Air-ground integrated geological disaster monitoring equipment combination optimization deployment method and equipment

Abstract

The invention relates to the field of intelligent traffic monitoring, in particular to an air-ground integrated geological disaster monitoring equipment combination optimization deployment method and equipment. According to the method, a deployment scheme of a to-be-deployed area is constructed through a constructed multi-objective optimization function and a two-stage heuristic algorithm, and a feasible solution is calculated through a hierarchical column generation method. Therefore, the coverage rate and the response timeliness of geological disaster monitoring of the reservoir entry channel are effectively improved, and meanwhile, the equipment cost is reduced. When a disaster occurs, the monitoring device can respond in the shortest time, and the timeliness of disaster early warning is effectively improved. And by reasonably selecting the equipment type and the layout position, the layout cost is greatly reduced. While the monitoring comprehensiveness is ensured, the equipment deployment cost is reduced, and the response efficiency is optimized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent traffic monitoring, and in particular to a method and device for optimizing deployment of air-ground integrated geological disaster monitoring equipment. BACKGROUND

[0002] In special areas such as the Tibet access channel, due to the influence of factors such as climate and geology, geological disasters not only threaten traffic safety, but also pose a great risk to economic development and the lives and property of residents. In order to monitor and respond to these disasters in a timely manner, traditional manual patrol methods have been difficult to meet the efficient and accurate monitoring needs.

[0003] In recent years, with the rapid development of remote sensing technology, sensor technology and artificial intelligence, various advanced monitoring equipment such as radar and video monitoring equipment has been widely used in the field of geological disaster monitoring. The monitoring capabilities of these devices have greatly improved the efficiency of disaster prediction and emergency response, but due to the heterogeneity of the devices (such as different monitoring radii, response times and costs) and differences in coverage, how to reasonably configure and optimize these monitoring devices remains a technical problem to be solved.

[0004] Currently, common technical solutions for geological disaster monitoring mainly rely on the following types of equipment: Radar monitoring technology: can realize real-time monitoring, mainly used for detecting ground changes, foreign matter intrusion, etc. Its advantage is that it can realize all-weather, all-time monitoring, but it is greatly affected by environmental factors (such as weather) and has a limited monitoring range.

[0005] Video monitoring technology: real-time monitoring through high-definition cameras, with strong image processing capabilities, suitable for real-time tracking and judging disaster occurrence. The main problem with video monitoring equipment is that the monitoring range is narrow and requires high network bandwidth support.

[0006] Although existing technologies have improved the ability to monitor geological disasters to some extent, due to the large number of different types of equipment, there are significant differences in their monitoring ranges, costs, response times, etc. How to consider these factors comprehensively and achieve maximum monitoring coverage while minimizing equipment costs and response times through reasonable configuration remains a complex optimization problem.

[0007] Therefore, there is a need today for an air-ground integrated geological disaster monitoring equipment combination optimization deployment method that can optimize the deployment of monitoring equipment according to user needs. SUMMARY

[0008] The present application aims to overcome the problem in the prior art that the deployment of monitoring equipment cannot be optimized according to user needs, and provides an air-ground integrated geological disaster monitoring equipment combination optimization deployment method.

[0009] To achieve the above-mentioned purpose of the application, the application provides the following technical solutions. A combined optimization deployment method of air-ground integrated geological disaster monitoring equipment, comprising the following steps: S1: input disaster point data of a region to be deployed and monitoring equipment parameters, and pre-process the disaster point data to output a disaster point set; S2: establish a multi-objective optimization function according to the monitoring equipment parameters and the disaster point set; S3: construct a deployment scheme of the region to be deployed through a two-stage heuristic algorithm; S4: calculate feasible solutions of the deployment scheme of the region to be deployed according to a hierarchical column generation method; S5: output each feasible solution as an optimized deployment scheme.

[0010] As a preferred scheme of the application, the disaster point data in S1 includes longitude and latitude coordinates, occurrence probability and disaster grade of the disaster point; the pre-processing of the disaster point data comprises the following steps: S11: eliminate disaster points with a vertical distance to a road exceeding a preset threshold; S12: project the longitude and latitude of the remaining disaster points to a stake coordinate system of a linear reference system of the road through spatial interpolation, and map each disaster point to a linear stake number; S13: sort each remaining disaster point in order of the linear stake number, and output as a disaster point set ; wherein, is a stake position, i∈[1, N], and N is the number of remaining disaster points.

