Geographic information query method and system based on GIS (Geographic Information System)

Abstract

The invention relates to the technical field of geographic information, in particular to a GIS-based geographic information query method and system, and the method comprises the following steps: based on road traffic information, extracting road traffic conditions, traffic flow data and construction closing information, calling road accessibility data, screening normal traffic roads, removing impassable road sections, and generating a passable road set. According to the invention, by screening road traffic conditions and traffic flows, path planning is ensured to be based on real-time passable roads, interest points are screened in combination with user positions and target categories, a path with the shortest traffic time is calculated, path connectivity is optimized, planning precision and efficiency are improved, task priorities are calculated according to query requirements, and task resource allocation is optimized. Network congestion is reduced, task execution states are monitored, resources are dynamically adjusted, stable and efficient execution of tasks is ensured, query accuracy and resource utilization rate are improved, and stability and timeliness of query tasks are remarkably enhanced.

Description

Technical Field

[0001] The present invention relates to the field of geographic information technology, and in particular to a geographic information query method and system based on GIS. Background Art

[0002] The field of geographic information technology includes the collection, processing, storage, analysis and application of geographic spatial data. The core content of this technology field is to use global positioning system, remote sensing, geographic information and other technologies to digitally express, manage and analyze geographic information. Geographic information system is an important part of this technology field. It has the functions of spatial data storage, query, analysis and visualization. It is widely used in urban planning, environmental monitoring, traffic management, disaster warning and other industries. The development of geographic information technology has promoted the efficient acquisition and in-depth application of spatial information data, enabling various industries to make accurate decisions based on spatial data.

[0003] Among them, the GIS-based geographic information query method refers to the use of geographic information systems to realize the query and retrieval of geographic data, including queries based on spatial indexes, queries based on topological relationships, and queries based on attribute conditions. This method improves the efficiency of geographic data retrieval by constructing a spatial index structure, uses topological relationship calculations to support complex spatial relationship analysis, and combines attribute screening mechanisms for accurate data matching. During the query execution process, geographic coordinate calculations, spatial relationship determination, and data screening strategies are combined to improve query accuracy and response speed.

[0004] The existing technology is limited by static or preset road information when planning a path, and fails to eliminate inaccessible roads affected by construction, closure and other factors in real time, resulting in the planned path being unable to actually be driven, affecting the user experience. In the process of screening points of interest, the road traffic conditions and travel time factors are not taken into account, resulting in the recommended target location being located in an area with restricted traffic or long detour time, reducing the rationality and efficiency of path planning. The setting of query task priority lacks a trade-off between the urgency of the query and the size of the scope, resulting in low-priority tasks occupying computing resources, affecting the processing efficiency of urgent tasks. The allocation of computing resources fails to fully take into account the task network occupancy, which easily leads to uneven allocation of bandwidth resources, hindering the execution of some tasks and even causing task backlogs. There is a lack of real-time monitoring of the execution status of query tasks, and it is impossible to make timely adjustments to abnormal resource occupancy, resulting in excessive resource consumption for some tasks, and the tasks cannot be executed due to insufficient resources, affecting the stability and processing efficiency of the method. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a geographic information query method and system based on GIS.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: A geographic information query method based on GIS, comprising the following steps: S1: Based on road traffic information, extract road traffic conditions, traffic flow data, construction closure information, call road accessibility data, screen normal roads, eliminate impassable sections, and generate a set of passable roads; S2: Based on the passable road set, extract the user's geographic location and the query request target category, filter the points of interest that meet the category, combine the road network status data, filter the path with the shortest travel time, analyze the key nodes in the path, eliminate low-connectivity paths, filter the connectivity paths, adjust the path priority, eliminate the detour paths, and generate the optimal query pass path; S3: Based on the optimal query path, extract the query request category, query scope, time requirement, call the query urgency, calculate the query data weight, and generate a query task priority list based on the query scope size; S4: Based on the query task priority list, analyze the availability of computing resources, filter available computing resources, call query task processing requirements, filter priority tasks, allocate task computing resources, filter task network occupancy, optimize low priority task bandwidth allocation, and execute task resource allocation.

[0007] As a further solution of the present invention, the set of accessible roads includes normally accessible roads, road accessibility data, and restricted roads; the optimal query path includes a path with the shortest travel time, a connectivity path, and a detour path elimination result; the query task priority list includes query data weight, query scope, and query urgency; the task resource allocation includes a priority task resource allocation plan, a low-priority task bandwidth optimization plan, and task network occupancy.

[0008] As a further solution of the present invention, the step of obtaining the passable road set is specifically as follows: S111: Based on the road traffic information, extract the road traffic condition data, traffic flow data, construction closure information, analyze the traffic capacity of each section of road, and calculate the road traffic index using the formula: ; Determine the traffic condition of each section of road and obtain a road traffic condition data set; in, represents the road traffic index, Representative The average speed of the road, Representative The length of the road, Representative The congestion duration of the road Represents the vehicle speed adjustment weight, represents the length adjustment weight, represents the congestion threshold, Indicates the total number of roads; S112: calling the road traffic condition data set, combining the construction closure information, screening the blocked roads, eliminating the impassable road sections, and obtaining the normal traffic road data; S113: Based on the normal passable road data, call the road accessibility data, screen the roads that meet the accessibility requirements, and generate a passable road set.

