Low-carbon post-disaster rescue system based on quantum computing and autonomous robot technology

Abstract

The invention discloses a low-carbon post-disaster rescue system based on quantum computing and an autonomous robot technology, and belongs to the technical field of post-disaster rescue. The system comprises a post-disaster information acquisition module, a data acquisition and analysis module, a quantum computing decision optimization and resource scheduling module, an autonomous robot task scheduling and execution module and a low-carbon technology model module. Performing ultra-high-speed processing and multi-target optimization decision on post-disaster environment data by adopting a mixed quantum annealing algorithm, and generating a resource scheduling and path planning scheme; the autonomous robot executes post-disaster building evaluation, material delivery and personnel search and rescue tasks based on an AI algorithm, so that the manual risk is reduced; and by combining low-energy-consumption hardware, renewable energy power supply and real-time carbon emission monitoring technologies, the carbon emission in the rescue process is reduced by more than 30%. The intelligent low-carbon rescue system solves the problems of low rescue efficiency, poor safety and high carbon emission in the prior art, is suitable for extreme post-disaster scenes with communication interruption, and provides an innovative solution for intelligent low-carbon rescue.

Description

Technical Field

[0001] The present invention relates to the technical field of post-disaster rescue, and particularly to a low-carbon post-disaster rescue system based on quantum computing and autonomous robot technology, which is used to improve rescue efficiency, reduce personnel risks and reduce carbon emissions. Background Art

[0002] In recent years, with the frequent occurrence of global climate change and natural disasters, post-disaster rescue work has faced increasing challenges. The traditional rescue mode mainly relies on manual efforts. Especially in the case of damaged infrastructure and interrupted communication, the efficiency and response speed of this method are severely restricted. Relevant research shows that the traditional rescue method has low efficiency in extreme post-disaster environments and often relies on a large amount of manpower, increasing the safety risks of personnel.

[0003] To solve this problem, in recent years, automation technology and artificial intelligence have gradually been introduced into the field of post-disaster rescue. As an important innovation, autonomous robot technology can perform high-risk tasks in complex environments, such as search and rescue, material delivery, and structural assessment. However, despite the high potential of these robots, their high cost and complex scheduling problems are still the main obstacles to large-scale application.

[0004] In addition, as a new type of computing technology, quantum computing has made remarkable progress in multiple fields due to its unique advantages in large-scale data processing and optimization problems. Research shows that quantum computing can provide capabilities beyond traditional computing in aspects such as resource scheduling, path planning, and decision support. A low-carbon rescue system that combines quantum computing and autonomous robot technology can not only improve rescue efficiency but also reduce costs by optimizing resource allocation and respond to the global green environmental protection goal.

[0005] Therefore, combining quantum computing and autonomous robot technology to develop a low-carbon and intelligent post-disaster rescue system is the key to solving the current problems of low post-disaster rescue efficiency and high safety risks. The application of such technology can not only optimize the rescue process but also promote the transformation of the rescue system from the traditional manpower-dependent mode to an intelligent and low-carbon future. Summary of the Invention

[0006] The purpose of the present invention is to provide a low-carbon post-disaster rescue system based on quantum computing and autonomous robot technology. By innovatively combining cutting-edge computing technology and intelligent robot technology, this system aims to significantly improve post-disaster rescue efficiency, reduce manual dependence, reduce carbon emissions, and solve multiple bottleneck problems existing in current traditional rescue methods by optimizing resource scheduling and operation efficiency, aiming to improve the efficiency, safety, and low-carbon performance of post-disaster rescue.

[0007] The technical solution of the present invention is as follows:

[0008] A low-carbon post-disaster rescue system based on quantum computing and autonomous robotics technology, the system includes:

[0009] (1) Post-disaster information collection module: By deploying wireless sensor networks, drones or ground sensing devices, collect disaster area environmental information, building structure status, personnel location and resource distribution data;

[0010] (2) Data collection and analysis module: Preprocess the collected raw data by denoising and format standardization, and use data fusion technology to integrate multi-source data to achieve unified formatting processing;

[0011] (3) Quantum computing decision optimization and resource scheduling module: Based on the preprocessed data, use the hybrid quantum annealing algorithm for resource scheduling, path planning and task allocation to optimize the decision-making plan;

[0012] (4) Autonomous robot task scheduling and execution module: According to the decision-making plan generated by quantum computing, schedule the autonomous robot to autonomously execute building assessment, material delivery or personnel search and rescue tasks;

[0013] (5) Low-carbon technology model module: Combine quantum computing optimization algorithms to reduce energy consumption, and use renewable energy power supply systems to achieve low-carbon rescue.

