Construction site transportation path optimization method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of engineering, and discloses a construction site transportation path optimization method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining map data and transportation task data; compressing the map data by using an adaptive data compression algorithm to obtain compressed map data; obtaining a path optimization request, and after the path optimization request is received, decompressing the compressed map data to obtain map data; based on the transportation task data and the map data, performing path search and optimization on the map data to obtain an initial optimal transportation path; and acquiring real-time preset area environment data, and updating the initial optimal transportation path based on the real-time preset area environment data to obtain a target optimal transportation path. According to the method, resources required by data storage and transmission are remarkably reduced, and the overall working efficiency of path optimization is improved. According to the real-time special environment of the construction site, the transportation path is dynamically adjusted, and a more accurate and more reliable path optimization scheme is provided.

Description

Technical Field

[0001] The present invention relates to the field of engineering technology, and in particular to a method, device, computer equipment and storage medium for optimizing a construction site transportation path. Background Art

[0002] In the field of construction engineering, technology is developing rapidly to optimize site transportation routes and cost calculations, especially through innovative map data compression technology, which can significantly reduce data storage requirements and speed up processing. At the same time, combined with advanced path optimization algorithms and real-time traffic data integration, transportation efficiency and decision-making quality are improved. In addition, intuitive user interface design, enhanced data security and privacy protection, and support for multi-platform compatibility are all key factors driving technological progress in the industry.

[0003] In the related art, the amount of map data required for the construction site transportation path is large, and a lot of resources are needed during storage and transmission. The map data used for path optimization is not real-time enough and difficult to adapt to changes in the construction site environment. At the same time, the path optimization algorithm is also difficult to adapt to the rapid changes in the construction site environment, such as temporary construction changes or traffic conditions. It lacks sufficient flexibility and adaptability, and thus cannot provide the optimal transportation path, resulting in inaccurate or outdated path optimization results. Summary of the invention

[0004] In view of this, the present invention provides a construction site transportation path optimization method, device, computer equipment and storage medium to solve the problem of inaccurate or outdated path optimization results.

[0005] In a first aspect, the present invention provides a method for optimizing a construction site transportation path, the method comprising:

[0006] Obtain map data and transportation mission data;

[0007] Using an adaptive data compression algorithm to compress the map data to obtain compressed map data;

[0008] Obtaining a path optimization request, and after receiving the path optimization request, decompressing the compressed map data to obtain the map data;

[0009] Based on the transportation task data and map data, the map data is searched and optimized to obtain the initial optimal transportation path;

[0010] Collect real-time preset regional environmental data, and based on the real-time preset regional environmental data, update the initial optimal transportation path to obtain the target optimal transportation path.

[0011] In the present invention, by using a data compression algorithm to compress map data, and decompressing it after receiving a path optimization request, the resources required for data storage and transmission are significantly reduced, which not only reduces the hardware requirements of the system, but also speeds up the loading and processing speed of map data, thereby improving the overall work efficiency of path optimization. The optimal transportation path is obtained by using the decompressed map data for path search, and the transportation path is optimized according to the real-time special environment of the construction site, such as ice areas, windy areas, pollution areas and other special areas, and the transportation path is dynamically adjusted according to the real-time special area data, providing a more accurate and reliable path optimization solution, more comprehensively considering the construction site environmental factors, and the optimization results are more in line with the actual transportation needs, achieving real-time response to changes in the construction site environment and traffic conditions, and dynamically adjusting the transportation path. The real-time nature of path optimization ensures that the transportation path is always the optimal transportation path.

[0012] In an optional implementation, the map data is compressed using an adaptive data compression algorithm to obtain compressed map data, including:

[0013] Preprocessing the map data to obtain preprocessed map data;

[0014] Using a dictionary-based compression algorithm, a terrain feature dictionary is constructed in the preprocessed map data, and the preprocessed map data is encoded using the terrain feature dictionary to obtain initial compressed map data;

[0015] The initial compressed map data is compressed twice using Huffman coding to obtain compressed map data.

[0016] In this method, by preprocessing the map data, using a dictionary-based compression algorithm to build a terrain feature dictionary and using the terrain feature dictionary for encoding, and using Huffman coding for secondary compression, the compression rate is further improved, the amount of compressed map data is lower, the burden on the system is further reduced, the speed and efficiency of map data processing are improved, and it is convenient to further optimize the storage structure.

[0017] In an optional implementation, the compressed map data is decompressed to obtain the map data, including:

[0018] Use multi-threading technology to decompress the compressed map data in parallel;

[0019] A caching strategy is adopted to cache the map data whose appearance times in the compressed map data are greater than a preset threshold, and the map data is restored.