[0009] As a further solution of the present invention, the step of obtaining the optimal query path is specifically as follows: S211: extracting the user's geographical location and the query request target category based on the passable road set, screening points of interest that meet the category, and obtaining a road travel time adjustment value; S212: Call the road travel time adjustment value, analyze the key nodes of the path, eliminate the low connectivity path, and use the formula: ; Calculate the path connectivity correction value, filter the connectivity path, and obtain the connectivity path set; in, represents the path connectivity correction value, Represents the path The connectivity of key nodes, represents the travel time of the node, Represents the total number of key nodes in the path, represents the average travel time of the path; S213: calling the connectivity path set, adjusting the path priority, eliminating the detour path, screening the optimal path, and generating the optimal query path.

[0010] As a further solution of the present invention, the step of obtaining the query task priority list is specifically as follows: S311: Based on the optimal query path, extract the query request category, query scope, and time requirement, call the query urgency, calculate the query data weight, and process the query data weight and query urgency using the formula: ; Calculate the weighted query priority value and combine it with the query range size to obtain the query priority adjustment coefficient; in, represents the weighted query priority value, Represents the query data weight, Indicates the urgency of the query. Represents the query range adjustment benchmark value. Represents the query scope impact factor, Represents the query range size, A parameter representing the urgency of time requirements; S312: calling the query priority adjustment coefficient, combining the query data weight and time requirement, performing a comparison operation, screening the data query tasks, and obtaining priority query data; S313: Based on the priority query data and in combination with the query range size, the query task priority list is obtained by arranging in descending order.

[0011] As a further solution of the present invention, the step of obtaining the task resource allocation is specifically as follows: S411: Based on the query task priority list, analyze the task computing resource requirements, network bandwidth occupancy, and computing resource availability, select available computing resources that meet the requirements, identify the computing resource adaptability of the query task, and use the formula: ; Get the computing resource suitability list; in, Represents the computing resource adaptation index, Represents the computing resource requirements of the query task, Represents the available computing resources, Represents the bandwidth usage of the query task, Represents the query task priority value, represents the bandwidth occupancy adjustment factor, Represents the priority adjustment parameter, represents the base adjustment value; S412: calling the computing resource adaptability list, screening available computing resources, allocating computing resources according to priority query task requirements, and obtaining computing resource allocation status; S413: Based on the computing resource allocation, screen the network occupancy of low-priority tasks, identify the bandwidth usage of low-priority tasks, optimize bandwidth allocation, and perform task resource allocation.

[0012] As a further solution of the present invention, the method further comprises step S5: S5: Based on the task resource allocation, extract the query task execution status, monitor the computing resource occupancy, evaluate the query task computing resource load, filter out tasks with abnormal resource occupancy, adjust the computing resource scheduling, and obtain the query task execution monitoring record; The query task execution monitoring record includes query task execution status, computing resource load evaluation, and resource scheduling adjustment plan.

[0013] As a further solution of the present invention, the steps of obtaining the query task execution monitoring record are specifically as follows: S511: Based on the task resource allocation, extract the query task execution status, call the timestamp data, task status identification data and computing resource usage record of the task execution log, calculate the execution time difference of the query task on the differentiated nodes, and normalize it to obtain the normalized value of the task execution time; S512: calling the normalized value of the task execution time, monitoring the computing resource occupancy, identifying the computing resource consumption of the query task in the differentiated time window, and comparing the computing resource consumption with the computing resource threshold, using the formula: ; Calculate the computing resource usage deviation value and compare it with the threshold value to obtain the computing resource usage abnormality mark; in, Represents the computing resource usage deviation value, Representative The computing resource consumption of a time window, represents the computing resource threshold, Represents the total number of time windows, Represents the average value of computing resource consumption, Representative The resource occupation weight of a time window, Representative The adjustment coefficient of resource deviation is calculated in each time window. Represents the computing resource consumption deviation stability factor; S513: According to the computing resource occupancy abnormality identifier, filter the resource occupancy abnormality tasks, identify the execution time comparison value of the query task before and after the adjustment, analyze the computing resource utilization change, and obtain the query task execution monitoring record.

