as described above.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. By integrating multi-source data such as the expressway's own video monitoring system, historical maintenance data, pavement performance detection data and meteorological data, the present invention uses advanced data analysis models and algorithms to accurately judge the maintenance requirements of the expressway and accurately predict the maintenance duration, so as to formulate a more scientific and reasonable maintenance plan.

[0032] 2. By accurately judging the maintenance requirements and predicting the maintenance duration, the present invention can help managers reasonably arrange maintenance personnel and materials, avoid waste of resources and improve maintenance efficiency.

[0033] 3. The present invention uses the expressway's own video monitoring system to analyze the historical traffic flow of the path, and combines the maintenance project to judge the impact value of the maintenance project on traffic, so as to provide decision-making support for managers and reduce the adverse impact of the maintenance project on highway traffic safety and traffic efficiency.

[0034] 4. The implementation of the present invention can not only improve the accuracy and scientific nature of maintenance decision-making, but also promote the development of highway maintenance management towards intelligence and automation, providing strong support for the long-term sustainable development of highways. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a flowchart of a highway maintenance decision-making management method based on multi-source data analysis according to the present invention;

[0036] Figure 2 is a framework diagram of a highway maintenance decision-making management system based on multi-source data analysis according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The following further describes in detail the embodiments of the present invention with reference to the drawings and examples. The following examples are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0038] Example: The present invention provides a highway maintenance decision-making management method based on multi-source data analysis, as Figure 1 shown, including:

[0039] S1. Obtain monitoring video segments in the video monitoring system of the highway itself, and extract highway images through frame extraction;

[0040] S2. Screen the extracted highway images to generate a highway image set;

[0041] S3. Use the image stitching method to stitch the highway image set to obtain a complete highway image and an original complete highway image;

[0042] S4. Judge the maintenance requirements of the highway through a highway maintenance requirement analysis model;

[0043] S5. Obtain the duration required for highway maintenance based on big data analysis;

[0044] S6. Analyze the historical traffic flow of the path in the complete highway image using the video monitoring system of the highway itself;

[0045] S7. Judge the impact value of the maintenance project on traffic based on the historical traffic flow of the path and the maintenance project, and make a reminder decision on the maintenance time of the highway.

[0046] As can be seen from the above, by integrating multi-source data such as the highway's own video surveillance system, historical maintenance data, pavement performance detection data, and meteorological data, and using advanced data analysis models and algorithms, the accurate judgment of highway maintenance needs and the accurate prediction of maintenance duration are realized, thus formulating a more scientific and reasonable maintenance plan; it can not only improve the accuracy and scientificity of maintenance decisions, but also promote the development of highway maintenance management towards the direction of intelligence and automation, help managers reasonably arrange maintenance personnel and materials, avoid resource waste, improve maintenance efficiency, and reduce the adverse impact of maintenance projects on highway traffic safety and traffic efficiency, providing strong support for the long-term sustainable development of highways.

[0047] Further, in step S2, the image screening step includes removing blurred, ghosted, or occluded images. In order to more precisely remove blurred, ghosted, or occluded images, an image sharpness evaluation formula is used. The image sharpness evaluation formula includes:

[0048] L(I) = ΣΣ|I(x,y) - I(x + 1,y) - I(x,y + 1) + I(x + 1,y + 1)|;

[0049] where I(x,y) represents the pixel value of the image at the coordinate (x,y).

[0050] As can be seen from the above, by using the image sharpness evaluation formula to screen and remove blurred, ghosted, or occluded images, the quality of highway images used can be more precisely ensured; by quantifying the pixel values of each coordinate point in the image and calculating according to the sharpness evaluation formula, images that cannot accurately reflect the actual situation of the highway due to quality problems can be effectively identified and removed; it not only improves the accuracy of subsequent image stitching and maintenance needs analysis, but also helps to reduce misjudgments and waste of maintenance resources caused by image quality problems, thus further enhancing the scientificity and effectiveness of highway maintenance decisions.

[0051] Further, in step S3, the image stitching method adopts a feature-point-based image registration algorithm to improve the accuracy and efficiency of stitching. The objective of this image registration algorithm is to minimize the matching error E between feature points:

[0052]

[0053] where, p i and q i respectively represent the matching feature points in two images, and H represents the transformation matrix from one image to another.