[0011] As a preferred scheme of the application, the monitoring equipment includes radar equipment, video monitoring equipment and unmanned aerial vehicle equipment, and the monitoring equipment parameters include coverage radius, equipment cost and response time.

[0012] As a preferred scheme of the application, the optimization objectives of the multi-objective optimization function include: a cost optimization objective: taking the lowest total cost as the optimization objective; a response time optimization objective: taking the shortest response time as the optimization objective.

[0013] As a preferred scheme of the application, the constraint conditions of the multi-objective optimization function include: a full coverage hard constraint: each disaster point must be covered by at least one device; a coverage chain constraint: the coverage state of a disaster point is determined by the deployment state of the device covering it; a response time dynamic constraint: the response time of a disaster point does not exceed the response time of the deployment device of the disaster point; Cover redundancy prohibition constraint: each disaster point is covered by at most one device.

[0014] As a preferred scheme of the present application, the S3 comprises the following steps: S31: deploying the unmanned aerial vehicle device in the disaster point set according to the monitoring range interval of the unmanned aerial vehicle device; S32: deploying the video monitoring device for the area not covered by the unmanned aerial vehicle device until the blank area of the set size is covered; S33: deploying the radar device for the remaining blank area, and outputting the first-stage full-coverage deployment scheme; S34: acquiring the disaster points covered by multiple monitoring devices in the first-stage full-coverage deployment scheme; S35: reserving the monitoring device with the highest priority among the disaster points covered by multiple monitoring devices according to the set priority, and outputting the second-stage local optimization deployment scheme; S36: optimizing the second-stage local optimization deployment scheme by using the multi-objective optimization function, and outputting the deployment scheme of the to-be-deployed area.

[0015] As a preferred scheme of the present application, the S4 comprises the following steps: S41: satisfying the full-coverage constraint and minimizing the total cost; S42: generating the pile number-device combination capable of covering each uncovered point; S43: calculating the pile number-device combination of each sub-problem to score, adding the optimal solution with the highest combination score to the main problem, and circulating S42-S43 until all uncovered points are calculated and entering S44; S44: solving the current main problem, and outputting the feasible solution of the deployment scheme of the to-be-deployed area.

[0016] As a preferred scheme of the present application, the S4 further comprises constructing the spatial index of each disaster point.

[0017] As a preferred scheme of the present application, the S4 further comprises precomputing the coverage relationship of all pile number-device combinations, which is called by S42 and S43.

[0018] An air-ground integrated geological disaster monitoring device combination optimization deployment device comprises at least one processor and a memory in communication connection with the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method in any one of the above.

[0019] Compared with the prior art, the present application has the following beneficial effects: This invention constructs a deployment scheme for the area to be deployed by building a multi-objective optimization function and a two-stage heuristic algorithm, and calculates feasible solutions using a hierarchical column generation method. This effectively improves the coverage and response timeliness of geological disaster monitoring along the route to Tibet, while reducing equipment costs. When a disaster occurs, the monitoring equipment can respond in the shortest possible time, effectively improving the timeliness of disaster early warning. By rationally selecting equipment types and deployment locations, deployment costs are significantly reduced. While ensuring comprehensive monitoring, it reduces equipment deployment costs and optimizes response efficiency. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating an optimized deployment method for an integrated air-ground geological disaster monitoring equipment as described in Embodiment 1 of the present invention. Figure 2 This is a flowchart illustrating step S1 of the air-ground integrated geological disaster monitoring equipment combination optimization deployment method described in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the structure of an integrated air-ground geological disaster monitoring equipment combination optimization deployment device, which utilizes the air-ground integrated geological disaster monitoring equipment combination optimization deployment method described in the foregoing embodiments, as described in Embodiment 3 of the present invention. Detailed Implementation

[0021] The present invention will be further described in detail below with reference to experimental examples and specific embodiments. However, this should not be construed as limiting the scope of the above-mentioned subject matter of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0022] Example 1 like Figure 1 As shown, an optimized deployment method for an integrated air-ground geological disaster monitoring equipment system includes the following steps: S1: Input disaster point data and monitoring equipment parameters for the area to be deployed, preprocess the disaster point data, and output a set of disaster points.

[0023] S2: Establish a multi-objective optimization function based on the monitoring equipment parameters and the set of disaster points.

[0024] S3: Construct a deployment scheme for the region to be deployed using a two-stage heuristic algorithm.

[0025] S4: Calculate feasible solutions for deployment schemes in the region to be deployed using the hierarchical column generation method.

