An intelligent robot for environmental sample collection

Abstract

The application discloses an intelligent robot for environment sample collection, which comprises a moving platform, a collection module, an analysis module, a storage module, a communication module and a control module; the moving platform is used for driving the intelligent robot to move; the collection module comprises a plurality of collectors corresponding to a plurality of environment sample types, and is used for automatically switching to a collector of a corresponding type and collecting a corresponding sample under the control of the control module; the analysis module is used for analyzing the collected sample; the storage module is used for saving the sample and analysis data; the communication module is used for exchanging data with a central database or a remote control center; and the control module is electrically connected with the collection module, the analysis module, the storage module and the communication module, and is used for being responsible for the operation control of the whole robot. The intelligent robot can automatically collect samples such as soil, water and air in various environments.

Description

Technical Field

[0001] This invention relates to the fields of environmental monitoring and robotics, specifically to an intelligent robot for environmental sample collection, which aims to improve the efficiency and accuracy of environmental sample collection while reducing the need for manual operation. Background Technology

[0002] With the rapid advancement of industrialization and urbanization, environmental pollution problems have become increasingly prominent, with phenomena such as declining air quality, water pollution, and soil degradation emerging one after another. Against this backdrop, environmental monitoring has become crucial, serving not only as the foundation for assessing environmental quality but also as a prerequisite for formulating environmental protection policies and measures.

[0003] However, traditional environmental sampling methods rely primarily on manual operation, which has many shortcomings. First, manual sampling is inefficient, requires significant human and material resources, and is easily limited by natural conditions such as weather and terrain. Second, in dangerous or inaccessible areas, such as chemically contaminated areas, radiation-contaminated areas, deep seas, or high mountains, manual sampling presents extreme difficulties and risks, and may even threaten the lives of the sampling personnel.

[0004] Therefore, developing an automated and intelligent environmental sample collection robot is crucial for addressing these issues. Such robots can replace manual labor, efficiently and accurately collecting samples in various complex and hazardous environments. They can not only improve work efficiency and reduce labor costs, but also obtain more comprehensive and accurate environmental data while ensuring personnel safety, providing stronger support for environmental protection and governance. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide an intelligent robot for environmental sample collection, which can automatically collect soil, water, air and other samples in various environments, and has the ability to learn and adapt to the environment, so as to improve collection efficiency and accuracy.

[0006] To achieve the above objectives, an intelligent robot for environmental sample collection is provided, comprising: a mobile platform, a collection module, an analysis module, a storage module, a communication module, and a control module;

[0007] The mobile platform is used to drive the intelligent robot to move;

[0008] The acquisition module includes modules suitable for various environments.

[0009] Multiple sample collectors of different sample types are used to automatically switch to the corresponding type of collector and collect the corresponding sample under the control of the control module.

[0010] The analysis module is used to analyze the collected samples;

[0011] The storage module is used to store samples and analysis data;

[0012] The communication module is used to exchange data with the central database or remote control center;

[0013] The control module is electrically connected to the acquisition module, the analysis module, the storage module, and the communication module, and is responsible for the operation control of the entire robot.

[0014] In some possible implementations, the mobile platform includes:

[0015] The chassis, as the foundation and support structure of the mobile platform, is used to support the robot body. The chassis is designed with mounting points for mounting wheel hubs and multiple connection interfaces. Some connection interfaces are used to connect to the robot body, and some connection interfaces are used to connect to the adjustable suspension system.

[0016] Deformable tires are mounted under the chassis and connected to the chassis via wheel hubs and an adjustable suspension system. The deformable tires can automatically adjust their shape and / or air pressure according to terrain features.

[0017] An adjustable suspension system, connecting the chassis and the deformable tires, is used to adjust the distance between the robot body and the ground to adapt to different terrain undulations.

[0018] In some possible implementations, the acquisition module includes: a soil collector, the soil collector comprising:

[0019] The telescopic drill rod is composed of multiple interlocking rod sections, and its length can be extended or retracted by adjusting the relative positions of the rod sections to adapt to soil sampling needs at different depths.

[0020] A multi-functional sampling head is connected to the lower end of the drill rod. The sampling head is equipped with a soil hardness sensor and an adjustment mechanism. The soil hardness sensor is used to detect the soil hardness in real time, obtain the soil hardness detection signal, and feed it back to the control module. The adjustment mechanism is connected to the drill rod and is used to drive the drill rod to adjust the drilling force according to the control command of the control module.

[0021] The control module includes a drilling force control component, which is electrically connected to the soil sensor and the adjustment mechanism. It is used to receive the hardness detection signal from the soil sensor and control the adjustment mechanism to adjust the drilling force of the drill rod according to the hardness detection signal, so as to achieve adaptive soil sampling.