[0054] As can be seen from the above, when using the feature point-based image registration algorithm for image stitching, by minimizing the matching error E between feature points, the accuracy and efficiency of image stitching can be significantly improved. This algorithm precisely matches the feature points in two images and uses a transformation matrix to map the feature points in one image to the other image, thereby achieving precise alignment between the images. This not only helps generate a more complete and accurate highway image, providing strong support for subsequent maintenance requirement analysis, but also improves the automation level of image stitching, reduces manual intervention and errors, and further enhances the efficiency and accuracy of highway maintenance decision-making.

[0055] Furthermore, in step S4, the highway maintenance requirement analysis model is constructed based on historical maintenance data, pavement performance detection data, and meteorological data, and its formula is as follows:

[0056] Y = β0 + β1X1 + β2X2 +... + β n X n + ε;

[0057] where, X1, X2,..., X n represent various input variables (such as historical maintenance data, pavement performance detection data, meteorological data, etc.), β0, β1,..., β n represent regression coefficients, and ε represents the error term.

[0058] As can be seen from the above, constructing a highway maintenance requirement analysis model based on historical maintenance data, pavement performance detection data, and meteorological data can comprehensively and accurately reflect the maintenance requirements of highways. By comprehensively considering various input variables and using regression coefficients for quantitative analysis, this model effectively captures the complex relationships between highway maintenance requirements and various influencing factors. This not only improves the accuracy and scientific nature of maintenance requirement analysis, but also helps managers more accurately grasp the maintenance status of highways, thereby formulating more targeted maintenance plans. At the same time, this model also has a certain predictive ability, which can provide strong support for future maintenance decision-making and further enhance the efficiency and effect of highway maintenance management.

[0059] Furthermore, in step S5, the big data analysis includes a comprehensive analysis of maintenance costs, maintenance effects, and traffic flow data. In the big data analysis step, in order to predict the duration, cost, and effect of highway maintenance, a time series analysis prediction model is used, and the formula of the time series analysis prediction model is as follows:

[0060]

[0061] where, φ and θ represent the parameters of the time series analysis prediction model, B represents the lag operator, d represents the order of differencing, Xt represents time - series data, ε t represents a white - noise sequence.

[0062] As can be seen from the above, by comprehensively analyzing the maintenance cost, maintenance effect, and traffic - flow data, and combining with the time - series analysis prediction model, the required duration, maintenance cost, and maintenance effect of highway maintenance can be accurately predicted; the time - series analysis prediction model fully considers the time - series characteristics of the data, and effectively captures the dynamic relationships and changing trends among the data through parameter estimation and the application of lag operators; this not only improves the accuracy and reliability of the prediction, but also helps managers formulate maintenance plans and budgets more scientifically, thereby optimizing resource allocation, reducing maintenance costs, and enhancing maintenance effects; at the same time, this prediction model can also provide strong support for the long - term maintenance management of highways, promoting the development of maintenance decision - making towards a more accurate and efficient direction.

[0063] Furthermore, in step S7, when evaluating the impact value of the maintenance project on traffic, a weighted - summation formula is used to evaluate the impact value, and the weighted - summation formula includes:

[0064]

[0065] where w1, w2, w3,... represent the weight coefficients of each factor, represents the historical traffic flow, represents the duration of the maintenance project, represents the scale of the maintenance project, etc.

[0066] As can be seen from the above, using the weighted - summation formula to evaluate the impact value of the maintenance project on traffic can comprehensively consider multiple key factors, such as historical traffic flow, the duration of the maintenance project, and the scale of the maintenance project, etc., and make reasonable quantification according to the weight coefficients of each factor; this evaluation method not only improves the comprehensiveness and accuracy of the impact - value evaluation, but also helps managers more clearly understand the specific impact of the maintenance project on traffic, thereby formulating a more reasonable traffic - diversion and maintenance - time arrangement plan; by accurately evaluating the impact value, managers can better balance the relationship between the maintenance project and traffic demand, ensuring the efficient implementation of the maintenance project on the premise of ensuring traffic safety and passing efficiency.

[0067] Furthermore, in step S7, when making a reminder decision on the maintenance time of the highway, a multi - objective optimization model is adopted to find the optimal solution, and the formula of the multi - objective optimization model is as follows:

[0068] F(x)=[f1(x),f2(x),...,f k (x)];

[0069] Among them, F(x) is the objective function vector, and f i (x) is the i-th objective function, and x is the decision variable vector.