[0014] For the low-carbon post-disaster rescue system described above, the post-disaster information collection module includes: wireless sensor data collection module → building structure damage assessment module → material reserve and demand assessment module → personnel positioning and risk assessment module; among them, the sensor layout density of the wireless sensor data collection module is not less than 50 per square kilometer, and the data loss rate is less than 5%.

[0015] For the low-carbon post-disaster rescue system described above, the data collection and analysis module includes: data preprocessing and fusion module → environmental data screening and sorting module → abnormal data identification and processing module → data analysis and strategy feedback module, where: the abnormal data identification and processing module adopts a multi-level data analysis mechanism to detect and correct abnormal data, the abnormal detection rate is not less than 95%, the processing accuracy rate exceeds 90%, and the response time is less than 3 seconds; the data analysis and strategy feedback module dynamically adjusts the rescue plan based on real-time data by screening and processing invalid or outdated data, eliminating interference information, and the waste data identification rate is greater than 95%, the timeliness of strategy feedback is less than 5 seconds, and the feedback accuracy rate exceeds 90%.

[0016] For the described low-carbon post-disaster rescue system, the quantum computing decision optimization and resource scheduling module includes: a hybrid quantum annealing algorithm processing and decision optimization module → a resource path planning and dynamic adjustment module → an adaptive task allocation module → a rescue task monitoring and adjustment module; among which, the quantum algorithm processing and decision optimization module completes post-disaster data processing within 2 seconds and generates an optimized resource scheduling and path planning scheme.

[0017] For the described low-carbon post-disaster rescue system, the autonomous robot task scheduling and execution module includes: a task execution and autonomous adjustment module → an emergency task response and decision implementation module → a path planning and autonomous execution module → a real-time data feedback and task optimization module; this module is equipped with an adaptive control algorithm, can dynamically adjust the task execution strategy based on real-time feedback, and has an obstacle avoidance function with an obstacle avoidance success rate of not less than 95%.

[0018] For the described low-carbon post-disaster rescue system, the real-time data feedback and task optimization module has the functions of real-time data feedback and task optimization, with a feedback delay of less than 100 milliseconds and the task execution efficiency improvement rate of not less than 30%.

[0019] For the described low-carbon post-disaster rescue system, the low-carbon technology model module includes: a low-energy consumption device design module → a green energy supply integration module → a real-time energy consumption monitoring and optimization module → a carbon emission assessment and feedback module; this module monitors and optimizes the carbon emissions during the rescue process in real time, with a carbon emission reduction rate of not less than 30%.

[0020] The design concept of the present invention is:

[0021] First of all, the present invention calculates data processing and decision support in the post-disaster environment through the hybrid quantum annealing algorithm technology. During the post-disaster rescue process, there are often real-time processing requirements for a large amount of data, such as: the resource status in the disaster area, environmental changes, and the selection of rescue routes. Traditional computing methods cannot process such a large amount of data in a timely manner, resulting in low rescue efficiency. The introduction of the hybrid quantum annealing algorithm technology can process large-scale complex data systems at ultra-high speed. Through quantum algorithms for post-disaster data collection, optimization analysis, and decision support, it greatly improves the decision-making efficiency and provides a fast and accurate resource allocation and path planning scheme for subsequent rescue activities.

[0022] Secondly, leveraging autonomous robotics technology, the present invention proposes a highly integrated rescue robot system capable of independently performing various high-risk tasks in complex post-disaster environments, such as building structure assessment, material delivery, and searching for affected people. Autonomous robots can complete tasks autonomously through built-in sensors and AI algorithms without the support of communication networks and infrastructure in the disaster area, reducing dependence on human rescue personnel and ensuring safe and efficient operation in harsh environments. Through unmanned operation, not only is the rescue efficiency improved, but also the safety of personnel is guaranteed, avoiding unnecessary casualties.