[0014] The GIS-based geographic information query system is used to execute the above-mentioned GIS-based geographic information query method, and the system includes: The road traffic status screening module extracts road traffic conditions, road flow values, and closed road sets based on road traffic information, calls road accessibility data, screens roads with normal traffic conditions, removes impassable roads, and establishes a set of accessible roads; The point of interest path calculation module extracts the user's geographical location and the query request target category based on the passable road set, selects the points of interest that meet the category, combines the road network status data, identifies the path travel time, selects the path with the shortest travel time, adjusts the path priority, and establishes the optimal query travel path; The query task scheduling module extracts the query request category, query scope, and time requirement based on the optimal query path, selects priority query tasks, analyzes the spatial distribution characteristics of GIS geographic information query tasks, adjusts the query task execution order, and establishes a query task scheduling list; The resource dynamic allocation module analyzes the resource availability based on the query task scheduling list, selects resources that meet the requirements, calls the query task processing requirements, calculates the task bandwidth occupancy value, selects nodes with low load, and establishes task resource allocation; The task execution monitoring module extracts the query task execution status based on the task resource allocation, monitors the resource occupancy, evaluates the query task load, screens tasks with abnormal resource occupancy, adjusts resource scheduling, and obtains the query task execution monitoring record.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by screening road traffic conditions, traffic flow data and construction closure information, it is ensured that the route planning is based on real-time passable roads, avoiding the interference of impassable sections on route planning, improving the feasibility and accuracy of the route, combining the user's geographical location and target category, screening the points of interest that meet the category, and calculating the shortest travel time path based on the road network status data, thereby optimizing the path connectivity, eliminating low-connectivity paths and detour paths, making the route planning more accurate and efficient, reducing unnecessary time consumption and route detour costs, and calculating the query data weight based on the query category, scope and time requirements, combined with the query urgency and scope size, so as to Reasonably formulate the priority of query tasks, improve the response speed of priority query tasks, analyze the available computing resources, allocate tasks based on the query task processing requirements, and optimize the bandwidth occupancy of low-priority tasks, reduce network congestion during task execution, improve overall computing resource utilization, monitor the execution status of query tasks, and evaluate the resource load situation, dynamically adjust resource scheduling, ensure that query tasks can be executed stably and efficiently, avoid task delays or execution failures caused by abnormal resource occupation, make query execution more accurate, path planning more efficient, fully utilize computing resources, and greatly improve the stability and timeliness of query task execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the workflow of the present invention; Figure 2 A flowchart for obtaining a set of drivable roads in the present invention; Figure 3 This is a flowchart for obtaining the optimal query path in the present invention; Figure 4 A flowchart for obtaining a query task priority list in the present invention; Figure 5 A flowchart for obtaining task resource allocation in the present invention; Figure 6 This is a flowchart for obtaining the query task execution monitoring record in the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0019] Embodiment 1: See also Figure 1 The present invention provides a technical solution: a geographic information query method based on GIS, comprising the following steps: S1: Based on road traffic information, extract road traffic conditions, traffic flow data, construction closure information, call road accessibility data, screen normal roads, eliminate impassable sections, and generate a set of passable roads; S2: Based on the set of accessible roads, extract the user's geographic location and the target category of the query request, filter the points of interest that meet the category, combine the road network status data, filter the path with the shortest travel time, analyze the key nodes in the path, eliminate low-connectivity paths, filter the connectivity paths, adjust the path priority, eliminate the detour paths, and generate the optimal query path; S3: Based on the optimal query path, extract the query request category, query scope, time requirement, call the query urgency, calculate the query data weight, and generate a query task priority list based on the query scope size; S4: Based on the query task priority list, analyze the available computing resources, filter available computing resources, call query task processing requirements, filter priority tasks, allocate task computing resources, filter task network occupancy, optimize low priority task bandwidth allocation, and perform task resource allocation; S5: Based on task resource allocation, extract the query task execution status, monitor the computing resource occupancy, evaluate the query task computing resource load, filter out tasks with abnormal resource occupancy, adjust computing resource scheduling, and obtain query task execution monitoring records.

[0020] The set of accessible roads includes normal roads, road accessibility data, and restricted roads. The optimal query path includes the path with the shortest travel time, the connectivity path, and the detour path elimination results. The query task priority list includes the query data weight, query scope, and query urgency. Task resource allocation includes priority task resource allocation plan, low-priority task bandwidth optimization plan, and task network occupancy. The query task execution monitoring record includes the query task execution status, computing resource load evaluation, and resource scheduling adjustment plan.

[0021] See also Figure 2 , the specific steps for obtaining the passable road set are: S111: Based on the road traffic information, extract the road traffic condition data, traffic flow data, construction closure information, analyze the traffic capacity of each section of road, and calculate the road traffic index using the formula: ; Determine the traffic condition of each section of road and obtain a road traffic condition data set; in, represents the road traffic index, Representative The average speed of the road, Representative The length of the road, Representative The congestion duration of the road Represents the vehicle speed adjustment weight, represents the length adjustment weight, represents the congestion threshold, Indicates the total number of roads; In urban road traffic management, real-time traffic flow data and construction information are used to update the road traffic index , considering a specific example, suppose there are three roads data: Average speed on the first road km / h, length km, congestion duration minute; Average speed on the second road km / h, length km, congestion duration minute; Average speed on the third road km / h, length km, congestion duration minute; Speed ​​adjustment weight , length adjustment weight , congestion threshold Minutes, congestion duration The average value is (because ), data is used to calculate the contribution of each road to the overall accessibility index: ; The calculated numerator is (because ), the denominator is (because ), substituting into the formula we get: ; The calculated road traffic index is 71.1, which reflects the traffic capacity of the three roads after taking into account vehicle speed, road length and congestion status. This value can help the traffic management center make decisions, such as whether to adjust the timing of traffic lights or issue alternative route suggestions to cope with actual traffic conditions. In this way, the road traffic index Become a useful tool for evaluating and managing the efficiency of urban traffic flows.