[0020] In this way, after receiving the path optimization request, multi-threading technology is used to parallel decompress and quickly restore the original map data. The cache strategy is used to cache frequently accessed data, reducing the computational cost of repeated decompression and further improving the speed and efficiency of map data processing.

[0021] In an optional implementation, based on the transportation task data and the map data, path search and optimization are performed on the map data to obtain an initial optimal transportation path, including:

[0022] The path optimization engine is used to calculate the initial optimal transportation path based on the transportation task data and map data, combined with the real-time data of the preset area. The path optimization engine includes the improved Dijkstra algorithm and the A* algorithm.

[0023] In this method, the optimal transportation path is calculated by utilizing a path optimization engine including an improved Dijkstra algorithm, and the cost of each path node is predicted, thereby further accelerating the path search speed and improving the user experience.

[0024] In an optional implementation, based on real-time preset regional environmental data, the initial optimal transportation path is updated to obtain the target optimal transportation path, including:

[0025] Based on the real-time preset regional environment data, the parameters and weights of the path optimization engine are adjusted to obtain the path optimization engine corresponding to the current environment;

[0026] Utilize the path optimization engine corresponding to the current environment to calculate the optimal transportation path for the target.

[0027] In this method, the weight of the algorithm is dynamically adjusted by combining the influence of real-time special areas, such as ice areas, windy areas, and polluted areas, thereby achieving dynamic adjustment of the transportation path according to the real-time construction site environment, ensuring that the calculated target final transportation path is the optimal transportation path under any circumstances.

[0028] In an optional embodiment, the method further includes:

[0029] Determine whether the current user's user role is authorized;

[0030] When the user role of the current user is authorized, the target optimal transportation path is displayed to the current user.

[0031] In this method, by determining whether the user's role is authorized, sensitive data such as the transport route is only displayed to the authorized user role, ensuring that only authorized users can access sensitive data, further ensuring the security of the system.

[0032] In a second aspect, the present invention provides a construction site transportation path optimization device, the device comprising:

[0033] Data acquisition module, used to obtain map data and transportation task data;

[0034] A data compression module, used to compress the map data using an adaptive data compression algorithm to obtain compressed map data;

[0035] A data decompression module is used to obtain a path optimization request, and after receiving the path optimization request, decompress the compressed map data to obtain the map data;

[0036] The path optimization module is used to search and optimize the path of the map data based on the transportation task data and the map data to obtain the initial optimal transportation path;

[0037] The path adjustment module is used to collect real-time preset area environmental data, and based on the real-time preset area environmental data, update the initial optimal transportation path to obtain the target optimal transportation path.

[0038] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the construction site transportation path optimization method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0039] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the construction site transportation path optimization method of the first aspect or any corresponding embodiment thereof.

[0040] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the construction site transportation path optimization method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 4 is a flow chart of a method for optimizing a construction site transportation path according to an embodiment of the present invention.

[0043] Figure 24 is a flow chart of another method for optimizing a construction site transportation path according to an embodiment of the present invention.

[0044] Figure 3 It is a flowchart of another method for optimizing the construction site transportation path according to an embodiment of the present invention.

[0045] Figure 4 4 is a structural block diagram of a construction site transportation path optimization device according to an embodiment of the present invention.

[0046] Figure 5 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0048] In the related art, the amount of map data required for the construction site transportation path is large, and a lot of resources are needed during storage and transmission. The map data used for path optimization is not real-time enough and difficult to adapt to changes in the construction site environment. At the same time, the path optimization algorithm is also difficult to adapt to the rapid changes in the construction site environment, such as temporary construction changes or traffic conditions. It lacks sufficient flexibility and adaptability, and thus cannot provide the optimal transportation path, resulting in inaccurate or outdated path optimization results.

[0049] In order to solve the above problems, a method for optimizing the transportation path of a construction site is provided in an embodiment of the present invention, which is used in a computer device. It should be noted that its execution subject can be a construction site transportation path optimization device, which can be implemented as part or all of the computer device through software, hardware, or a combination of software and hardware. The computer device can be a terminal, a client, or a server. The server can be a single server or a server cluster composed of multiple servers. The terminal in the embodiment of the present application can be a smart phone, a personal computer, a tablet computer, or other intelligent hardware devices. In the following method embodiments, the execution subject is a computer device as an example for explanation.