[0022] In some possible implementations, the collection module includes a water sampler, which includes a suction pump and a filtration system. The suction pump is used to draw water samples from the water body and connects to the filtration system via a pipe or hose to deliver the drawn water samples to the filtration system. The filtration system is located adjacent to the suction pump and is used to receive and process the water samples delivered from the suction pump, removing impurities with a particle diameter larger than a preset diameter threshold.

[0023] In some possible implementations, the acquisition module includes an air sampler; the air sampler is equipped with an intake pump and an expandable gas collection bag; the intake pump is used to draw in air samples and is connected to the gas collection bag via an airtight connecting tube to ensure that the drawn air samples can be completely delivered into the gas collection bag; the gas collection bag is located adjacent to the intake pump and is used to receive and temporarily store the air samples delivered by the intake pump for subsequent analysis and processing; the expandable gas collection bag is made of flexible material and has an automatic expansion and contraction function, which can adjust its capacity according to the amount of air sample collected to adapt to different sampling needs, thereby facilitating subsequent sample storage and transportation.

[0024] In some possible implementations, the analysis module includes: a chemical sensor for detecting the presence and concentration of a predetermined chemical substance in the sample; a temperature and humidity sensor for measuring the temperature and humidity of the sample; and a spectroscopic instrument for analyzing the spectral characteristics of the sample to determine the types and amounts of elements and compounds in the sample.

[0025] In some possible implementations, the analysis module further includes a data processing unit for collecting and jointly analyzing data from various sensors and spectroscopic instruments to assess the quality and contamination status of environmental samples.

[0026] In some possible implementations, the intelligent robot for environmental sample collection further includes an intelligent navigation system, the intelligent navigation system comprising:

[0027] The GPS module is used for global positioning and provides the robot's longitude, latitude, and altitude information in real time.

[0028] Geomagnetic sensors are used to detect geomagnetic field information and use this information to assist in positioning.

[0029] LiDAR is used to detect surrounding obstacles and terrain, enabling precise positioning and autonomous navigation.

[0030] In some possible implementations, the control module includes an adaptive acquisition module, which uses machine learning algorithms to automatically adjust the acquisition strategy and optimize the acquisition time and sequence based on environmental changes.

[0031] In some possible implementations, the machine learning algorithm is used to learn from historical and environmental data and continuously optimize the acquisition strategy.

[0032] The above technical solution has the following beneficial technical effects:

[0033] Intelligent robots can automatically complete tasks such as environmental sample collection, analysis, storage, and data transmission without human intervention, greatly improving work efficiency and accuracy.

[0034] The acquisition module is equipped with various types of acquisition devices to adapt to the acquisition needs of samples in different environments. Under the control of the control module, the acquisition devices can be automatically switched, realizing flexible and diverse acquisition methods.

[0035] The analysis module can perform high-precision analysis on the collected samples, providing accurate analytical data and a reliable basis for environmental monitoring and assessment.

[0036] The storage module can save the collected samples and analysis data for easy subsequent processing and use; the communication module enables data exchange with the central database or remote control center, facilitating real-time monitoring and remote management.

[0037] The control module, acting as the brain of the robot, is responsible for the coordination, control, and operation management among the various modules, ensuring the robot's stable and efficient operation. Attached Figure Description

[0038] The accompanying drawings are provided to better understand the invention and are not intended to unduly limit the scope of the invention. Wherein:

[0039] Figure 1 This is a functional block diagram of an intelligent robot for environmental sample collection according to an embodiment of the present invention;

[0040] Figure 2 This is a functional block diagram of the acquisition module in an embodiment of the present invention;

[0041] Figure 3 This is a functional block diagram of the analysis module in an embodiment of the present invention;

[0042] Figure 4 This is a flowchart of the adaptive acquisition module in an embodiment of the present invention. Detailed Implementation

[0043] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0044] The purpose of this invention is to provide an intelligent robot for environmental sample collection, which can automatically collect samples such as soil, water, and air in various environments, and has the ability to learn and adapt to the environment, thereby improving collection efficiency and accuracy.

[0045] like Figure 1 As shown, an intelligent robot for environmental sample collection includes: a mobile platform, a collection module, an analysis module, a storage module, a communication module, and a control module. The mobile platform drives the intelligent robot to move. The collection module includes multiple collectors corresponding to various environmental sample types, which automatically switch to the appropriate collector type and collect the corresponding sample under the control of the control module. The analysis module analyzes the collected samples. The storage module stores the sample and analysis data. The communication module exchanges data with a central database or remote control center. The control module is electrically connected to the collection module, analysis module, storage module, and communication module, and is responsible for the overall operation control of the robot, including the scheduling of collection tasks, management of the navigation system, operation of the collection module, and coordination of data analysis and communication.