[0023] In terms of resource optimization and cost control, the present invention optimizes the robot scheduling strategy through quantum computing and constructs a resource allocation and path optimization system. Quantum computing can overcome the complexity limitations of traditional methods in scheduling problems, enabling robots to efficiently and accurately allocate tasks and paths based on real-time data during the rescue process. This optimization can effectively reduce the usage cost of robots while improving resource utilization rate, thereby reducing the operating cost of the entire rescue system and ensuring the maximization of existing resources during post-disaster rescue.

[0024] In addition, the present invention also combines low-carbon technologies to propose a green and environmentally friendly rescue mode. During the design process of robots and quantum computing, the need for low-carbon emissions is fully considered, adopting low-energy-consuming robot hardware and green energy power supply methods to minimize the carbon footprint during the rescue process. This design not only conforms to the global green development trend but also demonstrates the broad prospects of low-carbon technologies in the field of post-disaster rescue.

[0025] The innovation of the present invention lies in: by organically combining quantum computing and autonomous robotics technology, it breaks through the efficiency bottleneck of traditional rescue methods and provides a new intelligent and low-carbon technical framework for post-disaster rescue. This technical framework is not only of great significance in improving rescue efficiency and reducing carbon emissions but also can reduce the dependence on human resources during the rescue process, enhance the modernization level of the post-disaster rescue system, and provide new ideas and practical bases for the progress and development of global post-disaster emergency response technologies in the future.

[0026] The advantages and beneficial effects of the present invention are:

[0027] 1. The present invention provides a low-carbon post-disaster rescue system based on quantum computing and autonomous robotics technology. By integrating multiple advanced technologies, it proposes a new efficient, intelligent, and low-carbon post-disaster rescue mode. The system includes links such as post-disaster information collection, environmental data preprocessing, calculation and analysis using the hybrid quantum annealing algorithm, resource scheduling, and autonomous robot task execution, and works collaboratively through an integrated technical framework, aiming to improve the speed, accuracy, and efficiency of post-disaster rescue, reduce carbon emissions, and optimize resource allocation.

[0028] 2. The present invention provides a low-carbon post-disaster rescue system based on quantum computing and autonomous robot technology. The core of the system includes two main technical modules: a quantum computing module and an autonomous robot module. The hybrid quantum annealing algorithm module is used for big data analysis and decision optimization in the post-disaster environment. It quickly processes the huge post-disaster data through a quantum computer and provides decision support such as efficient resource scheduling, path planning, and task allocation. The autonomous robot module uses artificial intelligence technology and adaptive control algorithms to complete complex task execution, such as building assessment, material delivery, and victim search and rescue in the disaster area.

[0029] 3. The present invention uses a hybrid quantum annealing algorithm to perform ultra-high-speed processing and multi-objective optimization decision-making on post-disaster environment data, generating resource scheduling and path planning schemes; autonomous robots execute post-disaster building assessment, material delivery, and personnel search and rescue tasks based on AI algorithms, reducing human risks; combined with low-energy-consuming hardware, renewable energy power supply, and real-time carbon emission monitoring technology, it realizes a reduction of more than 30% in carbon emissions during the rescue process. Thus, it solves the problems of low traditional rescue efficiency, poor safety, and high carbon emissions, is applicable to extreme post-disaster scenarios with communication interruption, and provides an innovative solution for intelligent low-carbon rescue. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flowchart of the low-carbon post-disaster rescue system based on quantum computing and autonomous robot technology of the present invention.

[0031] Figure 2 It is a flowchart of the post-disaster information collection module of the present invention.

[0032] Figure 3 It is a flowchart of the data collection and analysis module of the present invention.

[0033] Figure 4 It is a flowchart of the quantum computing decision optimization and resource scheduling module of the present invention.

[0034] Figure 5 It is a flowchart of the autonomous robot task scheduling and execution module of the present invention.