[0050] The computer device in this embodiment is suitable for the use scenarios of efficient compression and decompression of map data and optimization of construction site transportation paths in the engineering field. Through the construction site transportation path optimization method provided by the present invention, by using the data compression algorithm to compress the map data, and decompressing it after receiving the path optimization request, the resources required for data storage and transmission are significantly reduced, which not only reduces the hardware requirements of the system, but also speeds up the loading and processing speed of the map data, thereby improving the overall work efficiency of the path optimization. The optimal transportation path is obtained by using the decompressed map data for path search, and the transportation path is optimized according to the real-time special environment of the construction site, such as ice areas, windy areas, pollution areas and other special areas, and the transportation path is dynamically adjusted according to the real-time special area data, providing a more accurate and reliable path optimization solution, more comprehensively considering the construction site environmental factors, and the optimization results are more in line with the actual transportation needs, realizing real-time response to changes in the construction site environment and traffic conditions, and dynamically adjusting the transportation path. The real-time nature of the path optimization ensures that the transportation path is always the optimal transportation path.

[0051] According to an embodiment of the present invention, an embodiment of a method for optimizing a construction site transportation path is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0052] In this embodiment, a path optimization method is provided, which can be used in the above-mentioned computer device. Figure 1 is a flow chart of a method for optimizing a construction site transportation path according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0053] Step S101, obtaining map data and transportation task data.

[0054] In one example, the user may input the starting point, end point and other relevant parameters of the transport task from the user interface (UI), and the user may upload the original map data, and the system may record the metadata of the original map data, such as the upload time, data version and the like.

[0055] Step S102: compressing the map data using an adaptive data compression algorithm to obtain compressed map data.

[0056] In one example, the system may send the received map data to a data compression module, apply an adaptive data compression algorithm, compress the map data into a smaller volume, and store it in a system server or a local storage device.

[0057] Step S103, obtaining a path optimization request. After receiving the path optimization request, the compressed map data is decompressed to obtain the map data.

[0058] In one example, a user initiates a route optimization request through the user interface UI module. The system calls the data decompression module based on the transportation task parameters input by the user. The data decompression module quickly decompresses the necessary map data blocks according to the requirements of the route optimization task.

[0059] Step S104, based on the transportation task data and the map data, path search and optimization are performed on the map data to obtain an initial optimal transportation path.

[0060] In one example, the decompressed map data is fed into a path optimization engine, which combines the real-time special area data to perform path search and optimization calculations to calculate the optimal transportation path.

[0061] Step S105 , collecting real-time preset area environment data, and updating the initial optimal transportation path based on the real-time preset area environment data to obtain the target optimal transportation path.

[0062] In one example, the real-time special area data integration module continuously monitors and obtains the latest site environment data. The path optimization engine dynamically adjusts the parameters and weights of the optimization algorithm based on the real-time data to calculate the optimal path that adapts to the current environment. The calculated optimal path is displayed to the user through the UI module. The user can view path details, cost assessment, time estimate and other information. The user can provide feedback on the results, and the system makes adjustments based on the feedback.

[0063] In one implementation scenario, the workflow of the construction site transportation path optimization system based on map data compression may include:

[0064] Step 1): User data input and upload: The user inputs the starting point, end point and other relevant parameters of the transportation task through the user interface (UI) module. The user uploads the original map data, and the system records the metadata of the data, such as upload time, data version, etc.

[0065] Step 2): Data compression processing: The system sends the received map data to the data compression module. The data compression module performs preprocessing on the map data, including format unification and noise removal. An adaptive data compression algorithm is applied to compress the map data into a smaller volume and store it in the system server or local storage device.

[0066] Step 3): Data storage and index building: The compressed data is stored securely and indexed to optimize access efficiency. The system records the index information of the compressed data, including the location, size, and access path of the data block.

[0067] Step 4): Triggering of route optimization task: The user initiates a route optimization request through the UI module. The system calls the data decompression module based on the transport task parameters entered by the user.

[0068] Step 5): Fast data decompression: The data decompression module quickly decompresses the necessary map data blocks according to the requirements of the path optimization task. During the decompression process, the system uses a cache mechanism to reduce waiting time and improve system response speed.

[0069] Step 6): Path optimization calculation: The decompressed map data is fed into the path optimization engine. The path optimization engine combines the real-time special area data to perform path search and optimization calculation. The engine uses the improved Dijkstra algorithm and A* algorithm to calculate the optimal transportation path.

[0070] Step 7): Real-time data integration and path adjustment: The real-time special area data integration module continuously monitors and obtains the latest site environment data. The path optimization engine dynamically adjusts the parameters and weights of the optimization algorithm based on real-time data to calculate the optimal path that adapts to the current environment.

[0071] Step 8): Optimization result display and user interaction: The calculated optimal path is displayed to the user through the UI module. The user can view path details, cost assessment, time estimate and other information. The user can also provide feedback on the results, and the system will make adjustments based on the feedback.