[0046] The mobile platform supports multi-terrain adaptability, and the acquisition module is equipped with different acquisition tools for different samples (soil, water, air). The analysis module can analyze the acquired samples, the storage module is used to save sample and analysis data, and the communication module supports data exchange with a central database or remote control center. Through the communication module, the working status and location of the intelligent robot can be remotely monitored, acquisition data can be received in real time, and the acquisition plan can be remotely adjusted or the robot can be directly controlled to perform specific operations. Specifically, the analysis module is used to analyze the acquired samples, and the processed data or results include information on the sample's composition, concentration, properties, structure, or other relevant characteristics. Specifically, the communication module supports 4G / 5G network communication, enabling real-time data exchange and remote control with the remote control center.

[0047] In some embodiments, the mobile platform may include: a chassis, serving as the foundation and support structure of the mobile platform, for carrying the robot body, the robot body including a data acquisition module, an analysis module, a storage module, a communication module, and a control module; the chassis is designed with mounting points for mounting wheel hubs and multiple connection interfaces, some of which are used to connect to the robot body, and some of which are used to connect to an adjustable suspension system; deformable tires adapted to various terrains, mounted under the chassis and connected to the chassis via wheel hubs and the adjustable suspension system, the deformable tires being able to automatically adjust their shape and / or air pressure according to terrain features to improve passability and adaptability; and an adjustable suspension system, connecting the chassis and the deformable tires, for adjusting the distance between the robot body and the ground to adapt to different terrain undulations.

[0048] In a further embodiment, the mobile platform may also include a floating device for providing buoyancy on the water surface, mounted around or below the chassis and fixedly connected to the chassis, for providing buoyancy to the mobile platform so that the mobile platform can float stably on the water surface, thereby improving mobility and / or stability in wetland environments.

[0049] The chassis supports the robot body and other key components, ensuring their stable installation and operation on the mobile platform. The robot body is mounted on the chassis of the mobile platform. The chassis provides robust support and connection points for mounting various parts of the robot body, such as control modules, detection sensors, and data acquisition actuators. The connection between the robot body and the chassis can be achieved through bolts, welding, or other suitable connection methods to ensure the stability and safety of the robot during movement.

[0050] The chassis is made of robust materials to ensure sufficient strength and stability. Mounting points and connection interfaces on the chassis are used for reliable mechanical connections with other components. Deformable tires, supported by the chassis and adjustable by the suspension system, effectively transfer the robot's weight and power to the ground, enabling stable and flexible movement. The adjustable suspension system allows the mobile platform to adapt to different terrain undulations, maintaining smooth travel and reducing the impact of bumps on the robot body and other components. The design and location of the floating device are determined according to specific needs to ensure sufficient buoyancy and stability in wetland environments.

[0051] The advantages of the mobile platform in this embodiment of the invention are:

[0052] The system employs intelligent deformable tires that can intelligently sense changes in terrain and automatically adjust their shape and / or internal air pressure. This design not only enhances the tire's contact with various surfaces but also significantly improves the mobility and terrain adaptability of the mobile platform.

[0053] Employing an advanced suspension system, this system precisely connects the mobile platform's chassis to its intelligent deformable tires. It automatically adjusts height and stiffness as needed, ensuring the mobile platform remains stable in complex terrain, reducing bumps, and protecting equipment and loads on the platform.

[0054] Integrated floating devices are employed, cleverly integrated with the mobile platform structure, and can be quickly deployed when encountering water. They provide the necessary buoyancy for the mobile platform, ensuring stable and efficient movement in wetlands, shallow water, or flooded environments.

[0055] like Figure 2 As shown, in some embodiments, the acquisition module may include: a soil sampler, which includes: a telescopic drill rod composed of multiple interlocking rod segments, capable of length extension and retraction by adjusting the relative positions of the segments to adapt to soil sampling needs at different depths; and a multi-functional sampling head connected to the lower end of the drill rod, the sampling head containing a soil hardness sensor and an adjustment mechanism, the soil hardness sensor being used to detect soil hardness in real time, obtain soil hardness detection signals, and provide feedback to the control module; and the adjustment mechanism being connected to the drill rod drive mechanism to adjust the drilling force according to the control commands of the control module.

[0056] The control module includes a drilling force control component, which is electrically connected to the soil sensor and the adjustment mechanism. It is used to receive the hardness detection signal from the soil sensor and control the adjustment mechanism to adjust the drilling force of the drill rod according to the hardness detection signal, so as to achieve adaptive soil sampling.

[0057] The drill rod is used to drill samples in the soil. The sampling head is equipped with a sensor to detect soil hardness and adjust the drilling force according to the detection results. When encountering harder soil, the drilling force will increase; when encountering softer soil, the drilling force will decrease to prevent excessive damage to the soil sample.

[0058] Furthermore, the multi-functional sampling head also integrates a processing mechanism for processing soil samples after collection, in order to preserve and further process them; the adjustment mechanism includes an electric motor or a hydraulic mechanism.