[0035] Figure 6 It is a flowchart of the low-carbon technology model module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0036] As Figure 1 shown, the low-carbon post-disaster rescue system based on quantum computing and autonomous robot technology of the present invention mainly includes a post-disaster information collection module, a data collection and analysis module, a quantum computing decision optimization and resource scheduling module, an autonomous robot task scheduling and execution module, and a low-carbon technology model module, and realizes its solution through the following steps:

[0037] (1) Post-disaster information collection module: By deploying devices such as wireless sensor networks, drones, and ground sensors, it collects environmental information, building structure status, personnel locations, and resource distribution data in the disaster area in real time, providing basic data support for subsequent analysis and decision-making.

[0038] (2) Data collection and analysis module: Preprocesses the raw data obtained by the post-disaster information collection module (such as denoising, format standardization, etc.), and uses data fusion technology to integrate data from different sensors into a unified format, providing high-quality data input for subsequent quantum computing analysis.

[0039] (3) Quantum computing decision optimization and resource scheduling module: Based on the data collected after the disaster, it uses hybrid annealing quantum computing technology for efficient analysis to generate optimized decision-making plans. This module optimizes decisions such as resource scheduling and path planning according to the real-time situation in the disaster area, resource distribution, and task priorities, thereby improving rescue efficiency.

[0040] (4) Autonomous robot task scheduling and execution module: According to the optimized decisions generated by quantum computing, it schedules tasks for autonomous robots and executes specific rescue tasks. The robot will perform tasks such as building assessment, material delivery, and personnel search and rescue according to the preset tasks, and adjust the action strategy according to real-time feedback to ensure the efficient completion of tasks.

[0041] (5) Low-carbon technology model module: During the robot task execution and resource scheduling process, it reduces energy consumption through quantum computing optimization algorithms, and preferentially selects low-energy-consuming paths and task sequences. It uses renewable energy sources such as solar energy and wind energy as the main power sources for robots and other devices, reducing the dependence on traditional high-carbon energy sources. Environmentally friendly materials are selected during the robot manufacturing process, and post-disaster equipment is recycled and reused to improve the sustainability of the system. The carbon emissions during the rescue process are monitored in real time, and the resource allocation is optimized through quantum computing to ensure that the carbon emissions are minimized.

[0042] As Figure 1 shown, the detailed process of the low-carbon post-disaster rescue system based on quantum computing and autonomous robot technology of the present invention is as follows:

[0043] 1. The post-disaster information collection module includes: wireless sensor data collection module → building structure damage assessment module → material reserve and demand assessment module → personnel positioning and risk assessment module, as Figure 2 shown.

[0044] (1) Wireless sensor data collection: Through the wireless sensor network deployed in the disaster area, real-time monitoring of environmental data, including temperature, humidity, vibration and other data, provides accurate basic information for post-disaster rescue. Specific indicators: The number of sensors deployed is 250 sensors per square kilometer; the data loss rate is less than 5%; the battery life of the sensor is greater than 2 hours.

[0045] (2) Building structure damage assessment: Use the data collected by sensors to conduct real-time damage assessment of buildings in the disaster area, judge their structural stability and whether there is a risk of collapse, so as to make decisions for subsequent rescue tasks. Specific indicators: Accuracy rate: The damage recognition accuracy rate should reach more than 95%; Processing time: The assessment time for a single building should be less than 5 seconds; Classification accuracy: Structural damage is required to be in three categories: "mild", "moderate" and "severe".

[0046] (3) Material reserve and demand assessment: According to the results of post-disaster information collection, assess the material demand in the disaster area, and optimize resource allocation through quantum computing technology to ensure that limited materials can be quickly delivered to where they are needed. Specific indicators: Demand assessment accuracy rate: greater than 90%; Material distribution efficiency: The distribution time should be reduced by more than 30%; Transportation time: The time to reach the core area of the disaster area should be <6 hours.

[0047] (4) Personnel positioning and risk assessment: This module aims to quickly locate the trapped personnel in the disaster area through multi-source data fusion technology, and evaluate the possible risks based on real-time environmental data to prioritize the safety and urgency of rescue targets. Specific indicators: Positioning accuracy: The error is less than 1 meter; Risk response time: less than 1 second; High-risk area recognition rate: greater than 95%.