[0072] Step 9): Data security and privacy protection: The system implements data encryption and access control throughout the data processing process. Data is protected using the AES encryption standard during transmission and storage. The system conducts regular security audits to ensure the effectiveness of data security and privacy protection measures.

[0073] Step 10): System log recording and monitoring: The system records operation logs, including all key operations such as data upload, compression, decompression, optimization calculation, etc. Monitor system performance to ensure stable system operation and respond to any potential security threats in a timely manner.

[0074] For example, the UI module provides a customized interface layout for the engineering field, including a map display area, a parameter input area, and a result display area. A multi-level navigation menu is implemented, and users can quickly switch between different views and functional modules. A parameter configuration wizard is provided to guide users to complete the input of transportation requirements, including the starting point, the end point, the type of transportation tool, etc. The result display area not only displays the optimized path, but also provides detailed statistical data and chart analysis.

[0075] The construction site transportation path optimization method provided in this embodiment compresses the map data by using a data compression algorithm, and decompresses it after receiving the path optimization request, which significantly reduces the resources required for data storage and transmission, not only reduces the hardware requirements of the system, but also speeds up the loading and processing speed of the map data, thereby improving the overall work efficiency of the path optimization. The optimal transportation path is obtained by using the decompressed map data for path search, and the transportation path is optimized according to the real-time special environment of the construction site, such as ice areas, windy areas, pollution areas and other special areas, and the transportation path is dynamically adjusted according to the real-time special area data, providing a more accurate and reliable path optimization solution, more comprehensively considering the construction site environmental factors, and the optimization results are more in line with the actual transportation needs, achieving real-time response to changes in the construction site environment and traffic conditions, and dynamically adjusting the transportation path. The real-time nature of the path optimization ensures that the transportation path is always the optimal transportation path.

[0076] In this embodiment, a construction site transportation path optimization method is provided, which can be used in the above-mentioned computer device. Figure 2 is a flow chart of another construction site transportation path optimization method according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0077] Step S201, obtain map data and transportation task data. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0078] Step S202: compressing the map data using an adaptive data compression algorithm to obtain compressed map data.

[0079] Specifically, the above step S202 includes:

[0080] Step S2021, preprocessing the map data to obtain preprocessed map data.

[0081] Step S2022, using a dictionary-based compression algorithm to construct a terrain feature dictionary in the preprocessed map data, and using the terrain feature dictionary to encode the preprocessed map data to obtain initial compressed map data.

[0082] Step S2023: perform secondary compression on the initial compressed map data using Huffman coding to obtain compressed map data.

[0083] In one example, the original map data is first preprocessed, including denoising and normalization, to enhance compression efficiency. The dictionary-based LZ77 compression algorithm is applied to build a terrain feature dictionary, and the terrain feature dictionary is used to encode the data. Huffman coding is used to perform secondary compression on the compressed data to further improve the compression rate. The compressed data is stored locally or on a server, and the storage structure is optimized to support fast indexing and access.

[0084] Specifically, map data compression is performed through the following steps:

[0085] Preprocessing map data: In the first step of map data compression, the original map data is preprocessed to facilitate subsequent compression processing.

[0086] Constructing a terrain feature dictionary: Using a dictionary-based compression algorithm, the system analyzes the preprocessed map data and extracts terrain features from it. The terrain features are organized into a dictionary, the terrain feature dictionary. The terrain feature dictionary contains common terrain elements and patterns in the map data.

[0087] Coding: Using the terrain feature dictionary, the system encodes the pre-processed map data. By replacing the terrain elements in the map data with entry references in the dictionary, the redundancy of the data is reduced, achieving the purpose of compression.

[0088] Secondary compression: After dictionary-based compression, the system also uses Huffman coding to perform secondary compression on the initially compressed map data. Huffman coding is a frequency-based coding method that assigns shorter codes to terrain features that appear frequently, further reducing the amount of data.

[0089] Storage and indexing: The compressed map data will be stored in the system server or local storage device, and an index will be built to optimize access efficiency. The terrain feature dictionary also plays a key role in storage and index construction, helping the system to quickly locate and access compressed data blocks. In this way, by preprocessing the map data, using a dictionary-based compression algorithm to build a terrain feature dictionary and encode it using the terrain feature dictionary, and using Huffman coding for secondary compression, the compression rate is further improved, the amount of compressed map data is lower, and the burden on the system is further reduced. The speed and efficiency of map data processing are improved, and it is easy to further optimize the storage structure.

[0090] Step S203, obtaining a path optimization request. After receiving the path optimization request, the compressed map data is decompressed to obtain the map data.