[0059] The drill pipe and the multi-functional sampling head are connected by a detachable mechanical connection to facilitate the replacement or repair of the sampling head when needed. This mechanical connection should ensure that the drill pipe and the sampling head can work stably and collaboratively during drilling, while also being able to withstand the forces and vibrations generated during drilling.

[0060] The adjustment mechanism is housed inside the multi-functional sampling head, a design that makes the entire soil sampler more compact and efficient. This allows the adjustment mechanism to be closer to the drill rod and soil, reducing transmission losses and improving response speed. This layout also helps reduce external interference, improving the stability and reliability of the entire system. The adjustment mechanism is mechanically connected to the drill rod and drives it to operate. Specifically, the adjustment mechanism may include transmission components such as gears, worm gears, belts, or chains, which transmit the output power of the adjustment mechanism to the drill rod, driving it to rotate or move up and down. Furthermore, the adjustment mechanism can adjust the drilling force of the drill rod by changing the magnitude and direction of the output power according to the control commands of the control module, adapting to soils of different hardness. If the adjustment mechanism is an electric motor, it changes the motor's speed or direction, thereby driving the drill rod to rotate or move up and down with different forces through mechanical connection. If the adjustment mechanism is a hydraulic mechanism, it adjusts the flow or pressure of the hydraulic oil, thereby changing the magnitude and direction of the force acting on the drill rod. In this way, through the action of the adjustment mechanism, the drill rod can drill into the soil with appropriate force, achieving soil sample collection. This adaptive adjustment method ensures sampling efficiency while avoiding excessive damage to soil samples, thus improving the accuracy and reliability of soil collection.

[0061] The connection between the adjustment mechanism and the drill rod is achieved through a transmission connection. A transmission connection refers to the method of transmitting power from one component to another using transmission elements (such as gears, worm gears, belts, chains, etc.). In this case, the adjustment mechanism transmits power to the drill rod via the transmission connection, thereby driving the drill rod to rotate or move up and down to adjust the drilling force. This transmission connection method ensures the efficiency and stability of power transmission, enabling the soil sampler to adapt to different soil hardness and sampling requirements.

[0062] The multi-functional sampling head can be tapered, allowing for easier soil penetration and reduced resistance during sampling. Furthermore, the tapered tip can concentrate force, aiding in obtaining samples from harder soils. Additionally, the tapered design helps minimize soil disturbance during sampling, resulting in more accurate soil samples.

[0063] like Figure 2As shown, in some embodiments, the acquisition module may include a water quality sampler, which includes a suction pump and a filtration system. The suction pump is used to draw water samples from the water body and connects to the filtration system via pipes or hoses to deliver the drawn water samples to the filtration system. The filtration system is located adjacent to the suction pump and is used to receive and process the water samples delivered from the suction pump, removing impurities with particle diameters larger than a preset diameter threshold. In some embodiments, the filtration system of the water quality sampler aims to remove impurities with particle diameters larger than the preset diameter threshold. The filtration system may be set up to protect subsequent analytical instruments from damage by larger particle impurities, or to prevent these larger particle impurities from affecting the accuracy of sample analysis. In environmental sample collection, the collected samples need to undergo a certain degree of pretreatment to ensure that the samples can be correctly analyzed. For example, water samples may contain larger particle impurities such as silt. If these impurities are not removed, they may clog analytical instruments, affecting the normal operation of the instruments and the accuracy of test results. Therefore, a filtration system is set up to remove these impurities. However, to ensure that the water quality sampler can obtain the true water quality conditions, the design of the filtration system needs to take into account different detection requirements. For example, if the goal is to detect suspended particulate matter in water, the preset diameter threshold of the filtration system should be set large enough to allow these suspended particles to pass through and be collected. If the goal is to detect dissolved contaminants, the filtration system can be set more finely to remove larger particulate impurities that may interfere with the analysis. Therefore, the settings of the filtration system should be determined based on the specific application purpose and detection requirements of the intelligent robot to ensure both the protection of the analytical instrument and the acquisition of accurate environmental sample data.

[0064] like Figure 2 As shown, in some embodiments, the acquisition module includes an air sampler; the air sampler is equipped with an air intake pump and an expandable gas collection bag; the air intake pump is used to draw in air samples and is connected to the gas collection bag via an airtight connecting tube to ensure that the drawn air samples can be completely delivered into the gas collection bag; the gas collection bag is located adjacent to the air intake pump and is used to receive and temporarily store the air samples delivered from the air intake pump for subsequent analysis and processing; the expandable gas collection bag is made of flexible material and has an automatic expansion and contraction function, which can adjust its capacity according to the amount of air sample collected to adapt to different sampling needs, thereby facilitating subsequent sample storage and transportation.