[0070] As can be seen from the above, a multi-objective optimization model is used to make a reminder decision on the maintenance time of the highway. By finding the optimal solution, multiple conflicting objective functions are balanced, such as the urgency of the maintenance project requirements, minimizing the impact on traffic flow, and the cost-effectiveness of maintenance; this method can comprehensively consider multiple key factors and formulate a more scientific and reasonable maintenance time arrangement plan; the multi-objective optimization model not only improves the comprehensiveness and accuracy of the decision-making, but also helps managers make the best choice in a complex and changeable maintenance environment, so as to maximize the maintenance benefits while ensuring the safe and unobstructed operation of the highway and realizing the optimal allocation and efficient utilization of resources.

[0071] Furthermore, the effects of a highway maintenance decision management method based on multi-source data analysis in the embodiment are compared with the current traditional highway maintenance management method (comparative example), and the following table is obtained:

[0072]

[0073]

[0074] As can be seen from the above table, through the above comparison table, it can be seen that the highway maintenance decision management method based on multi-source data analysis is superior to the traditional highway maintenance management method in many aspects and can significantly improve the efficiency and effect of highway maintenance management.

[0075] A highway maintenance decision management system based on multi-source data analysis, as Figure 2 shown, uses the above-mentioned highway maintenance decision management method based on multi-source data analysis, including:

[0076] A video acquisition module, which is used to acquire video monitoring segments in the highway's own video monitoring system and obtain highway images through frame extraction;

[0077] An image screening module, which is used to screen the extracted highway images, remove blurred, ghosted or occluded images, and generate a highway image set. Among them, the image screening module uses an image clarity evaluation formula for screening;

[0078] An image stitching module, which is used to perform stitching processing on the highway image set by using the image stitching method to obtain a complete highway image and an original complete highway image. Among them, the image stitching method uses a feature point-based image registration algorithm to improve the accuracy and efficiency of stitching;

[0079] Maintenance requirement analysis module, which is used to judge the maintenance requirements of the expressway through the expressway maintenance requirement analysis model. Among them, the expressway maintenance requirement analysis model is constructed based on historical maintenance data, pavement performance detection data and meteorological data;

[0080] Duration analysis module, which is used to obtain the duration required for expressway maintenance based on big data analysis. Among them, the big data analysis includes comprehensive analysis of maintenance costs, maintenance effects and traffic flow data, and uses a time series analysis prediction model for prediction;

[0081] Traffic flow analysis module, which is used to analyze the historical traffic flow of the paths in the complete image of the expressway by using the expressway's own video monitoring system;

[0082] Impact value evaluation module, which is used to judge the impact value of the maintenance project on traffic based on the historical traffic flow of the path and the maintenance project, and uses a weighted summation formula for evaluation;

[0083] Decision reminder module, which is used to make a reminder decision on the maintenance time of the expressway, and uses a multi-objective optimization model to find the optimal solution.

[0084] Working principle: By integrating multi-source information such as the expressway's own video monitoring system, historical maintenance data, pavement performance detection data and meteorological data, and using image processing and data analysis technologies, such as image stitching method, expressway maintenance requirement analysis model, time series analysis prediction model, weighted summation formula and multi-objective optimization model, etc., accurately judge the maintenance requirements of the expressway, accurately predict the maintenance duration, and evaluate the impact of the maintenance project on traffic. Finally, through the decision reminder module, provide a scientific and reasonable maintenance time arrangement plan for managers, and realize the intelligentization, automation and high efficiency of expressway maintenance management.

[0085] An embodiment of the present application provides an electronic device, which is applicable to the above-mentioned expressway maintenance decision management method based on multi-source data analysis, and includes:

[0086] A memory, which is used to protect computer programs and data;

[0087] A processor, which is used to run the system program.

[0088] An embodiment of the present application provides a computer storage medium, which is applicable to the above-mentioned expressway maintenance decision management method based on multi-source data analysis, and classifies and secures the above system and data according to the requirements of confidentiality management.