[0091] Specifically, the above step S203 includes:

[0092] Step S2031, decompressing the compressed map data in parallel using multi-threading technology.

[0093] Step S2032: adopting a cache strategy, caching the map data whose appearance times in the compressed map data are greater than a preset threshold, and restoring the map data.

[0094] In one example, when a user requests path optimization, the system automatically triggers the decompression module based on the request. The decompression module uses multi-threading technology to process compressed data in parallel and quickly restore the original map data. During the decompression process, the system adopts a caching strategy to cache frequently accessed data to reduce the computational cost of repeated decompression. When a path optimization request is received, the system needs to decompress the compressed map data. The terrain feature dictionary allows the system to restore the entry references in the compressed data to the original terrain features, thereby restoring the original form of the map data. In this way, after receiving the path optimization request, multi-threading technology is used for parallel decompression to achieve rapid restoration of the original map data. The use of a caching strategy to cache frequently accessed data reduces the computational cost of repeated decompression and further improves the speed and efficiency of map data processing.

[0095] Step S204: Based on the transport task data and map data, the map data is searched and optimized to obtain the initial optimal transport path. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0096] Step S205: collect real-time preset regional environmental data, and update the initial optimal transportation path based on the real-time preset regional environmental data to obtain the target optimal transportation path. Figure 1 Step S105 of the illustrated embodiment will not be described in detail here.

[0097] The construction site transportation path optimization method provided in this embodiment pre-processes the map data, constructs a terrain feature dictionary using a dictionary-based compression algorithm and encodes using the terrain feature dictionary, and performs secondary compression using Huffman coding, thereby further improving the compression rate. The amount of compressed map data is lower, further reducing the burden on the system, improving the speed and efficiency of map data processing, and facilitating further optimization of the storage structure. After receiving the path optimization request, multi-threading technology is used for parallel decompression to achieve rapid restoration of the original map data. The cache strategy is used to cache frequently accessed data, reducing the computational cost of repeated decompression, and further improving the speed and efficiency of map data processing.

[0098] In this embodiment, a construction site transportation path optimization method is provided, which can be used in the above-mentioned computer device. Figure 3is a flow chart of another construction site transportation path optimization method according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0099] Step S301, obtain map data and transportation task data. Figure 2 Step S201 of the illustrated embodiment will not be described in detail here.

[0100] Step S302: compress the map data using an adaptive data compression algorithm to obtain compressed map data. Figure 2 Step S202 of the illustrated embodiment will not be described in detail here.

[0101] Step S303, obtaining a route optimization request. After receiving the route optimization request, the compressed map data is decompressed to obtain the map data. Figure 2 Step S203 of the illustrated embodiment will not be described in detail here.

[0102] Step S304: Based on the transportation task data and the map data, path search and optimization are performed on the map data to obtain an initial optimal transportation path.

[0103] Specifically, the above step S304 includes:

[0104] Step S3041, using the path optimization engine, based on the transportation task data and map data, combined with the real-time data of the preset area, calculate the initial optimal transportation path.

[0105] In the embodiment of the present invention, the path optimization engine includes an improved Dijkstra algorithm and an A* algorithm.

[0106] In one example, the path optimization engine receives decompressed map data and merges it with real-time special area data. The engine uses an improved Dijkstra algorithm, taking into account multiple factors such as the size and weight limit of the transport vehicle, as well as transportation time and risk. A heuristic evaluation function is introduced to predict the cost of each path node based on historical data and real-time information to speed up the path search. Among them, the decompressed map data will be used in the path optimization engine, where the terrain feature dictionary helps to quickly identify and process specific terrain features on the map and optimize the performance of the path search algorithm.

[0107] Exemplarily, the optimal transportation path calculation is achieved through the following steps:

[0108] Defining the cost function: The core of Dijkstra's algorithm is the cost function, which determines the cost of traveling from one node to another. The cost function is redefined to include the limitations of the transportation tools and the transportation risks. For example, the cost function can be a weighted combination of transportation time, distance length, terrain difficulty, adaptability of the transportation tools, etc.

[0109] Consider size and weight restrictions: Add parameters related to the size and weight of the vehicle to the cost function. For example, if a road segment has restrictions on the size or weight of the vehicle, an additional penalty factor will be added to the cost calculation of the segment, making the algorithm tend to choose a path that meets the size and weight requirements.

[0110] Real-time data integration: Using real-time preset regional environmental data, the weights in the cost function are dynamically adjusted. For example, if an area becomes more congested due to construction, the cost weight of that area will increase, affecting the algorithm's path selection.

