Industrial inspection robot applied to ultrasonic detection and control method

Abstract

The invention relates to the technical field of intelligent inspection, in particular to an industrial inspection robot applied to ultrasonic detection and a control method, and the industrial inspection robot comprises an automatic guide vehicle which is used for carrying out navigation advancing according to a preset inspection route; the sensor integration module is installed on the automatic guide vehicle in a lifting mode through the lifting mechanism and used for collecting data of equipment at different heights; the sensor control unit is used for generating optimal working parameters of the sensor according to the historical data and the environmental data acquired by the sensor integrated module and controlling the sensor integrated module to perform ultrasonic data acquisition according to the optimal working parameters of the sensor; working parameters of the ultrasonic sensor can be dynamically adjusted according to environmental data, and the accuracy and reliability of detection are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent inspection, and particularly to an industrial inspection robot and a control method applied to ultrasonic detection. Background Art

[0002] In modern industrial production, inspection robots can perform inspection tasks in harsh environments such as high temperature, high pressure, toxic and harmful environments, greatly improving production efficiency and safety. However, industrial environments are usually complex and dynamic. For example, environmental factors such as temperature, humidity, and gas concentration may change suddenly.

[0003] Existing industrial inspection robots usually use sensors with fixed parameters for environmental detection. For example, ultrasonic sensors work with fixed emission frequencies, detection ranges, and sensitivities. This method can meet basic requirements in relatively stable environments, but when the environment changes suddenly, ultrasonic sensors with fixed parameters are difficult to adapt, resulting in a significant reduction in detection accuracy and reliability, and even possible false detections or missed detections, thus affecting the inspection effect and safe production.

[0004] Therefore, there is an urgent need for an industrial inspection robot and a control method applied to ultrasonic detection to solve the above problems. Summary of the Invention

[0005] The present invention describes an industrial inspection robot and a control method applied to ultrasonic detection, which solve the problem of how to dynamically adjust the working parameters of ultrasonic sensors in industrial inspection robots according to environmental data to improve the accuracy and reliability of detection.

[0006] According to a first aspect, the present invention provides an industrial inspection robot applied to ultrasonic detection, comprising:

[0007] An automatic guided vehicle for navigating and traveling according to a preset inspection route;

[0008] A lifting mechanism and a sensor integration module, the sensor integration module being liftably mounted on the automatic guided vehicle by means of the lifting mechanism for collecting data of equipment at different heights;

[0009] A sensor control unit that generates the best working parameters of the sensor according to historical data and environmental data collected by the sensor integration module, and controls the sensor integration module to collect ultrasonic data according to the best working parameters of the sensor.

[0010] According to a second aspect, the present invention provides a control method for an industrial inspection robot applied to ultrasonic detection, the method comprising:

[0011] Collect data on multiple groups of environmental mutation events in the industrial area from historical records;

[0012] Perform spatial clustering analysis on the environmental mutation event data according to the occurrence locations of the environmental mutation event data, identify areas where the environmental mutation frequency exceeds a preset threshold, and form a set of dynamic risk areas;

[0013] Obtain the preset inspection route of the inspection robot, identify inspection nodes that spatially overlap with the set of dynamic risk areas, and mark them as high-risk inspection nodes;

[0014] In response to reaching a preset distance before the inspection robot arrives at the high-risk inspection node, collect real-time environmental parameters;

[0015] Input the real-time environmental parameters and the environmental mutation frequency corresponding to the high-risk inspection node into a pre-constructed environmental mutation prediction model to obtain an environmental mutation index when the inspection robot arrives at the high-risk inspection node;

[0016] Traverse the environmental mutation index in a pre-established sensor operation parameter comparison library to determine the optimal working parameters of the ultrasonic sensor when the inspection robot arrives at the high-risk inspection node;

[0017] When the inspection robot arrives at the high-risk inspection node, control the ultrasonic sensor to collect data based on the optimal working parameters.

[0018] In a third aspect, an embodiment of this specification also provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the method described in any embodiment of this specification is implemented.

[0019] In a fourth aspect, an embodiment of this specification also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed on a computer, the computer is made to execute the method described in any embodiment of this specification.