[0065] like Figure 3As shown, in some embodiments, the analysis module includes: a chemical sensor for detecting the presence and concentration of a predetermined chemical substance in a sample, suitable for analyzing heavy metal content in soil, dissolved oxygen levels in water, and concentrations of harmful gases in the air; a temperature and humidity sensor for measuring the temperature and humidity of the sample, which is crucial for assessing environmental indicators such as soil growth conditions, thermosphere distribution in water bodies, and air comfort; and a spectroscopic analysis instrument for analyzing the spectral characteristics of the sample's absorption and emission to determine the types and contents of elements and compounds in the sample, playing an important role in soil fertility analysis, identification of organic and inorganic matter in water bodies, and compositional analysis of suspended particulate matter in the air.

[0066] Spectroscopic instruments are advanced analytical tools that provide crucial information about the internal composition of a sample by detecting its absorption and emission of light. The following is a detailed explanation of how to determine the types and amounts of elements and compounds in a sample using spectral characteristics:

[0067] The absorption properties of light: When light passes through a substance, the atoms or molecules within that substance absorb light of specific wavelengths. This is because electrons within atoms and molecules undergo transitions at specific energies, jumping from lower energy levels to higher energy levels. The energy required for this transition is exactly equal to the energy of the absorbed photon. Each element and compound has its unique absorption spectrum, meaning they absorb light at different intensities at different wavelengths. By comparing the absorption spectrum of a sample with the standard spectra of known elements or compounds, it can be determined whether the sample contains those components.

[0068] The emission properties of light: When atoms or molecules are excited by energy (e.g., through electrothermal activity, chemical reactions, or high-energy photons), they return from an unstable high-energy state to a more stable low-energy state, releasing energy in the process. This energy release is typically manifested as light emission. The emitted light also has a specific wavelength, depending on the type of atom or molecule and its energy level. Therefore, by analyzing the wavelength and intensity of the light emitted by a sample, the composition of the sample can also be determined.

[0069] Determining Content: In addition to identifying the types of elements and compounds, spectroscopic analysis can provide information about the content of these components in a sample. This is achieved by comparing the intensity of the sample spectrum with that of a standard spectrum. For example, if the absorption peak of a certain element in the sample is particularly strong, then the content of that element in the sample is likely to be high. To obtain more accurate quantitative results, standard curve methods or other quantitative analysis methods can be used. These methods involve establishing a relationship between the spectral response and concentration using standard samples of known concentrations, and then using this relationship to calculate the concentration of each component in the unknown sample.

[0070] like Figure 3As shown, in a further embodiment, the analysis module also includes a data processing unit. This unit is responsible for collecting data from various sensors and spectroscopic instruments, and performing joint analysis of multi-dimensional data using a preset algorithm model to comprehensively assess the quality and pollution status of environmental samples. This data processing unit can select appropriate analysis models and parameters based on the different characteristics of soil, water, and air samples to ensure the accuracy and reliability of the analysis results. Furthermore, the data processing unit can compare the analysis results with historical data and environmental standards to promptly identify environmental change trends and potential pollution problems.

[0071] Specifically, the implementation process of the data processing unit is described as follows:

[0072] The data processing unit first collects data from various sensors and spectroscopic instruments in the analysis module. These sensors include chemical sensors, temperature and humidity sensors, etc., used to detect the chemical composition, temperature, and humidity of the sample. The spectroscopic instruments are used to acquire the spectral data of the sample, thereby analyzing the elements and compounds in the sample.

[0073] The collected data may contain noise or incomplete information, thus requiring preprocessing. Data preprocessing includes steps such as noise filtering, missing value imputation, and data normalization to ensure data quality and prepare for subsequent analysis.

[0074] The data processing unit will extract features from the preprocessed data to identify features that have a significant impact on the quality and pollution status of environmental samples. For example, it can extract absorption peaks at specific wavelengths from spectral data or extract the concentration of specific chemical substances from chemical sensor data.

[0075] The data processing unit will use pre-defined algorithmic models to perform joint analysis on the extracted features. These algorithmic models may include statistical analysis methods, machine learning algorithms, or deep learning frameworks, which are capable of processing multi-dimensional data and revealing correlations and potential patterns between different data.

[0076] Through joint analysis, the data processing unit can comprehensively assess the quality and pollution status of environmental samples. For example, by combining chemical composition analysis and spectral analysis results, the types and concentrations of pollutants in water samples can be assessed; by combining temperature and humidity data and chemical data, soil fertility and pollution levels can be assessed.

[0077] After the analysis is completed, the data processing unit will output comprehensive evaluation results. These comprehensive evaluation results can be displayed directly on the intelligent robot's display interface, or sent to a remote server or remote control center via the communication module for further decision-making and analysis.

[0078] The data processing unit can also self-optimize based on analysis results and external feedback. For example, if it finds that certain features are not strongly correlated with pollution levels, the feature extraction algorithm can be adjusted; if the prediction accuracy of the analysis model is not high, a more advanced algorithm can be used or the model parameters can be adjusted.