[0111] Heuristic function: In the A* algorithm, the heuristic evaluation function is used to estimate the cost of the best path from the current node to the target node. The heuristic evaluation function can combine historical data and real-time information to predict the cost of each path node and speed up the path search.

[0112] Specifically, the introduction of the heuristic evaluation function includes: 1) Data collection: It is necessary to collect historical data and real-time information, including but not limited to the speed of transportation vehicles, road conditions, traffic flow, weather conditions, terrain features, etc. 2) Feature selection: Analyze historical data to identify key factors that affect transportation costs and time. These factors include road type, slope, etc. 3) Heuristic function design: Based on the selected features, a heuristic evaluation function is designed. This function is used to estimate the cost from the current node to the target node. Common heuristic methods include Euclidean distance, Manhattan distance, or distance metrics customized according to actual conditions. 4) Weight assignment: Assign weights to each factor in the heuristic function, which reflect the degree of influence of different factors on path selection. The weights can be determined by historical data analysis, expert opinions, or machine learning models. 5) Real-time data fusion: Integrate real-time data into the heuristic evaluation function. For example, if a section of the road is closed due to construction, the real-time data will update the status of the section, and the heuristic function will increase the cost of the section accordingly. 6) Model training: If machine learning methods are used to determine the parameters of the heuristic function, the model can be trained through a training data set so that it can predict the cost of the path node based on the input features. 6) Dynamic adjustment: During the path search process, the heuristic evaluation function can be dynamically adjusted based on real-time data to reflect the latest environmental conditions. 7) Algorithm integration: The heuristic evaluation function is integrated into the A algorithm. In each step of the A algorithm, the heuristic function is used to estimate the cost from the current node to the target node, and the path with the minimum cost is selected. 8) Optimization and iteration: In each iteration of the algorithm, the estimated value of the heuristic evaluation function is updated, and the best path node is selected based on the new cost information. 9) Result verification: The accuracy and effectiveness of the heuristic evaluation function are verified by comparison with the actual transportation situation, and adjustments are made based on feedback. 10) Multi-factor comprehensive decision-making: During the path search process, the algorithm will comprehensively consider all relevant factors, such as transportation tool restrictions, time efficiency, risks, etc., to find the optimal transportation path. 11) Algorithm iterative optimization: In each iteration, the algorithm will update the cost estimate of each node and select the path with the minimum current cost. This process will continue until the lowest cost path from the starting point to the end point is found. 12) Post-processing adjustment: After finding the initial optimal path, the algorithm can also perform post-processing, such as smoothing the path or avoiding specific sensitive areas, to further improve the practicality and safety of the path.

[0113] In this method, the optimal transportation path is calculated by utilizing a path optimization engine including an improved Dijkstra algorithm, and the cost of each path node is predicted, thereby further accelerating the path search speed and improving the user experience.

[0114] Step S305 , collecting real-time preset regional environmental data, and updating the initial optimal transportation path based on the real-time preset regional environmental data to obtain a target optimal transportation path.

[0115] Specifically, the above step S305 includes:

[0116] Step S3051, based on the real-time preset area environment data, the parameters and weights of the path optimization engine are adjusted to obtain the path optimization engine corresponding to the current environment.

[0117] Step S3052, using the path optimization engine corresponding to the current environment, calculate the optimal transportation path for the target.

[0118] In one example, the algorithm weights are adjusted dynamically to adjust the transportation route according to the real-time special area data, such as the impact of ice areas, windy areas, and polluted areas.

[0119] Specifically, dynamically adjusting the transportation path includes:

[0120] Real-time data collection: Through various sensors, monitoring systems and data interfaces, data on construction site environment and traffic conditions are collected in real time.

[0121] Data preprocessing: Clean, format and normalize the collected real-time data to facilitate subsequent analysis and processing.

[0122] Environmental impact assessment: Analyze the impact of real-time data on transportation routes and evaluate the impact of different environmental factors on the size and weight limit of transportation tools, as well as transportation time, risks, etc.

[0123] Dynamic weight adjustment: Dynamically adjust the cost function weights in the path optimization algorithm based on the results of the environmental impact assessment. For example, if a certain area is congested due to construction, increase the weight of that area accordingly so that the algorithm tends to avoid that area.

[0124] Heuristic function update: Update the heuristic evaluation function to reflect the latest environment conditions. This may involve recalculating or adjusting parameters in the heuristic function.

[0125] Path optimization engine trigger: Use the improved Dijkstra algorithm or A* algorithm, combined with the updated heuristic function and adjusted weights, to recalculate the optimal transportation path.

[0126] Comparison of multiple options: Generate multiple alternative paths, compare them based on factors such as cost, time, and risk, and select the best option.