[0026] S5: Output each feasible solution as an optimized deployment scheme.

[0027] Example 2 The embodiment is one of the specific implementations of the air-ground integrated geological disaster monitoring equipment combination optimization deployment method described in embodiment 1, and includes the following steps. S1: input disaster point data of a region to be deployed and monitoring equipment parameters, and pre-process the disaster point data to output a disaster point set.

[0028] In this embodiment, the monitoring equipment includes: a radar monitoring equipment: used for large-scale and all-weather geological monitoring, especially for monitoring and sensing of foreign matter intrusion caused by landslides; a video monitoring equipment: suitable for fixed-point monitoring and dynamic monitoring, and capable of feeding back the state of a monitoring region in real time; a drone equipment: used for air monitoring of areas that cannot be directly reached, and having flexibility and high efficiency.

[0029] In the equipment selection process, the monitoring accuracy, monitoring range, response time, anti-interference ability and other technical indexes of each equipment need to be considered. In this embodiment, the main monitoring equipment parameters considered include a coverage radius, equipment cost and response time.

[0030] As shown in FIG. 1, the disaster point data includes the latitude and longitude coordinates of the disaster point, the occurrence probability and the disaster level; and the pre-processing of the disaster point data includes the following steps: Figure 2 S11: disaster point screening: eliminating disaster points with a perpendicular distance to a road exceeding a preset threshold; Further, the embodiment eliminates points with a perpendicular distance to the road exceeding 300 meters through threshold screening, thereby retaining effective monitoring targets. S12: coordinate conversion: projecting the latitude and longitude of the remaining disaster points to a linear reference system (for example, a kilometer stake number) of a road through spatial interpolation, and mapping each disaster point to a linear stake number (this operation can be realized through a GIS tool);

[0031] S13: spatial dimension reduction: sorting each remaining disaster point in a linear stake number order, and outputting a disaster point set ; wherein, is a stake number position, i∈[1, N], and N is the number of remaining disaster points.

[0032] This embodiment converts a two-dimensional geographical network into a one-dimensional linear sequence, simplifying subsequent coverage relationship calculation.

[0033] S2: establishing a multi-objective optimization function according to the monitoring equipment parameters and the disaster point set.

[0034] ​This step is based on the analysis of the distribution characteristics of disaster points, the potential impact of disasters on roads, and the characteristics of different monitoring equipment, and then constructs a multi-objective optimization model.

[0035] S21: Decision variable labeling: This includes equipment deployment status, coverage status, and response time, specifically reflected in the selection of equipment types, coverage of disaster sites, and response speed.

[0036] Equipment deployment status: The equipment deployment status variable directly determines the equipment deployment location and type, and is a key decision variable in the optimization problem. In this embodiment, it is manifested as follows: ; Indicates whether it is at station number Deployment type is The monitoring equipment, if at the chainage number Deployment device types ( If the value is 1, then the value is 1; otherwise, the value is 0. That is: =1: At station number Deployment equipment ; =0: Do not deploy.

[0037] Coverage State: The coverage state variable indirectly reflects the coverage state and is used to constrain the satisfaction of the full coverage condition. In this embodiment, it is represented as follows: ; Indicates disaster point Whether it is covered by at least one device.

[0038] like =1: indicates a disaster point Covered by at least one device; like =0: indicates a disaster point Not covered.

[0039] Response time: The response time variable quantifies the response time at each point, used to minimize the average response time. In this embodiment, it is represented as follows: disaster point Response time (unit: minutes).

[0040] S22: Optimize target setting: Cost optimization objective: To minimize total cost; optimize equipment selection and placement to reduce procurement, installation, and maintenance costs. By rationally arranging the quantity and location of equipment, resource waste can be avoided, thereby reducing total cost.

[0041] Response time optimization objective: the shortest response time as the optimization objective; ensure that the monitoring equipment can respond quickly when disaster occurs, and minimize the response time. For the deployment of monitoring equipment, priority should be given to the coverage of important areas and disaster-prone areas to quickly obtain relevant data when disaster occurs.

[0042] At this time, the multi-objective optimization function expression is: Total cost minimization : ; Average response time minimization : .

[0043] wherein k is the equipment category, k∈{1,2,3}, representing unmanned aerial vehicle equipment, video monitoring equipment and radar equipment respectively; is the cost of the kth monitoring equipment. is the response time of the i th stake number position monitoring equipment.