[0022] S112: calling the road traffic condition data set, combining the construction closure information, screening the blocked roads, eliminating the impassable sections, and obtaining the normal traffic road data; After comprehensively analyzing the road traffic data, the urban traffic management department will conduct a traffic assessment on roads that are severely affected by construction. For example, a certain expressway connecting the city center and the suburbs is undergoing road maintenance, resulting in a decrease in traffic capacity. The traffic data and construction information of this section are centrally processed, and the traffic volume reduction of this section is obtained through traffic monitoring. Then, based on the construction schedule and scope in the construction plan, the specific impact of the construction on traffic is assessed. If the construction causes the number of lanes to be halved, the management department will temporarily adjust the traffic instructions for the section to guide vehicles to divert in advance. This screening and judgment process ensures road safety and smoothness during construction. In this way, those severely obstructed sections can be effectively eliminated, thereby obtaining normal traffic road data.

[0023] S113: Based on the normal passable road data, calling the road accessibility data, screening the roads that meet the accessibility requirements, and generating a passable road set; Filter out roads that meet accessibility requirements to ensure the continuity and efficiency of the urban transportation network. For example, if a major road is closed due to construction, the relevant algorithm will immediately calculate alternative routes to ensure the optimal redistribution of traffic flow. The process includes detecting the accessibility indicators of all alternative routes, such as road width, traffic capacity and historical congestion data. After selecting the best path, the system automatically updates the navigation suggestions and publishes them in real time through the city traffic information board. This not only optimizes the vehicle's driving route, but also reduces traffic congestion. Through careful calculation and screening, a set of accessible roads is finally established, providing real-time and accurate data support for urban traffic management.

[0024] See also Figure 3 , the specific steps for obtaining the optimal query path are: S211: extracting the user's geographic location and the query request target category based on the passable road set, screening points of interest that meet the category, and obtaining a road travel time adjustment value; In the GIS system, the user's geographic location and the data of the target category of the query request are the basis of the map service. This information provides information about the distance between the user and the point of interest and the required road information. For example, assuming that the user is located in the center of City A and the target point of interest is a restaurant in City A, the user's exact location is first confirmed through GPS data, and then the road network database of City A is called to determine all routes to the restaurant. For each route, real-time traffic status data is further called, such as road construction, traffic congestion, etc., to adjust the estimated travel time of each route. The travel time of each path is adjusted to ensure that the most accurate travel time prediction can be obtained when selecting a path. This process avoids direct calls to advanced models or algorithms, but instead calculates the time one by one through actual geographic data and traffic conditions to obtain the road travel time adjustment value.

[0025] S212: Call the road travel time adjustment value, analyze the key nodes of the path, eliminate the low connectivity path, and use the formula: ; Calculate the path connectivity correction value, filter the connectivity path, and obtain the connectivity path set; in, represents the path connectivity correction value, Represents the path The connectivity of key nodes, represents the travel time of the node, Represents the total number of key nodes in the path, represents the average travel time of the path; When calculating path connectivity, the road travel time adjustment value is combined with the node connectivity and travel time The connectivity correction value of each path is calculated by the following formula: ; To further clarify the application and calculation process of the formula, imagine a specific scenario: suppose there is a path containing three key nodes, and the connectivity of each node is are 1.2, 0.9 and 1.5 respectively, and the travel time of each node The average travel time is 10 minutes, 15 minutes, and 20 minutes respectively. For 15 minutes, substitute the formula to calculate as follows: Calculate the sum of the products of connectivity and travel time: ; Compute the square root of the sum of the squares of the connectivity: ; Calculate the absolute value of the sum of the differences between the transit time and the average time: ; In summary, the path connectivity correction value Calculated as: ; In this way, the calculated connectivity correction value can help select the best path among multiple paths that is both fast and highly connected. This calculation example not only demonstrates the calculation process of the formula, but also provides an application example in a real scenario, showing how to effectively evaluate and select the optimal path based on actual road conditions and traffic data.

[0026] S213: calling the connectivity path set, adjusting the path priority, eliminating the detour path, screening the optimal path, and generating the optimal query pass path; Adjust the priority of the set of paths that already consist of highly connected paths and eliminate detour paths. For example, in the road network of city A, in the set of connected paths that have been obtained in the previous step, for each path, further analyze the important nodes such as landmarks and commercial areas it passes through, and adjust the priority of each path based on factors such as the economic activity level of the nodes and the frequency of traffic, and eliminate those paths that have high connectivity but actually take a long detour. This ensures high connectivity not only from a technical perspective, but also meets the needs of actual application scenarios, such as ensuring that users can reach the target location in the most economical and convenient way, and ultimately generates the optimal query path, which not only has high connectivity but also meets the actual needs of urban traffic optimization.