[0079] Through the above implementation process, the data processing unit in the analysis module can effectively perform multi-dimensional analysis of environmental samples, provide a comprehensive assessment of quality and pollution status, and provide a scientific basis for environmental monitoring and protection.

[0080] The chemical sensors, temperature and humidity sensors, and spectroscopic instruments in the analysis module are not limited to a specific type of environmental sample but can be applied to a variety of samples. Chemical sensors can detect the presence and concentration of predetermined chemical substances in water, air, or soil samples. For example, they can detect heavy metal content in soil, dissolved oxygen levels in water, or concentrations of harmful gases in the air. Temperature and humidity sensors are suitable for measuring the temperature and humidity of various environmental samples, whether soil, water, or air. This is crucial for assessing environmental indicators such as soil growth conditions, thermosphere distribution in water bodies, or air comfort. Spectroscopic instruments can also analyze the spectral characteristics of various environmental samples to determine the types and amounts of elements and compounds in the samples. They can be used for soil fertility analysis, identification of organic and inorganic matter in water bodies, and compositional analysis of suspended particulate matter in the air.

[0081] In some embodiments, the intelligent robot for environmental sample collection further includes an intelligent navigation system, which includes: a GPS module for global positioning, providing the robot's longitude, latitude, and altitude information in real time; a geomagnetic sensor for detecting geomagnetic field information and using this information to assist in positioning; and a lidar for detecting surrounding obstacles and terrain to achieve precise positioning and autonomous navigation. The intelligent navigation system combines multiple navigation technologies such as GPS, geomagnetic sensors, and lidar to achieve precise positioning and autonomous navigation. The intelligent robot can autonomously plan the optimal path based on preset collection points and can adjust the path according to the actual environment. The intelligent navigation system is used to autonomously plan the optimal path based on preset collection points and can adjust the path according to the actual environment to avoid obstacles and dangerous areas.

[0082] In some embodiments, the control module includes an adaptive acquisition module, used to automatically adjust the acquisition strategy based on environmental changes (e.g., weather, terrain, etc.) using machine learning algorithms, optimizing acquisition time and sequence. This adaptive acquisition strategy, through machine learning algorithms, enables the intelligent robot to automatically adjust its acquisition strategy based on environmental changes (e.g., weather, terrain, etc.), optimizing acquisition time and sequence, thereby improving acquisition efficiency and sample quality.

[0083] The core of the adaptive acquisition module is a machine learning-based decision engine. This engine can process real-time data from multiple environmental sensors, including but not limited to meteorological data (such as temperature, humidity, and wind speed), terrain data (such as topographic maps, slope, and ground texture), and historical data. By analyzing this data, the decision engine can dynamically adjust its acquisition strategy to adapt to environmental changes, improving acquisition efficiency and data quality.

[0084] like Figure 4 As shown, its working principle is as follows:

[0085] S110: The adaptive data acquisition module first collects real-time or current environmental data from the intelligent robot's built-in weather and terrain sensors, while also receiving historical environmental data from the central database. This data undergoes preprocessing, including data cleaning and normalization, to facilitate subsequent analysis.

[0086] S120: Employs machine learning algorithms suitable for time series forecasting and pattern recognition, such as random forests, deep learning networks, or reinforcement learning algorithms. These algorithms can predict future environmental change trends and the potential effects of different data collection strategies based on current and historical environmental data.

[0087] S130: Based on the prediction results of the machine learning algorithm, the adaptive acquisition module calculates the optimal acquisition strategy, including determining the best acquisition time, acquisition order, and acquisition path. Examples are provided below; please refer to Examples 1 to 6:

[0088] Example 1: When rainfall is predicted, the module may prioritize adjusting the data collection plan and complete the data collection work on open water areas first.

[0089] Example 2: When a machine learning algorithm predicts a significant change in wind direction and an increase in wind speed within the next few hours, the adaptive acquisition module will prioritize collecting samples from downwind areas. This is because the spread of hazardous substances is often influenced by wind direction and speed; prioritizing downwind areas ensures monitoring of these areas is completed before pollutants spread.

[0090] Example 3: For certain environmental parameters that are sensitive to temperature and humidity, such as the activity of biological samples or the rate of chemical reactions, the adaptive acquisition module adjusts the acquisition plan based on predicted temperature and humidity trends. For example, if hot and dry weather is predicted, the adaptive acquisition module will schedule acquisition in the morning or evening to avoid sample damage due to high temperatures during acquisition.

[0091] Example 4: If a machine learning algorithm predicts that the pollutant concentration in a certain area will exceed the safety threshold within the next few hours, the adaptive acquisition module will immediately adjust its plan and prioritize acquisition of data for that area. This helps to obtain data on pollutant exceedances in a timely manner and initiate corresponding emergency response measures.