[0044] S23: constraint calibration: Full coverage hard constraint: each disaster point must be covered by at least one device (100% coverage), ensuring that there is no monitoring omission, which is the core constraint of the model; ; Coverage chain constraint: the coverage state of a disaster point is determined by the deployment state of the device covering it; ; wherein, ; Response time dynamic constraint: the response time of a disaster point does not exceed the response time of the deployed device at the disaster point;

[0045] Coverage redundancy prohibition constraint: each disaster point is covered by at most one device, avoiding resource waste caused by repeated coverage of devices.

[0046] ; S3: constructing the deployment scheme of the to-be-deployed area by a two-stage heuristic algorithm.

[0047] Further, the first stage constructs a rapid coverage skeleton, prioritizing the deployment of unmanned aerial vehicles to cover the largest range. The second stage performs local optimization, using the NSGA-II algorithm to optimize the cost and timeliness in the solution space, replacing high-latency devices with low-cost devices to avoid getting stuck in local optimum, which can quickly generate feasible solutions and is suitable for large-scale problems and emergency decision-making.

[0048] First stage: fast coverage skeleton construction S31: UAV deployment: deploy UAV devices in the disaster point set at intervals of the monitoring range of the UAV device, covering sparse areas.

[0049] For example, the monitoring radius of the UAV device is 10 km, and the UAV device is deployed at stake No. K10, K30, covering the range [K0-K20], [K20-K40].

[0050] S32: Video monitoring filling: deploy video monitoring devices in areas not covered by UAV devices until the set size of the blank area is covered.

[0051] S33: Radar leakage: deploy radar devices in the remaining blank area, and output the first-stage full-coverage deployment scheme.

[0052] This step is mainly used to deploy radar for the remaining isolated points (such as an interval <200m). Thus, the preliminary coverage scheme ensures 100% coverage, but there may be redundancy.

[0053] Second stage: local optimization S34: Obtain disaster points covered by multiple monitoring devices in the first-stage full-coverage deployment scheme (e.g., covered by both UAV and video).

[0054] S35: According to the set priority, retain the monitoring device with the highest priority among the disaster points covered by multiple monitoring devices, and output the second-stage local optimization deployment scheme.

[0055] In this embodiment, the priority is ranked from high to low as follows: radar device > video monitoring device > UAV device, that is, high-cost / high-latency devices (such as UAV) are removed first, and video or radar is retained.

[0056] Further, this embodiment calculates the comprehensive cost and latency when replacing the device as the dynamic score of the replacement operation to judge whether the replacement operation is feasible (when the score is less than a preset value, the replacement operation is cancelled).

[0057] For example, in this embodiment, the dynamic score = wherein, is the cost weight, is the latency weight, is the cost of the kth type of monitoring device, is the response time of the kth type of monitoring device.

[0058] S36: Optimize the second-stage local optimization deployment scheme by using the multi-objective optimization function, and output the deployment scheme of the to-be-deployed area.

[0059] That is, using NSGA-II algorithm for multi-objective optimization, fine-tuning device position / type; According to the multi-objective optimization function output Pareto front solution set, for decision maker to choose trade-off scheme.

[0060] Further, the embodiment calculates whether the device k can cover the point i at the stake number j through GIS and coverage matrix .

[0061] The application achieves dynamic combination of three types of devices, i.e., UAV (large range), video monitoring (medium range) and radar (small range), according to the spatial distribution density (sparse / dense) of disaster points, to realize optimal allocation of cost-time.

[0062] Further, the embodiment is characterized in that, by hierarchical deployment based on different characteristics of heterogeneous devices, timeliness is ensured and cost is optimized. Meanwhile, the model is adapted to complex terrain and non-uniform stake number distribution, and has good engineering applicability. Through these optimizations, disaster monitoring efficiency can be effectively improved and system deployment cost can be reduced.

[0063] Cost-time trade-off: hierarchical deployment based on different coverage radii and cost characteristics of heterogeneous devices.

[0064] Cost optimality: redundant coverage prohibition cooperates with devices to reduce total deployment cost.

[0065] Engineering applicability: support for non-uniform stake number distribution, adaptation to complex terrain monitoring requirements.

[0066] S4: calculate the feasible solution of the deployment scheme of the to-be-deployed area according to the hierarchical column generation method.

[0067] This step improves the solving efficiency of large-scale problems through main sub-problem decomposition.

[0068] S41: main problem setting (global deployment): Meet the full coverage constraint and minimize the total cost; solve the integer programming model, and use a solver or heuristic algorithm to generate a feasible solution.