[0027] See also Figure 4 , the specific steps for obtaining the query task priority list are: S311: Based on the optimal query path, extract the query request category, query scope, time requirement, call the query urgency, calculate the query data weight, process the query data weight and query urgency, and use the formula: ; Calculate the weighted query priority value and combine it with the query range size to obtain the query priority adjustment coefficient; in, represents the weighted query priority value, Represents the query data weight, Indicates the urgency of the query. Represents the query range adjustment benchmark value. Represents the query scope impact factor, Represents the query range size, A parameter representing the urgency of time requirements; First, it is necessary to perform weighted calculations on each data item according to the query request category, query scope, time requirement, and query urgency to ensure that the calculated priority can accurately reflect the urgency and importance of the query demand. For example, in a GIS-based emergency response, suppose the query request involves earthquake rescue, the query category is "emergency rescue path", the query scope covers an area with a diameter of 50 kilometers, the time requirement is "real-time update", and the query urgency is "highest". Therefore, all factors will affect the calculation of query priority, and the weight of query data will be The calculation of the importance weight of the query category needs to be referred to. Assuming that the basic weight of the emergency rescue path query is set to 0.8, and the weight of the general traffic condition query is 0.4, the data weight adjustment item brought by the query category is 0.8. It is necessary to combine the query urgency Adjust the weights. Assuming the urgency is divided into five levels, from 1 (lowest) to 5 (highest), and the urgency of this query is 5, then calculate it in a linear weighted manner and set the urgency impact factor. , then the weight of the query data after adjustment is: ; Next, calculate the query range impact factor The larger the query range, the more affected the query priority is, and the weight adjustment is required. Assuming the basic impact factor is 0.5, the impact factor decreases by 0.05 for every 10 kilometers increase. The query range is 50 kilometers this time, and the impact factor is calculated as follows: ; Next, calculate the query priority adjustment coefficient using the following formula: ; in, Indicates the query range adjustment benchmark value. Assume that the benchmark value is set to 3. Represents the query range size, which is 50 kilometers. Represents the time requirement urgency parameter. Assume that the time requirement is divided into three levels: 1 (normal), 2 (high), and 3 (real-time). The time requirement for this query is "real-time", and the value is 3. Then substitute it into the calculation: ; The final calculated query priority adjustment coefficient is 3.5845, which is subsequently used to determine the priority of query tasks to ensure that the most critical query tasks can be executed first.

[0028] S312: calling the query priority adjustment coefficient, combining the query data weight and time requirement, performing a comparison operation, screening the data query tasks, and obtaining the priority query data; Detect the time sensitivity and data integrity of query requests. For example, in traffic management, if the query request is about the traffic conditions of a major road, the time requirement is marked as high because the information needs to be updated in real time for emergency response. At this time, it can be calculated through a preset evaluation model, which takes into account time sensitivity, data integrity and other relevant factors. For example, the weight of time sensitivity is 0.5, and the weight of data integrity is 0.5. The specific comparison operation can be carried out through a series of data processing steps, including data collection, cleaning and analysis. Through detailed steps, priority query data is obtained, and tasks are sorted according to their priority to ensure that the most critical information can be processed first.

[0029] S313: based on the priority query data and in combination with the query range size, the query task priority list is obtained by arranging in descending order; By using the query priority adjustment coefficient and the specific scope of the query task that have been obtained, for example, in urban planning, if it is necessary to rank the development priorities of multiple areas in the city, the rank can be based on the development urgency of each area (query priority adjustment coefficient) and the size of the area (query scope). The specific operation can be achieved through database query, which includes extracting the data of each area stored in the database, calculating its comprehensive score, and then sorting from high to low according to the score. Through this process, it can be clearly demonstrated how to make decisions based on the priority and actual scope of the query task, and finally obtain a query task priority list.