[0127] User interaction: The calculated optimal path is displayed to the user through the user interface, allowing the user to adjust or select according to the actual situation.

[0128] In this method, the weight of the algorithm is dynamically adjusted by combining the influence of real-time special areas, such as ice areas, windy areas, and polluted areas, thereby achieving dynamic adjustment of the transportation path according to the real-time construction site environment, ensuring that the calculated target final transportation path is the optimal transportation path under any circumstances.

[0129] Step S306, determining whether the user role of the current user is authorized.

[0130] Step S307, when the user role of the current user is authorized, the target optimal transportation path is displayed to the current user.

[0131] In one example, the compliance process may include: The system implements a data encryption transmission mechanism, and all sensitive data is encrypted with AES before transmission. A role-based access control policy is designed to ensure that only authorized users can access sensitive data. Regular data security audits are conducted to check the security performance of the system to ensure compliance with the latest data protection regulations.

[0132] Specifically, implementing a role-based access control strategy may include:

[0133] Role definition: First, define the different roles in the system, such as administrator, project manager, ordinary user, etc. Each role has its specific permissions and responsibilities.

[0134] Permission allocation: Assign corresponding permissions to each role, and clarify which roles can access which data and functions. For example, only administrators and project managers can access user management functions.

[0135] User role assignment: When a user registers or creates an account, the user is assigned to one or more roles. This determines the actions that the user can perform in the system.

[0136] Access Control List (ACL): Create an access control list that details the resources and data that each role can access.

[0137] Authentication mechanism: Implement an authentication mechanism to ensure that users must be verified before accessing the system, which can include username and password, two-factor authentication, etc.

[0138] Authorization check: When a user attempts to access sensitive data or perform sensitive operations, the system checks the user's role and permissions to ensure that only authorized users can access.

[0139] Auditing and Monitoring: All user activities are logged and monitored, including data accessed and actions performed. This helps detect unauthorized access attempts and potential security issues.

[0140] Regular review and update: Regularly review roles and permissions to ensure they continue to meet the organization's security policies and business needs. Update roles and permissions to reflect changes in organizational structure or business processes.

[0141] Among them, the types of sensitive data may include: User information: including the user's personally identifiable information (PII), such as name, address, phone number, email address, etc. System configuration and settings: including algorithm parameters, weight settings, user permissions, etc., which are critical to the operation and security of the system. User operation log: records all user activities in the system, which may contain sensitive operation history.

[0142] In this method, by determining whether the user's role is authorized, sensitive data such as the transport route is only displayed to the authorized user role, ensuring that only authorized users can access sensitive data, further ensuring the security of the system.

[0143] The construction site transportation path optimization method provided in this embodiment calculates the optimal transportation path by utilizing a path optimization engine including an improved Dijkstra algorithm, thereby realizing the prediction of the cost of each path node, thereby further accelerating the path search speed and improving the user experience. By combining the influence of real-time special areas, such as ice areas, windy areas, and polluted areas, the weight of the algorithm is dynamically adjusted, thereby realizing the dynamic adjustment of the transportation path according to the real-time construction site environment, ensuring that the calculated target final transportation path is the optimal transportation path under any circumstances. By determining whether the user's role is authorized, sensitive data such as the transportation path is only displayed to the authorized user role, ensuring that only authorized users can access sensitive data, further ensuring the security of the system.

[0144] In this embodiment, a construction site transportation path optimization device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0145] This embodiment provides a construction site transportation path optimization device, such as Figure 4 As shown, including:

[0146] The data acquisition module 401 is used to obtain map data and transportation task data. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0147] The data compression module 402 is used to compress the map data using an adaptive data compression algorithm to obtain compressed map data. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0148] The data decompression module 403 is used to obtain a route optimization request. After receiving the route optimization request, the compressed map data is decompressed to obtain the map data. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0149] The path optimization module 404 is used to search and optimize the path of the map data based on the transportation task data and the map data to obtain the initial optimal transportation path. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0150] The path adjustment module 405 is used to collect real-time preset regional environmental data, and based on the real-time preset regional environmental data, update the initial optimal transportation path to obtain the target optimal transportation path. Figure 1 Step S105 of the illustrated embodiment will not be described in detail here.

[0151] In some optional implementations, the data compression module 402 includes:

[0152] The data preprocessing unit is used to preprocess the map data to obtain preprocessed map data.

[0153] The first data compression unit is used to construct a terrain feature dictionary in the preprocessed map data by using a dictionary-based compression algorithm, and to encode the preprocessed map data by using the terrain feature dictionary to obtain initial compressed map data.