[0048] 2. The data collection and analysis module includes: Data preprocessing and fusion module → Environmental data screening and sorting module → Abnormal data identification and processing module → Data analysis and strategy feedback module, as Figure 3 shown.

[0049] (1) Data preprocessing and fusion: The collected data is cleaned, denoised and data fused to form a more accurate post-disaster environmental data set for subsequent analysis. Specific indicators: Data cleaning rate: The clearance rate of incorrect or redundant data is greater than 98%; Multi-source data matching accuracy: The consistency accuracy after cross-platform or multi-sensor data fusion is greater than 95%; Preprocessing delay: The time delay between data processing and output is less than 2 seconds.

[0050] (2) Environmental data screening and sorting: Screen out the most important indicators for post-disaster rescue from the massive data, and sort and classify them to provide a direct basis for decision-making. Specific indicators: Effective data rate: greater than 90%; Noise removal effect: greater than 85%; Environmental factor integrity: greater than 80%.

[0051] (3) Abnormal data identification and processing: This module detects, identifies, and processes abnormal data collected after a disaster by designing a multi-level data analysis and correction mechanism, ensuring that the data quality meets the requirements of the rescue system and avoiding the impact of abnormal data on rescue mission planning. Specific indicators: The abnormal detection rate is required to be greater than 95%; the processing accuracy rate is required to be greater than 90%; the response time is required to be less than 3 seconds.

[0052] (4) Data analysis and strategy feedback: The core purpose of the data analysis and strategy feedback module is to ensure that every step of the strategy adjustment during the rescue process is based on high-quality feedback information, thereby dynamically optimizing rescue decisions. By screening and processing invalid or outdated data, the system can more effectively eliminate interference information and readjust the rescue plan according to real-time data. Specific indicators: The waste data identification rate is required to be greater than 95%; the timeliness of strategy feedback is required to be less than 5 seconds; the feedback accuracy rate is required to be greater than 90%.

[0053] 3. The quantum computing decision optimization and resource scheduling module includes: Quantum algorithm processing and decision optimization module → Resource path planning and dynamic adjustment module → Adaptive task allocation module → Rescue task monitoring and adjustment module, as Figure 4 shown.

[0054] (1) Quantum algorithm processing and decision optimization: Based on the hybrid quantum annealing algorithm, it can not only process a large amount of data but also solve multi-objective optimization problems, making resource allocation and rescue operations more efficient and accurate. Specific indicators: The processing speed requires that the quantum algorithm can complete post-disaster data processing and decision optimization within 2 seconds; the decision accuracy rate requires that the execution accuracy of the optimized decision strategy in actual rescue is greater than 90%; the computing resource utilization rate requires that the usage efficiency of quantum computing resources reaches greater than 95%.

[0055] (2) Resource path planning and dynamic adjustment: Quantum computing can plan the best paths for robots and rescue teams and dynamically adjust the paths based on real-time data feedback to ensure the most efficient resource scheduling. Index requirements: The path optimization time requires that the path planning system can complete resource allocation and path selection within the disaster area in less than 1 minute; the dynamic adjustment response time requires that the path dynamic adjustment can complete re-planning in less than 30 seconds under the condition of real-time disaster situation changes; the resource usage efficiency requires that the optimized path planning increases the resource transportation efficiency by more than 20%.

[0056] (3) Adaptive task allocation: Based on the actual situation and the feedback from the rescue teams, the intelligent system will automatically allocate tasks to ensure the collaborative work of each robot and team member, reducing human intervention. Specific indicators: For the task allocation response time, when the post-disaster environment changes, the adaptive task allocation system should be able to complete a new task allocation within less than 10 seconds and adjust the task priorities of robots or resources; for the task completion rate, adaptive task allocation should ensure that the task completion rate reaches more than 95%, even in the case of insufficient resources or environmental changes; for the load balancing rate, during the task allocation process, the system should be able to effectively balance the loads of robots and personnel to ensure that the load difference of each unit is less than 5%.