[0030] See also Figure 5 ,The specific steps for obtaining task resource allocation are: S411: Based on the query task priority list, analyze the task computing resource requirements, network bandwidth usage, and computing resource availability, select available computing resources that meet the requirements, identify the computing resource adaptability of the query task, and use the formula: ; Get the computing resource suitability list; in, Represents the computing resource adaptation index, Represents the computing resource requirements of the query task, Represents the available computing resources, Represents the bandwidth usage of the query task, Represents the query task priority value, represents the bandwidth occupancy adjustment factor, Represents the priority adjustment parameter, represents the base adjustment value; First, analyze the computing resource requirements of the query task and available computing resources These two data are obtained through the real-time monitoring system. For example, suppose a query task requires 100GB of memory, and the available computing resources are 120GB of memory. Then analyze the bandwidth usage of the query task. , assuming it is 20Mbps, through monitoring, we know that the actual available bandwidth is 50Mbps. Based on the data, adjust the bandwidth usage coefficient Set it to 0.5, which means that the impact of bandwidth usage on computing resource adaptation is half of its actual value. This adjustment helps balance the importance of bandwidth and memory resources. Priority adjustment parameters and base adjustment It is also adjusted according to the actual situation. For example, the priority adjustment parameter is 1 and the basic adjustment value is 2. This setting helps avoid too small resource quotas due to too low priority, and further calculates the computing resource adaptation index. , Assume that the computing resource adaptation index is 15. This value reflects the degree of adaptation of the query task to the computing resources. Through the index, we can determine which query tasks should be allocated resources first, and further ensure the reasonable allocation of resources. The calculation process example is as follows; ; This calculation demonstrates how to use specific data to calculate the suitability of computing resources to ensure the practical applicability and accuracy of the calculation.

[0031] S412: calling the computing resource adaptability list, screening available computing resources, allocating computing resources according to priority query task requirements, and obtaining computing resource allocation status; Sort the fitness score of each resource in the list, and then select the resource with the highest score for allocation. For example, if task B has the highest fitness score, resources will be automatically allocated to it. This process not only ensures that high-priority tasks can obtain the required resources, but also improves the efficiency of resource use. The results of the allocation are recorded in detail in the resource management for subsequent task monitoring and adjustment, and finally the computing resource allocation is obtained.

[0032] S413: Based on the computing resource allocation, screen the network occupancy of low-priority tasks, identify the bandwidth usage of low-priority tasks, optimize bandwidth allocation, and perform task resource allocation; Optimize the allocation of remaining resources, especially the allocation of network bandwidth, detect the current network bandwidth usage and the needs of low-priority tasks, and then reallocate bandwidth resources according to bandwidth usage efficiency and task urgency. For example, if a low-priority task is found to be running during the peak period of network usage, its bandwidth will be adjusted to avoid affecting high-priority tasks. This optimization process is executed through dynamic adjustment strategies, ultimately ensuring that all tasks can run under the optimal network conditions and perform task resource allocation.

[0033] See also Figure 6 , the specific steps for obtaining query task execution monitoring records are: S511: based on the task resource allocation, extract the query task execution status, call the timestamp data, task status identification data and computing resource usage record of the task execution log, calculate the execution time difference of the query task on the differentiated nodes, and normalize it to obtain the normalized value of the task execution time; First, it involves calling the timestamp data, task status identification data and computing resource usage records in the task execution log, so as to accurately capture the start and end time of each query task on different computing nodes. The specific method of calculating the time difference is to compare the timestamps in the log. For example, if a query task starts at node A with a timestamp of T1 and ends at T2, then the execution time of the task at node A is T2-T1. In this way, the execution time of all nodes is captured, and then the time is normalized to eliminate the impact of hardware performance differences on different nodes on the execution time. The normalized calculation method includes dividing the execution time of each node by the performance index of the node, and finally obtaining the normalized value of the task execution time.

[0034] S512: Call the normalized value of task execution time, monitor computing resource usage, identify the computing resource consumption of the query task in the differentiated time window, and compare the computing resource consumption with the computing resource threshold using the formula: ; Calculate the computing resource usage deviation value and compare it with the threshold value to obtain the computing resource usage abnormality mark; in, Represents the computing resource usage deviation value, Representative The computing resource consumption of a time window, represents the computing resource threshold, Represents the total number of time windows, Represents the average value of computing resource consumption, Representative The resource occupation weight of a time window, Representative The adjustment coefficient of resource deviation is calculated in each time window. Represents the computing resource consumption deviation stability factor; First, we need to calculate the computing resource consumption of each task based on the normalized value of the query task execution time. The computing resource usage varies in different time windows. In order to accurately measure the resource consumption, we need to collect parameters such as CPU usage and memory usage in each time window and calculate its resource usage deviation value. The specific calculation method is as follows: In a computing cluster, assume that there are 5 time windows, and the computing resource consumption of each window is (unit: core time) =120, =135, =145, =110, =155, when the computing resource threshold Z is set to 130 cores, the average computing resource is: ; The weight parameters of the time window are set as follows: =0.8, =1.0, =1.2, =0.9, =1.1, and the adjustment factor Set to 1.1, the stability factor If it is set to 0.5, the calculation is as follows: ; Calculate the denominator: ; ; The final calculation resource usage deviation value is: ; The computing resource occupancy deviation value of 1.96 indicates that the computing resource consumption of some tasks fluctuates greatly, exceeding the set threshold of 1.5 of the computing resource stability range. Therefore, it is necessary to further adjust the allocation of computing resources to avoid unbalanced load of computing tasks and finally obtain the computing resource occupancy abnormality mark.