[0154] The second data compression unit is used to perform secondary compression on the initial compressed map data by using Huffman coding to obtain compressed map data.

[0155] In some optional implementations, the data decompression module 403 includes:

[0156] The multi-thread decompression unit is used to decompress the compressed map data in parallel using multi-thread technology.

[0157] The data cache unit is used to adopt a cache strategy to cache the map data whose appearance times in the compressed map data are greater than a preset threshold, and restore the map data.

[0158] In some optional implementations, the path optimization module 404 includes:

[0159] The transport distance calculation unit is used to calculate the initial optimal transport path using a path optimization engine based on transport task data and map data combined with real-time data of a preset area. The path optimization engine includes an improved Dijkstra algorithm and an A* algorithm.

[0160] In some optional implementations, the path adjustment module 405 includes:

[0161] The engine adjustment unit is used to adjust the parameters and weights of the path optimization engine based on real-time preset regional environment data to obtain a path optimization engine corresponding to the current environment.

[0162] The path optimization unit is used to calculate the optimal transportation path for the target by using the path optimization engine corresponding to the current environment.

[0163] In some optional implementations, the construction site transportation path optimization device further includes:

[0164] The authorization judgment unit is used to judge whether the user role of the current user is authorized.

[0165] The data display unit is used to display the target optimal transportation path to the current user when the user role of the current user is authorized.

[0166] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0167] The construction site transportation path optimization device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0168] The embodiment of the present invention also provides a computer device having the above Figure 4 The construction site transportation path optimization device shown.

[0169] See also Figure 5 , Figure 5 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 5As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0170] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0171] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0172] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0173] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0174] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 5 The example of connecting through bus is taken in the following.

[0175] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0176] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0177] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0178] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for optimizing a construction site transportation path, characterized in that: The method comprises: Obtain map data and transportation mission data; Using an adaptive data compression algorithm to compress the map data to obtain compressed map data; Obtaining a path optimization request, and after receiving the path optimization request, decompressing the compressed map data to obtain the map data; Based on the transportation task data and the map data, performing path search and optimization on the map data to obtain an initial optimal transportation path; Real-time preset area environment data is collected, and based on the real-time preset area environment data, the initial optimal transportation path is updated to obtain a target optimal transportation path.

2. The method according to claim 1, characterized in that The method of compressing the map data using an adaptive data compression algorithm to obtain compressed map data includes: Preprocessing the map data to obtain preprocessed map data; Using a dictionary-based compression algorithm, construct a terrain feature dictionary in the preprocessed map data, and using the terrain feature dictionary, encode the preprocessed map data to obtain initial compressed map data; The initial compressed map data is compressed twice using Huffman coding to obtain the compressed map data.

3. The method according to claim 1, characterized in that Decompressing the compressed map data to obtain the map data includes: Decompressing the compressed map data in parallel using multi-threading technology; A cache strategy is adopted to cache map data whose appearance times in the compressed map data are greater than a preset threshold, and the map data is restored.

4. The method according to claim 1, characterized in that: The step of performing path search and optimization on the map data based on the transport task data and the map data to obtain an initial optimal transport path includes: The initial optimal transportation path is calculated by using a path optimization engine based on the transportation task data and the map data in combination with real-time data of a preset area. The path optimization engine includes an improved Dijkstra algorithm and an A* algorithm.

5. The method according to claim 4, characterized in that The updating of the initial optimal transportation path based on the real-time preset regional environment data to obtain the target optimal transportation path includes: Based on the real-time preset area environment data, the parameters and weights of the path optimization engine are adjusted to obtain a path optimization engine corresponding to the current environment, wherein the real-time preset area environment data is used to characterize having; The optimal transportation path for the target is calculated using the path optimization engine corresponding to the current environment.

6. The method according to claim 1, characterized in that The method further comprises: Determine whether the current user's user role is authorized; When the user role of the current user is authorized, the target optimal transportation path is displayed to the current user.

7. A construction site transportation path optimization device, characterized in that: The device comprises: Data acquisition module, used to obtain map data and transportation task data; A data compression module, used to compress the map data using an adaptive data compression algorithm to obtain compressed map data; A data decompression module, used for obtaining a path optimization request, and after receiving the path optimization request, decompressing the compressed map data to obtain the map data; A path optimization module, used for performing path search and optimization on the map data based on the transport task data and the map data to obtain an initial optimal transport path; The path adjustment module is used to collect real-time preset area environment data, and based on the real-time preset area environment data, update the initial optimal transportation path to obtain the target optimal transportation path.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the construction site transportation path optimization method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the construction site transportation path optimization method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the construction site transportation path optimization method according to any one of claims 1 to 6.

Images