[0057] (4) Rescue task monitoring and adjustment: This module aims to ensure the safety of personnel by monitoring the real-time location, health status, and workload of rescue personnel, while optimizing task allocation and operation strategies. In the complex post-disaster environment with limited resources, this function can significantly improve the personnel scheduling efficiency and reduce risks. Indicator requirements: For the personnel location accuracy, the system should be able to monitor in real-time and be accurate to an error range of ≤ 2 meters to ensure that the accurate location of rescue personnel can be queried and updated at any time; for the task execution progress monitoring, through the monitoring system, the task completion progress of each rescue personnel should be obtained in real-time to ensure that the tasks are completed as planned with a progress deviation of no more than 10%; for the safety warning ability, the system should be able to evaluate the safety status of rescue personnel based on real-time data. When potential dangers are detected, it should be able to trigger an emergency warning within 30 seconds and adjust the tasks or locations of personnel to ensure the safety of personnel.

[0058] 4. The autonomous robot task scheduling and execution module includes: Task execution and autonomous adjustment module → Emergency task response and decision implementation module → Path planning and autonomous execution module → Real-time data feedback and task optimization module, as Figure 5 shown.

[0059] (1) Task execution and autonomous adjustment: The task execution and autonomous adjustment module is the core part based on autonomous robots and intelligent algorithms, ensuring the efficient and safe completion of rescue tasks in a complex post-disaster environment. Through real-time task feedback, data processing, and path optimization, the robot can dynamically adjust its action strategy according to the on-site situation, improving the task completion degree and reducing resource waste.

[0060] (2) Emergency task response and decision implementation: The emergency task response and decision implementation module is a key function of the system when facing high-priority tasks or emergencies. Through intelligent analysis and task adaptive implementation, it ensures the efficient completion of tasks under the limited conditions of time, resources, and environmental conditions. The module has the capabilities of identifying emergency tasks, dynamically adjusting response priorities, task decomposition, and efficient execution.

[0061] (3) Path Planning and Autonomous Execution: Through real-time data analysis, autonomous robots perform dynamic path planning and execute tasks, capable of adapting to changes in the post-disaster environment and making self-adjustments. Specific indicators: The path planning time is required to be less than 10 seconds; the obstacle avoidance success rate is required to be approximately 95%; the dynamic adjustment frequency is required to be updated in real time per second.

[0062] (4) Real-time Data Feedback and Task Optimization: Each robot and sensor device during the rescue process will feedback its execution status and environmental data to the central system, and the system adjusts tasks and routes based on the feedback to continuously optimize the rescue plan. Specific indicators: The feedback delay is required to be less than 100 milliseconds; the number of task adjustments is required to be no more than 5 times per hour; the task optimization rate is required to increase the task efficiency by 30%.

[0063] 5. The low-carbon technology model module includes: low-energy consumption device design module → green energy supply integration module → real-time energy consumption monitoring and optimization module → carbon emission assessment and feedback module, as Figure 6 shown.

[0064] (1) Low-energy consumption device design: In the design stage of robots and sensor devices, high-efficiency low-energy consumption hardware is adopted, and algorithms are optimized to reduce energy consumption. Specific indicators: The power density is less than 50W / kg; the equipment energy efficiency ratio (EER) is greater than 8.

[0065] (2) Green energy supply integration: Integrate renewable energy such as solar energy and wind energy into the system to provide power for the devices and reduce dependence on traditional energy. Specific indicators: The proportion of renewable energy is greater than 70%; the charging efficiency is greater than 90%.

[0066] (3) Real-time energy consumption monitoring and optimization: Through sensors, the energy consumption data of the devices is monitored in real time, and the working mode is automatically adjusted to optimize energy use. Specific indicators: The energy consumption monitoring accuracy is less than ±0.5%; the automatic optimization response time is less than 100ms.

[0067] (4) Carbon emission assessment and feedback: Assess the carbon emission situation during the entire rescue process, generate a feedback report to optimize carbon emission management in future rescue tasks. Specific indicators: The carbon emission monitoring error is less than ±1%; the carbon emission reduction is greater than 30%.