[0020] According to the industrial inspection robot and control method for ultrasonic detection provided by the present invention, the industrial inspection robot can automatically navigate and move according to a preset route, realize automatic inspection of equipment at different heights, and improve the inspection efficiency; through the intelligent regulation of integrating multiple sensors and a sensor control unit, the working parameters of the ultrasonic sensor can be dynamically adjusted according to environmental data, improving the accuracy and reliability of detection. Description of the Drawings

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0022] Figure 1 Shows a schematic structural diagram of an industrial inspection robot applied to ultrasonic detection according to an embodiment;

[0023] Figure 2 Shows a schematic block diagram of a sensor control unit according to an embodiment;

[0024] Figure 3 Shows a schematic flowchart of a control method for an industrial inspection robot applied to ultrasonic detection according to an embodiment;

[0025] Reference numerals in the drawings: 1, automatic guided vehicle; 2, lifting mechanism; 3, sensor integration module. Detailed implementation manners

[0026] The following will describe the solution provided by the present invention with reference to the drawings.

[0027] Embodiment 1: As Figure 1 and Figure 2 shown, the industrial inspection robot applied to ultrasonic detection of the present invention includes:

[0028] An automatic guided vehicle 1, which is used to navigate and travel according to a preset inspection route;

[0029] A lifting mechanism 2 and a sensor integration module 3. The sensor integration module 3 is installed on the automatic guided vehicle 1 in a liftable manner by relying on the lifting mechanism 2 and is used to collect data from equipment at different heights; the sensor integration module 3 is integrated with an ultrasonic sensor, a temperature sensor, a humidity sensor, and a gas sensor;

[0030] A sensor control unit, which generates the best working parameters of the sensor according to historical data and the environmental data collected by the sensor integration module 3, and controls the ultrasonic sensor to collect data according to the best working parameters of the sensor.

[0031] The industrial inspection robot of this embodiment is mainly used to perform ultrasonic detection on various types of equipment in a complex industrial environment. By integrating multiple sensors and an intelligent control system, it realizes the automatic inspection of equipment at different heights and dynamically adjusts the working parameters of the ultrasonic sensor according to environmental data to improve the accuracy and efficiency of detection.

[0032] Specifically, the automated guided vehicle 1 uses a mobile chassis with high-precision positioning and navigation functions, equipped with various sensors such as lidar and vision sensors to sense the surrounding environment and build a map; it is installed with a high-performance processor and a large-capacity memory to run navigation algorithms and control programs, as well as store inspection data and map information; it is equipped with a wireless communication module, such as a 4G / 5G module or a Wi-Fi module, for data transmission and instruction interaction with the remote monitoring center; before starting the inspection, the inspection robot downloads the preset inspection route information through the remote monitoring center, and this information includes the coordinates of each inspection node, the location of the equipment, etc.; during the inspection process, the automated guided vehicle real-time locates its own position based on the environmental information obtained by the lidar and vision sensors, and matches it with the preset map; based on navigation algorithms, such as path planning algorithms and obstacle avoidance algorithms, the automated guided vehicle can automatically plan the optimal path, avoid obstacles, and navigate along the preset inspection route.

[0033] The lifting mechanism 2 uses an electric lifting platform, which consists of a motor, a transmission device (such as gears, chains), guide rails, limit switches, etc.; it is equipped with a height sensor to monitor the lifting height of the platform in real time and feed the data back to the sensor control unit; according to the requirements of the inspection task, the sensor control unit controls the motor to drive the transmission device, so that the lifting platform drives the sensor integration module 3 to perform lifting motion; during the lifting process, the height sensor monitors the platform height in real time, and when the preset height position is reached, the limit switch is triggered and the motor stops working to ensure that the platform accurately stops at the specified height.

[0034] The sensor integration module 3 integrates an ultrasonic sensor, a temperature sensor, a humidity sensor, and a gas sensor; the ultrasonic sensor is used to emit and receive ultrasonic signals to detect defects on the surface or inside of the equipment; the temperature sensor, humidity sensor, and gas sensor are respectively used to collect information such as ambient temperature, humidity, and gas concentration; each sensor is connected to the microcontroller through a signal conditioning circuit, and the microcontroller is responsible for collecting, processing, and transmitting sensor data; after the robot reaches the inspection node, the sensor control unit generates the best working parameters of the sensors based on the current environmental data and historical data, and sends the parameters to the microcontroller of the sensor integration module; the microcontroller adjusts the working states of each sensor according to the received parameters, then controls the ultrasonic sensor to perform ultrasonic detection on the equipment, and at the same time collects environmental data such as temperature, humidity, and gas concentration; the collected data is processed by the microcontroller and sent to the processor of the automated guided vehicle through the wireless communication module, and then uploaded to the remote monitoring center by the processor.