[0069] S42: sub-problem setting (candidate generation): For each uncovered point, generate a stake number-device combination that can cover it .

[0070] S43: calculate the score of each sub-problem stake number-device combination, add the optimal solution with the highest combination score to the main problem, and cycle S42-S43 until all uncovered points are calculated and enter S44; S44: Solve the current main problem and output a feasible solution for the deployment scheme of the area to be deployed.

[0071] Furthermore, S4 also includes constructing a spatial index (e.g., an R-tree index) for each disaster point, reducing the distance calculation complexity from... Down to This accelerates spatial query performance.

[0072] Furthermore, S4 also includes pre-calculating the coverage relationships of all station-equipment combinations for use in S42 and S43. That is, by pre-calculating all possible equipment-station coverage relationships... This facilitates real-time invocation and avoids redundant calculations. For example, when querying the coverage area of ​​a drone at station K50, the cached results for K40-K60 are read directly.

[0073] S5: Output each feasible solution as an optimized deployment scheme.

[0074] Obtain the optimized deployment schemes corresponding to each feasible solution, for example: Effective coverage deployment solution: low cost (relies on drones), but high average latency (e.g., 27 minutes).

[0075] Redundant coverage deployment solution: High cost (overlaying video surveillance), but low latency (e.g., 6.5 minutes).

[0076] Redundant deployment is implemented in key areas, and redundant deployment is carried out according to the optimized plan to ensure uninterrupted monitoring even if some equipment fails.

[0077] This invention fully realizes the entire chain of technical solutions from data input to optimization decision-making, combining theoretical rigor with engineering practicality.

[0078] Example 3 like Figure 3 As shown, an integrated air-ground geological disaster monitoring equipment deployment device includes at least one processor, a memory communicatively connected to the at least one processor, and at least one input / output interface communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor, which, when executed, enables the at least one processor to perform the integrated air-ground geological disaster monitoring equipment deployment method described in the foregoing embodiments. The input / output interface may include a display, keyboard, mouse, and USB interface for inputting and outputting data.

[0079] Further, the air-ground integrated geological disaster monitoring equipment combination optimization deployment device can be a radar monitoring device, a video monitoring device, an unmanned aerial vehicle air-ground integrated geological disaster monitoring equipment combination optimization deployment device, and the like, which can perform deep information identification.

[0080] Further, the processor can include one or more processing cores. The processor connects various parts in the entire air-ground integrated geological disaster monitoring equipment combination optimization deployment device through various interfaces and lines, performs various functions of the air-ground integrated geological disaster monitoring equipment combination optimization deployment device and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory, and calling data stored in the memory. Alternatively, the processor can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor can be integrated with a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU is mainly used to process the operating system, user interface, and application program; the GPU is used to render and draw display content; and the modem is used to process wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor, but can be realized by a separate communication chip.

[0081] The memory can include a random access memory (RAM) and a read-only memory (ROM). The memory can be used to store instructions, programs, codes, code sets or instruction sets, such as instructions or code sets for implementing an air-ground integrated geological disaster monitoring equipment combination optimization deployment method provided by the embodiments of the present application. The memory can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for implementing at least one function, instructions for implementing various method embodiments described above, and the like. The data storage area can also store data created by the air-ground integrated geological disaster monitoring equipment combination optimization deployment device in use (such as a mapping table of modulation order sequence and depth, image data, and spectrum graph data).

[0082] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction related hardware, the foregoing program can be stored in a computer readable storage medium, and the program executes the steps of the above-mentioned method embodiments when executed; and the foregoing storage medium includes a mobile storage device, a read only memory (Read Only Memory, ROM), a magnetic disc or an optical disc and various storage program codes.

[0083] When the above-mentioned integrated unit of the present application is realized in the form of a software function unit and sold or used as an independent product, it can also be stored in a computer readable storage medium, and the computer readable storage medium stores program codes, and the program codes can be called and executed by a processor to execute the method described in the above-mentioned method embodiments. Based on such understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product, the computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the method described in the embodiments of the present application. And the foregoing storage medium includes an electronic storage medium, such as a flash memory, an EEPROM (Electrically Erasable Programmable Read-Only Memory), an EPROM, a hard disk or a ROM. Alternatively, the computer readable storage medium includes a non-transitory computer readable medium. The computer readable storage medium has a storage space for program codes for executing any method steps in the above-mentioned methods. These program codes can be read from or written to one or more computer program products. And the program codes can be compressed in a suitable form.