[0092] Example 5: For data collection points affected by topographic or water level changes, such as rivers, lakes, or coastal areas, the adaptive data collection module adjusts the data collection path and sequence based on predicted water level rises or topographic changes. For example, when an impending flood is predicted, the adaptive data collection module will select higher-altitude data collection points for initial data collection to ensure the safety of personnel and the integrity of the data collection equipment.

[0093] Example 6: When a specific ecological event is predicted to occur, such as a large-scale algal bloom, fish migration, or insect hatching, the adaptive acquisition module will prioritize monitoring and sample collection for these events. This helps scientists understand and grasp the impact of these ecological events on the environment and ecosystems in a timely manner.

[0094] S140: The adaptive acquisition module monitors changes in environmental data in real time and dynamically adjusts the acquisition strategy according to the actual situation. If unexpected situations are encountered during the acquisition process (such as sudden severe weather or terrain obstacles), the adaptive acquisition module can quickly replan the acquisition plan to avoid data loss or acquisition interruption.

[0095] S150: After data collection is complete, the collected data and its results are fed back to the central database to optimize the machine learning model. Through continuous learning and adjustment, the adaptive data collection module can gradually improve the accuracy and efficiency of its decision-making.

[0096] The advantages of an adaptive acquisition module are as follows: It can automatically adjust its acquisition strategy based on real-time changes in the environment, improving the adaptability and flexibility of the acquisition task. By optimizing acquisition time and sequence, it reduces invalid and duplicate acquisition, improving data acquisition efficiency and quality. Through a feedback learning mechanism, the adaptive acquisition module can continuously optimize its decision model, enhancing long-term acquisition performance.

[0097] In some embodiments, machine learning algorithms can learn from historical and environmental data to continuously optimize acquisition strategies, thereby improving acquisition efficiency and sample quality. Specifically, machine learning algorithms are mainly applied in the control modules of intelligent robots, particularly the adaptive acquisition module. This algorithm learns and identifies key factors affecting acquisition efficiency and sample quality by analyzing historical acquisition data (including acquisition time, location, sample quality under environmental conditions, etc.) and real-time environmental data (such as weather conditions, terrain changes, etc.).

[0098] The learning process includes the following steps: While performing the collection task, the intelligent robot records environmental and sample data in real time, including environmental parameters such as weather conditions, soil moisture, and temperature at the collection site, as well as sample quality assessment results. Machine learning algorithms preprocess the collected data, extracting features that significantly affect collection efficiency and sample quality, such as collection efficiency under specific weather conditions and the impact of different terrains on sample quality. Using the extracted features and historical collection data, machine learning models are trained to establish a relationship model between environmental conditions and collection strategies. These models can predict which collection strategy will achieve the highest efficiency and sample quality under specific environmental conditions. Based on the model's predictions, the adaptive collection module automatically adjusts the collection strategy, including the selection of collection time, priority ranking of collection sites, and planning of collection paths, to ensure optimal collection results under the current environmental conditions.

[0099] The advantages of this machine learning algorithm are: by optimizing collection time and path, it reduces invalid and duplicate collections, thereby improving overall collection efficiency. It selects the optimal collection point and timing based on environmental conditions, ensuring that the collected samples better reflect environmental conditions and improving sample representativeness and accuracy. Over time, the intelligent robot continuously collects new data, and the machine learning algorithm continuously learns, optimizes, and adjusts the collection strategy, enabling the intelligent robot to better adapt to environmental changes and improve long-term collection performance. In this way, the machine learning algorithm allows the intelligent robot to automatically optimize its collection strategy based on environmental changes and historical experience, thereby improving the efficiency and intelligence of the collection process while ensuring sample quality.

[0100] Taking an intelligent robot for wetland environmental sample collection as an example, this robot is equipped with special tires and a floating device suitable for wetlands, enabling it to move freely in wetland environments. The collection module includes a water sampler and a soil sampler, which can automatically switch and collect the corresponding samples. Through onboard lidar and geomagnetic sensors, it achieves precise positioning and autonomous navigation in complex wetland environments. Utilizing machine learning algorithms, it automatically adjusts the collection plan based on weather changes and terrain features, optimizing collection efficiency. Through 4G / 5G networks, it transmits collected data back to the control center in real time and receives remote commands for adjustments or to execute specific tasks.

[0101] The advantages of the above technical solution are:

[0102] This robot is a multi-terrain adaptive mobile platform that can adapt to various complex terrains, including but not limited to mountains, swamps, and deserts, greatly expanding the data collection range.

[0103] The robot uses machine learning algorithms to automatically adjust its collection strategy based on environmental changes, thereby improving collection efficiency and sample quality.

[0104] This robot has a comprehensive analysis module that can not only collect samples but also perform preliminary analysis, providing faster and more accurate data support for environmental monitoring.