[0035] The sensor control unit adopts a high-performance embedded processor, which has computing and data processing capabilities; it is equipped with a large-capacity memory for storing information such as historical inspection data, environmental data, and the best working parameters of sensors; it has multiple communication interfaces for communicating with the automatic guided vehicle, the lifting mechanism, and the sensor integration module respectively; it receives the environmental data sent by the sensor integration module 3, including information such as temperature, humidity, and gas concentration, and performs preprocessing operations such as filtering and denoising on the data to improve data quality; according to the historical inspection data and the preprocessed environmental data, using machine learning algorithms or mathematical models, it analyzes the influence of various sensor parameters on the detection results in different environments, generates the best working parameters of the sensors in the current environment; and sends the generated best working parameters of the sensors to the microcontroller of the sensor integration module 3 to control the ultrasonic sensor and other sensors to work according to the best parameters.

[0036] The industrial inspection robot of this embodiment can automatically navigate and move according to a preset route, realizing the automatic inspection of equipment at different heights and improving the inspection efficiency; through the intelligent regulation of integrating multiple sensors and the sensor control unit, it can dynamically adjust the working parameters of the ultrasonic sensor according to the environmental data, improving the accuracy and reliability of detection.

[0037] In an embodiment of the present invention, the sensor control unit further includes the following sub-modules:

[0038] The historical data collection module 200 is used to collect multiple groups of environmental mutation event data in the industrial area in the historical records; this module extracts data related to environmental mutations from various data sources such as various monitoring devices, accident reports, and log records in the industrial area to provide a basis for subsequent analysis;

[0039] The spatial clustering analysis module 202 is used to perform spatial clustering analysis on the environmental mutation event data according to the occurrence locations of the environmental mutation event data collected by the historical data collection module, identify the areas where the environmental mutation frequency exceeds a preset threshold, and form a set of dynamic risk areas; through this module, the system can accurately locate the high-frequency risk areas in the industrial area;

[0040] The high-risk node marking module 204 is used to obtain the preset inspection route of the inspection robot, identify the inspection nodes that have spatial overlap with the set of dynamic risk areas, and mark them as high-risk inspection nodes; enabling the system to clarify the key attention areas during the inspection process;

[0041] The environmental parameter acquisition module 206 collects real-time environmental parameters in response to a preset distance before the inspection robot reaches a high-risk inspection node; this module uses various sensors installed on the inspection robot, such as temperature sensors, humidity sensors, gas sensors, etc., to obtain the parameter information of the current environment in real time;

[0042] An environmental mutation index calculation module 208 is configured to input real-time environmental parameters and the environmental mutation frequency corresponding to high-risk inspection nodes into a pre-constructed environmental mutation prediction model to obtain an environmental mutation index when the inspection robot reaches a high-risk inspection node; the environmental mutation index comprehensively reflects the possibility of environmental mutation at this node.

[0043] An optimal parameter determination module 210 is configured to traverse the environmental mutation index in a pre-established sensor operation parameter comparison library to determine the optimal working parameters of the ultrasonic sensor when the inspection robot reaches a high-risk inspection node.

[0044] A data acquisition control module 212, in response to the inspection robot reaching a high-risk inspection node, controls the ultrasonic sensor to perform data acquisition based on the optimal working parameters determined by the optimal parameter determination module; this module sends control instructions to the ultrasonic sensor to adjust its working parameters such as transmission frequency, detection range, and sensitivity, so that it performs data acquisition in an optimal state, thereby ensuring detection accuracy and reliability.

[0045] In this embodiment, the entire system realizes a high degree of automation and intelligence from historical data collection to final data acquisition control; each sub-module works together, and the sensor parameters can be automatically adjusted according to real-time environmental changes without manual intervention, improving the inspection efficiency and accuracy; through the spatial clustering analysis module, the system can accurately identify high-frequency risk areas in the industrial area, enabling targeted allocation of inspection resources, not only improving the inspection efficiency but also reducing unnecessary resource consumption; the environmental parameter acquisition module and the environmental mutation index calculation module can sense environmental changes in real time and predict possible future environmental mutations, enabling the system to respond before environmental changes occur and adjust sensor parameters in advance, thereby effectively avoiding false detections or missed detections; the optimal parameter determination module can find the optimal working parameters in the sensor operation parameter comparison library according to the environmental mutation index, ensuring that the ultrasonic sensor works in an optimal state in the current environment, not only improving the detection accuracy and reliability but also extending the service life of the sensor; by intelligently adjusting sensor parameters, the system can maintain high-efficiency inspection capabilities in complex environments, timely discover potential safety hazards, not only improving the safety of industrial production but also reducing production losses caused by equipment failures or accidents.