[0068] The implementation results show that the traditional manual rescue mode is inefficient and risky in extreme post-disaster environments. By combining quantum computing and autonomous robot technology, the present invention can automatically complete high-risk tasks, such as building structure assessment, collapse risk prediction, and material delivery, without a communication network. Quantum computing can break through the bottleneck of traditional computing, perform ultra-high-speed data processing and decision optimization, and greatly improve rescue efficiency. Autonomous robots reduce human intervention and lower human safety risks through AI technology. The system optimizes rescue resource scheduling through quantum computing, reduces the operating costs of robots, and at the same time combines low-carbon technology to reduce carbon emissions. The present invention provides an intelligent and low-carbon new post-disaster rescue mode with broad application prospects, especially suitable for emergency response in large-scale disasters.

Claims

1. A low-carbon post-disaster rescue system based on quantum computing and autonomous robotics technology, characterized in that, The system includes: (1) Post-disaster information collection module: By deploying wireless sensor networks, drones or ground sensing devices, it collects disaster area environmental information, building structure status, personnel locations and resource distribution data; (2) Data collection and analysis module: It preprocesses the collected raw data by denoising and format standardization, and uses data fusion technology to integrate multi-source data to achieve unified formatting processing; (3) Quantum computing decision optimization and resource scheduling module: Based on the preprocessed data, it uses a hybrid quantum annealing algorithm for resource scheduling, path planning and task allocation to optimize the decision-making plan; (4) Autonomous robot task scheduling and execution module: According to the decision-making plan generated by quantum computing, it schedules tasks for autonomous robots so that they can independently execute tasks such as building assessment, material delivery or personnel search and rescue; (5) Low-carbon technology model module: It combines quantum computing optimization algorithms to reduce energy consumption and uses a renewable energy power supply system to achieve low-carbon rescue.

2. The low-carbon post-disaster rescue system according to claim 1, characterized in that, The post-disaster information collection module includes: Wireless sensor data collection module → Building structure damage assessment module → Material reserve and demand assessment module → Personnel positioning and risk assessment module; Among them, the sensor deployment density of the wireless sensor data collection module is not less than 50 per square kilometer, and the data loss rate is less than 5%.

3. The low-carbon post-disaster rescue system according to claim 1, characterized in that The data collection and analysis module includes: Data preprocessing and fusion module → Environmental data screening and sorting module → Abnormal data identification and processing module → Data analysis and strategy feedback module, where: The abnormal data identification and processing module uses a multi-level data analysis mechanism to detect and correct abnormal data, the abnormal detection rate is not less than 95%, the processing accuracy rate exceeds 90%, and the response time is less than 3 seconds; The data analysis and strategy feedback module screens and processes invalid or outdated data, eliminates interference information, and dynamically adjusts the rescue plan based on real-time data. The waste data identification rate is greater than 95%, the strategy feedback timeliness is less than 5 seconds, and the feedback accuracy rate exceeds 90%.

4. The low-carbon post-disaster rescue system according to claim 1, characterized in that quantum The computing decision optimization and resource scheduling module includes: Hybrid quantum annealing algorithm processing and decision optimization module → Resource path planning and dynamic adjustment module → Adaptive task allocation module → Rescue task monitoring and adjustment module; Among them, the quantum algorithm processing and decision optimization module completes post-disaster data processing within 2 seconds and generates an optimized resource scheduling and path planning plan.

5. The low-carbon post-disaster rescue system according to claim 1, wherein, The autonomous robot task scheduling and execution module includes: Task execution and autonomous adjustment module → Emergency task response and decision implementation module → Path planning and autonomous execution module → Real-time data feedback and task optimization module; This module is equipped with an adaptive control algorithm, which can dynamically adjust the task execution strategy based on real-time feedback and has an obstacle avoidance function, and the obstacle avoidance success rate is not less than 95%.

6. The low-carbon post-disaster rescue system according to claim 5, characterized in that, The real-time data feedback and task optimization module has the functions of real-time data feedback and task optimization, with a feedback delay of less than 100 milliseconds and the task execution efficiency improvement rate of not less than 30%.

7. The low-carbon post-disaster rescue system according to claim 1, characterized in that The low-carbon technology model module includes: low-energy consumption equipment design module → green energy supply integration module → real-time energy consumption monitoring and optimization module → carbon emission assessment and feedback module; this module monitors and optimizes the carbon emissions in the rescue process in real time, and the carbon emission reduction rate is not less than 30%.

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