[0105] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0106] The units described in the embodiments of the present invention can be implemented in software or hardware. The described units can also be located in a processor, and the names of these units do not necessarily limit the specific unit itself.

[0107] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. An intelligent robot for environmental sample collection, characterized in that, include: Mobile platform, data acquisition module, analysis module, storage module, communication module, and control module; The mobile platform is used to drive the intelligent robot to move; The acquisition module includes multiple collectors corresponding to various environmental sample types, which are used to automatically switch to the corresponding type of collector and acquire the corresponding sample under the control of the control module. The analysis module is used to analyze the collected samples; The storage module is used to store samples and analysis data; The communication module is used to exchange data with the central database or remote control center; The control module is electrically connected to the acquisition module, the analysis module, the storage module, and the communication module, and is responsible for the operation control of the entire robot. The mobile platform includes: The chassis, as the foundation and support structure of the mobile platform, is used to support the robot body. The chassis is designed with mounting points for mounting wheel hubs and multiple connection interfaces. Some connection interfaces are used to connect to the robot body, and some connection interfaces are used to connect to the adjustable suspension system. Deformable tires are mounted under the chassis and connected to the chassis via wheel hubs and an adjustable suspension system. The deformable tires can automatically adjust their shape and / or air pressure according to terrain features. An adjustable suspension system, connecting the chassis and the deformable tires, is used to adjust the distance between the robot body and the ground to adapt to different terrain undulations. A floating device is installed around or below the chassis and fixedly connected to the chassis to provide buoyancy for the mobile platform, enabling the mobile platform to float stably on the water surface. The control module includes an adaptive acquisition module, which uses machine learning algorithms to automatically adjust the acquisition strategy based on environmental changes, including weather and terrain, and optimize the acquisition time, acquisition sequence, and acquisition path. The machine learning algorithm is used to learn from historical acquisition data and environmental data to continuously optimize the acquisition strategy.

2. The intelligent robot for environmental sample collection of claim 1, wherein, The data acquisition module includes: a soil collector, which includes: The telescopic drill rod is composed of multiple interlocking rod sections, and its length can be extended or retracted by adjusting the relative positions of the rod sections to adapt to soil sampling needs at different depths. A multi-functional sampling head is connected to the lower end of the drill rod. The sampling head is equipped with a soil hardness sensor and an adjustment mechanism. The soil hardness sensor is used to detect the soil hardness in real time, obtain the soil hardness detection signal, and feed it back to the control module. The adjustment mechanism is connected to the drill rod and is used to drive the drill rod to adjust the drilling force according to the control command of the control module. The control module includes a drilling force control component, which is electrically connected to the soil hardness sensor and the adjustment mechanism. It is used to receive the hardness detection signal from the soil hardness sensor and control the adjustment mechanism to adjust the drilling force of the drill rod according to the hardness detection signal, so as to achieve adaptive soil sampling.

3. The intelligent robot for environmental sample collection of claim 1, wherein, The collection module includes a water quality collector, which includes a suction pump and a filtration system. The suction pump is used to draw water samples from the water body and connects to the filtration system through a pipe or hose to deliver the drawn water samples to the filtration system. The filtration system is located adjacent to the suction pump and is used to receive and process the water samples delivered from the suction pump to remove impurities with a particle diameter larger than a preset diameter threshold.

4. The intelligent robot for environmental sample collection of claim 1, wherein, The acquisition module includes an air collector; the air collector is equipped with an air intake pump and an expandable gas collection bag; the air intake pump is used to draw in air samples and is connected to the gas collection bag through an airtight connecting pipe to ensure that the drawn air samples can be completely delivered into the gas collection bag; the gas collection bag is located adjacent to the air intake pump and is used to receive and temporarily store the air samples delivered from the air intake pump.

5. The intelligent robot for environmental sample collection of claim 1, wherein, The analysis module includes: Chemical sensors are used to detect the presence and concentration of predetermined chemical substances in samples; Temperature and humidity sensors are used to measure the temperature and humidity of samples. Spectroscopic analysis instruments are used to analyze the spectral characteristics of samples to determine the types and amounts of elements and compounds in the samples.

6. The intelligent robot for environmental sample collection of claim 5, wherein, The analysis module also includes a data processing unit for collecting and jointly analyzing data from various sensors and spectral analysis instruments to assess the quality and pollution status of environmental samples.

7. The intelligent robot for environmental sample collection of claim 1, wherein, The system further includes an intelligent navigation system, which comprises: The GPS module is used for global positioning and provides the robot's longitude, latitude, and altitude information in real time. Geomagnetic sensors are used to detect geomagnetic field information and use this information to assist in positioning. LiDAR is used to detect surrounding obstacles and terrain, enabling precise positioning and autonomous navigation.

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