[0035] S513: Filtering resource occupancy abnormal tasks according to computing resource occupancy abnormality identifiers, identifying execution time comparison values ​​of query tasks before and after adjustment, analyzing computing resource utilization rate changes, and obtaining query task execution monitoring records; First, we need to determine which tasks need to be adjusted due to abnormal resource usage. This involves analyzing the resource consumption pattern and execution time of the tasks, and making adjustments based on the computing resource scheduling strategy. For example, if the resource usage of a task is much higher than that of another task, it will be migrated to a node with richer resources, or its resource allocation will be adjusted to reduce the impact on the task. Specific adjustment strategies can include increasing the CPU or memory quota of the task, or changing the priority of the task. Adjustments must be made based on detailed resource usage reports and historical data to effectively utilize available computing resources. Finally, the execution time comparison value of the query task before and after the adjustment is calculated, and combined with the changes in computing resource utilization, the query task execution monitoring record is obtained.

[0036] The GIS-based geographic information query system is used to execute the above-mentioned GIS-based geographic information query method, and the system includes: The road traffic status screening module extracts road traffic conditions, road flow values, and closed road sets based on road traffic information, calls road accessibility data, screens roads with normal traffic conditions, removes impassable roads, and establishes a set of accessible roads; The POI path calculation module extracts the user's geographic location and the target category of the query request based on the set of accessible roads, selects POIs that meet the category, identifies the path travel time based on the road network status data, selects the path with the shortest travel time, adjusts the path priority, and establishes the optimal query travel path; The query task scheduling module extracts the query request category, query scope, and time requirements based on the optimal query path, screens the priority query tasks, analyzes the spatial distribution characteristics of GIS geographic information query tasks, adjusts the query task execution order, and establishes a query task scheduling list; The resource dynamic allocation module is based on querying the task scheduling list, analyzing the resource availability, screening the resources that meet the requirements, calling the query task processing requirements, calculating the task bandwidth occupancy value, screening the nodes with low load, and establishing task resource allocation; The task execution monitoring module extracts the query task execution status based on task resource allocation, monitors resource occupancy, evaluates query task load, screens tasks with abnormal resource occupancy, adjusts resource scheduling, and obtains query task execution monitoring records.

[0037] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A geographic information query method based on GIS, characterized in that: The following steps are involved: S1: Based on road traffic information, extract road traffic conditions, traffic flow data, construction closure information, call road accessibility data, screen normal roads, eliminate impassable sections, and generate a set of passable roads; S2: Based on the passable road set, extract the user's geographic location and the query request target category, filter the points of interest that meet the category, combine the road network status data, filter the path with the shortest travel time, analyze the key nodes in the path, eliminate low-connectivity paths, filter the connectivity paths, adjust the path priority, eliminate the detour paths, and generate the optimal query pass path; S3: Based on the optimal query path, extract the query request category, query scope, time requirement, call the query urgency, calculate the query data weight, and generate a query task priority list based on the query scope size; S4: Based on the query task priority list, analyze the availability of computing resources, filter available computing resources, call query task processing requirements, filter priority tasks, allocate task computing resources, filter task network occupancy, optimize low priority task bandwidth allocation, and execute task resource allocation.

2. The GIS-based geographic information query method according to claim 1, characterized in that: The set of accessible roads includes normal roads, road accessibility data, and restricted roads. The optimal query path includes the path with the shortest travel time, the connectivity path, and the detour path elimination result. The query task priority list includes the query data weight, the query scope, and the query urgency. The task resource allocation includes the priority task resource allocation plan, the low priority task bandwidth optimization plan, and the task network occupancy.

3. The GIS-based geographic information query method according to claim 1, characterized in that: The steps for obtaining the passable road set are specifically as follows: S111: Based on the road traffic information, extract the road traffic condition data, traffic flow data, construction closure information, analyze the traffic capacity of each section of road, and calculate the road traffic index using the formula: ； Determine the traffic condition of each section of road and obtain a road traffic condition data set; in, represents the road traffic index, Representative The average speed of the road, Representative The length of the road, Representative The congestion duration of the road Represents the vehicle speed adjustment weight, represents the length adjustment weight, represents the congestion threshold, Indicates the total number of roads; S112: calling the road traffic condition data set, combining the construction closure information, screening the blocked roads, eliminating the impassable road sections, and obtaining the normal traffic road data; S113: Based on the normal passable road data, call the road accessibility data, screen the roads that meet the accessibility requirements, and generate a passable road set.

4. The GIS-based geographic information query method according to claim 3, characterized in that: The steps for obtaining the optimal query path are specifically as follows: S211: Based on the passable road set, extract the user's geographical location and the query request target category, filter points of interest that meet the category, and obtain a road travel time adjustment value; S212: Call the road travel time adjustment value, analyze the key nodes of the path, eliminate the low connectivity path, and use the formula: ； Calculate the path connectivity correction value, filter the connectivity path, and obtain the connectivity path set; in, represents the path connectivity correction value, Represents the path The connectivity of key nodes, represents the travel time of the node, Represents the total number of key nodes in the path, represents the average travel time of the path; S213: calling the connectivity path set, adjusting the path priority, eliminating the detour path, screening the optimal path, and generating the optimal query pass path.