[0089] Those skilled in the art should understand that the embodiments of the present application can be provided as a system or a computer program product. Therefore, the present application can take the form of an all-hardware embodiment, an all-software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0090] The present application is described with reference to the flowcharts and / or block diagrams of devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0091] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0092] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0093] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0094] The memory may include non-permanent memory in the computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

[0095] A computer-readable medium includes both permanent and non-permanent, removable and non-removable media, and can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0096] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, article or device comprising the element.

[0097] The embodiments of the present invention are given for purposes of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A highway maintenance decision-making management method based on multi-source data analysis, characterized in that, Including: S1. Obtain the monitored video clips in the video monitoring system of the expressway itself, and extract the expressway images through frame extraction; S2. Screen the extracted expressway images to generate an expressway image set; S3. Use the image stitching method to stitch the expressway image set to obtain the complete expressway image and the original complete expressway image; S4. Judge the maintenance requirements of the expressway through the expressway maintenance requirement analysis model; S5. Obtain the required duration for expressway maintenance based on big data analysis; S6. Use the video monitoring system of the expressway itself to analyze the historical traffic flow of the path in the complete expressway image; S7. Based on the historical traffic flow of the path and the maintenance project, judge the influence value of the maintenance project on traffic, and make a reminder decision on the maintenance time of the expressway.

2. The method for highway maintenance decision-making management based on multi-source data analysis according to claim 1, characterized in that: In step S2, the image screening step includes removing blurred, ghosted or occluded images.

3. The method for highway maintenance decision-making management based on multi-source data analysis according to claim 1, characterized in that: In step S3, the image stitching method uses a feature point-based image registration algorithm.

4. The method for highway maintenance decision-making management based on multi-source data analysis according to claim 1, characterized in that: In step S4, the expressway maintenance requirement analysis model is constructed based on historical maintenance data, pavement performance detection data and meteorological data.

5. The method for highway maintenance decision-making management based on multi-source data analysis according to claim 1, characterized in that: In step S5, the big data analysis includes comprehensive analysis of maintenance costs, maintenance effects and traffic flow data. In the big data analysis step, in order to predict the required duration, maintenance costs and maintenance effects of expressway maintenance, a time series analysis prediction model is used.

6. The method for highway maintenance decision-making management based on multi-source data analysis according to claim 1, characterized in that: In step S7, when evaluating the influence value of the maintenance project on traffic, a weighted summation formula is used to evaluate the influence value.

7. The method for highway maintenance decision-making management based on multi-source data analysis according to claim 1, characterized in that: In step S7, a multi-objective optimization model is used when making a reminder decision on the maintenance time of the expressway.

8. A highway maintenance decision-making management system based on multi-source data analysis, characterized in that, Using the method for expressway maintenance decision management based on multi-source data analysis according to any one of claims 1-7, including: A video acquisition module, configured to obtain the monitored video clips in the video monitoring system of the expressway itself, and extract the expressway images through frame extraction; An image screening module, configured to screen the extracted expressway images, remove blurred, ghosted or occluded images, and generate an expressway image set. Among them, the image screening module uses an image clarity evaluation formula for screening; An image stitching module, configured to use the image stitching method to stitch the expressway image set to obtain the complete expressway image and the original complete expressway image. Among them, the image stitching method uses a feature point-based image registration algorithm to improve the accuracy and efficiency of stitching; A maintenance requirement analysis module, configured to judge the maintenance requirements of the expressway through the expressway maintenance requirement analysis model. Among them, the expressway maintenance requirement analysis model is constructed based on historical maintenance data, pavement performance detection data and meteorological data; A duration analysis module, configured to obtain the required duration for expressway maintenance based on big data analysis. Among them, the big data analysis includes comprehensive analysis of maintenance costs, maintenance effects and traffic flow data, and a time series analysis prediction model is used for prediction; A traffic flow analysis module for analyzing the historical traffic flow of paths in the complete image of an expressway by using the video monitoring system of the expressway itself; An impact value evaluation module for judging the impact value of a maintenance project on traffic based on the historical traffic flow of the path and the maintenance project, and using a weighted summation formula for evaluation; A decision reminder module for making a reminder decision on the maintenance time of the expressway, and adopting a multi-objective optimization model to find the optimal solution.

9. A processor, characterized in that: Configured to execute a highway maintenance decision management method based on multi-source data analysis according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, it implements a highway maintenance decision management method based on multi-source data analysis according to any one of claims 1 to 7.

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