[0084] The above only describes the preferred embodiments of the present application and is not used to limit the present application, and any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for optimizing the deployment of integrated air-ground geological disaster monitoring equipment, characterized in that, Includes the following steps: S1: Input disaster point data and monitoring equipment parameters for the area to be deployed, preprocess the disaster point data, and output a set of disaster points; S2: Establish a multi-objective optimization function based on the monitoring equipment parameters and the set of disaster points; S3: Construct a deployment plan for the region to be deployed using a two-stage heuristic algorithm; S4: Calculate feasible solutions for deployment schemes in the region to be deployed using the hierarchical column generation method; S5: Output each feasible solution as an optimized deployment scheme.

2. The method for optimizing the deployment of integrated air-ground geological disaster monitoring equipment according to claim 1, characterized in that, The disaster point data mentioned in S1 includes the latitude and longitude coordinates of the disaster point, the probability of occurrence, and the disaster level; the preprocessing of the disaster point data includes the following steps: S11: Eliminate disaster points whose perpendicular distance from the road exceeds a preset threshold; S12: The latitude and longitude of the remaining disaster points are projected onto the station coordinate system of the road linear reference system through spatial interpolation, and each disaster point is mapped to the linear station. S13: Sort the remaining disaster points according to the linear station number order, and output the disaster point set. ;in, Let i be the station number, i∈[1,N], and N be the number of remaining disaster points.

3. The method for optimizing the deployment of integrated air-ground geological disaster monitoring equipment according to claim 2, characterized in that, The monitoring equipment includes radar equipment, video monitoring equipment, and drone equipment. The parameters of the monitoring equipment include coverage radius, equipment cost, and response time.

4. The method for optimizing the deployment of integrated air-ground geological disaster monitoring equipment according to claim 3, characterized in that, The optimization objectives of the multi-objective optimization function include: Cost optimization objective: To minimize the total cost; Response time optimization objective: To minimize response time.

5. The method for optimizing the deployment of integrated air-ground geological disaster monitoring equipment according to claim 4, characterized in that, The constraints of the multi-objective optimization function include: Full coverage hard constraint: Each disaster point must be covered by at least one device; Coverage chain constraint: The coverage status of a disaster point is determined by the deployment status of the equipment covering it; Dynamic constraint on response time: The response time of a disaster site shall not exceed the response time of the equipment deployed at that disaster site; Redundancy prohibition constraint: Each disaster point can be covered by at most one device.

6. The method for optimizing the deployment of integrated air-ground geological disaster monitoring equipment according to claim 5, characterized in that, S3 includes the following steps: S31: Deploy drone equipment at intervals according to the monitoring range of the drone equipment in the set of disaster points; S32: Deploy video monitoring equipment in areas not covered by drone equipment until a blank area of ​​a set size is covered; S33: Deploy radar equipment in the remaining blank areas and output the first phase of full coverage deployment plan; S34: Obtain disaster points covered by multiple monitoring devices in the first phase of the full coverage deployment plan; S35: Based on the set priority, retain the highest priority monitoring device among the disaster points covered by multiple monitoring devices, and output a two-stage local optimization deployment plan; S36: Optimize the two-stage local optimization deployment scheme through the multi-objective optimization function, and output the deployment scheme for the area to be deployed.

7. The method for optimizing the deployment of integrated air-ground geological disaster monitoring equipment according to claim 1, characterized in that, S4 includes the following steps: S41: Satisfies the full coverage constraint and minimizes the total cost; S42: For each uncovered point, generate a station-equipment combination that can cover it; S43: Calculate the station number-equipment combination for each sub-problem and score it. Add the optimal solution with the highest combination score to the main problem. Repeat S42-S43 until all uncovered points have been calculated and then proceed to S44. S44: Solve the current main problem and output a feasible solution for the deployment scheme of the area to be deployed.

8. The method for optimizing the deployment of integrated air-ground geological disaster monitoring equipment according to claim 7, characterized in that, S4 also includes constructing a spatial index for each disaster point.

9. The method for optimizing the deployment of integrated air-ground geological disaster monitoring equipment according to claim 8, characterized in that, S4 also includes pre-calculating the coverage relationship of all station-equipment combinations for use in S42 and S43.

10. An optimized deployment device for integrated air-ground geological disaster monitoring equipment, characterized in that, The method includes at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 9.

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