5. The GIS-based geographic information query method according to claim 4, characterized in that: The steps for obtaining the query task priority list are specifically as follows: S311: Based on the optimal query path, extract the query request category, query scope, and time requirement, call the query urgency, calculate the query data weight, and process the query data weight and query urgency using the formula: ； Calculate the weighted query priority value and combine it with the query range size to obtain the query priority adjustment coefficient; in, represents the weighted query priority value, Represents the query data weight, Indicates the urgency of the query. Represents the query range adjustment benchmark value. Represents the query scope impact factor, Represents the query range size, A parameter representing the urgency of time requirements; S312: calling the query priority adjustment coefficient, combining the query data weight and time requirement, performing a comparison operation, screening the data query tasks, and obtaining priority query data; S313: Based on the priority query data and in combination with the query range size, the query task priority list is obtained by arranging in descending order.

6. The GIS-based geographic information query method according to claim 5, characterized in that: The steps for obtaining the task resource allocation are specifically as follows: S411: Based on the query task priority list, analyze the task computing resource requirements, network bandwidth occupancy, and computing resource availability, select available computing resources that meet the requirements, identify the computing resource adaptability of the query task, and use the formula: ； Get the computing resource suitability list; in, Represents the computing resource adaptation index, Represents the computing resource requirements of the query task, Represents the available computing resources, Represents the bandwidth usage of the query task, Represents the query task priority value, represents the bandwidth occupancy adjustment factor, Represents the priority adjustment parameter, represents the base adjustment value; S412: calling the computing resource adaptability list, screening available computing resources, allocating computing resources according to priority query task requirements, and obtaining computing resource allocation status; S413: Based on the computing resource allocation, screen the network occupancy of low-priority tasks, identify the bandwidth usage of low-priority tasks, optimize bandwidth allocation, and perform task resource allocation.

7. The GIS-based geographic information query method according to claim 1, characterized in that: The method further comprises step S5: S5: Based on the task resource allocation, extract the query task execution status, monitor the computing resource occupancy, evaluate the query task computing resource load, filter out tasks with abnormal resource occupancy, adjust computing resource scheduling, and obtain the query task execution monitoring record; The query task execution monitoring record includes query task execution status, computing resource load evaluation, and resource scheduling adjustment plan.

8. The GIS-based geographic information query method according to claim 7, characterized in that: The steps for obtaining the query task execution monitoring record are specifically as follows: S511: Based on the task resource allocation, extract the query task execution status, call the timestamp data, task status identification data and computing resource usage record of the task execution log, calculate the execution time difference of the query task on the differentiated nodes, and normalize them to obtain the normalized value of the task execution time; S512: calling the normalized value of the task execution time, monitoring the computing resource occupancy, identifying the computing resource consumption of the query task in the differentiated time window, and comparing the computing resource consumption with the computing resource threshold, using the formula: ； Calculate the computing resource usage deviation value and compare it with the threshold value to obtain the computing resource usage abnormality mark; in, Represents the computing resource usage deviation value, Representative The computing resource consumption of a time window, represents the computing resource threshold, Represents the total number of time windows, Represents the average value of computing resource consumption, Representative The resource occupation weight of a time window, Representative The adjustment coefficient of resource deviation is calculated in each time window. Represents the computing resource consumption deviation stability factor; S513: According to the computing resource occupancy abnormality identifier, filter the resource occupancy abnormality tasks, identify the execution time comparison value of the query task before and after the adjustment, analyze the computing resource utilization change, and obtain the query task execution monitoring record.

9. The GIS-based geographic information query system is characterized by: According to any one of claims 1 to 7, the GIS-based geographic information query method comprises: The road traffic status screening module extracts road traffic conditions, road flow values, and closed road sets based on road traffic information, calls road accessibility data, screens roads with normal traffic conditions, removes impassable roads, and establishes a set of accessible roads; The point of interest path calculation module extracts the user's geographical location and the query request target category based on the passable road set, selects the points of interest that meet the category, combines the road network status data, identifies the path travel time, selects the path with the shortest travel time, adjusts the path priority, and establishes the optimal query travel path; The query task scheduling module extracts the query request category, query scope, and time requirement based on the optimal query path, selects priority query tasks, analyzes the spatial distribution characteristics of GIS geographic information query tasks, adjusts the query task execution order, and establishes a query task scheduling list; The resource dynamic allocation module analyzes the resource availability based on the query task scheduling list, selects resources that meet the requirements, calls the query task processing requirements, calculates the task bandwidth occupancy value, selects nodes with low load, and establishes task resource allocation; The task execution monitoring module extracts the query task execution status based on the task resource allocation, monitors the resource occupancy, evaluates the query task load, screens tasks with abnormal resource occupancy, adjusts resource scheduling, and obtains the query task execution monitoring record.

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