[0046] Embodiment 2: As Figure 3 shown, the control method of the industrial inspection robot applied to ultrasonic detection of the present invention specifically includes the following steps;

[0047] Step 100: Collect multiple groups of environmental mutation event data in the industrial area in historical records.

[0048] Step 102: According to the occurrence location of the environmental mutation event data, perform spatial clustering analysis on the environmental mutation event data to identify areas where the environmental mutation frequency exceeds a preset threshold, and form a set of dynamic risk areas;

[0049] Step 104: Obtain the preset inspection route of the inspection robot, identify inspection nodes that have spatial overlap with the set of dynamic risk areas, and mark them as high-risk inspection nodes;

[0050] Step 106: In response to reaching a preset distance before the inspection robot arrives at the high-risk inspection node, collect real-time environmental parameters;

[0051] Step 108: Input the real-time environmental parameters and the environmental mutation frequency corresponding to the high-risk inspection node into a pre-constructed environmental mutation prediction model to obtain the environmental mutation index when the inspection robot arrives at the high-risk inspection node;

[0052] Step 110: Traverse the environmental mutation index in a pre-established sensor operation parameter comparison library to determine the optimal working parameters of the ultrasonic sensor when the inspection robot arrives at the high-risk inspection node; different sensor optimal working parameters are set in the sensor operation parameter comparison library according to different environmental mutation indexes;

[0053] Step 112: When the inspection robot arrives at the high-risk inspection node, control the ultrasonic sensor to collect data based on the optimal working parameters.

[0054] In this embodiment, the adaptability of the industrial inspection robot in a complex environment is effectively improved through a multi-dimensional dynamic adjustment mechanism; firstly, based on the spatial clustering analysis of historical environmental mutation events, high-frequency risk areas can be accurately identified, realizing targeted allocation of inspection resources and avoiding computational redundancy caused by global parameter adjustment; secondly, introducing a set of dynamic risk areas in the inspection path planning, combining the preset distance to trigger environmental parameter collection, and constructing a "prediction-response" two-stage mechanism, which can not only predict risks in advance but also sense environmental changes in real time, significantly shortening the system response delay; through the environmental mutation prediction model, multi-dimensional correlation between real-time parameters and historical frequencies is carried out, making the calculation of the mutation index more relevant to environmental characteristics; the sensor parameter comparison library adopts a hierarchical mapping strategy to discretize continuous environmental indexes into optimal parameter combinations, which not only ensures the adjustment accuracy but also reduces the real-time calculation load; in the spatial dimension, a risk heat map is established through clustering, in the time dimension, prediction is combined with historical frequencies and real-time data, and in the parameter dimension, a multi-objective optimization function is constructed to determine the optimal working point; the multi-dimensional cooperation mechanism enables the ultrasonic sensor to dynamically adapt to the coupled changes of multiple factors such as temperature and humidity, optimizing the system energy efficiency while ensuring the detection accuracy.

[0055] In an embodiment of the present invention, due to the conduct of various production activities, environmental factors such as temperature, humidity, and gas concentration may suddenly change, and mutation events are likely to pose threats to production efficiency and safety; collecting historical data of mutation events can help understand the laws of environmental mutations, predict future mutation events, and adjust the working parameters of the sensors of the inspection robot accordingly, thereby improving the accuracy and reliability of inspection; the collected environmental mutation event data includes but is not limited to:

[0056] Timestamp of the environmental mutation event: Recording the exact time when the mutation event occurs helps analyze the periodic or seasonal laws of the mutation event;

[0057] Type of the mutation event: Distinguish whether it is a temperature mutation, humidity mutation, gas concentration mutation, etc. Different types of mutation events have different effects on the working parameters of the sensors;

[0058] Intensity of the mutation event: Quantifying the severity of the mutation event, such as the amplitude of temperature change, the percentage of humidity change, etc., helps evaluate the impact of the mutation event on the production environment;

[0059] Location information of the mutation event: Recording the specific location where the mutation event occurs is the key information for subsequent spatial clustering analysis;

[0060] Other relevant parameters: Such as the operating status of production equipment, personnel activities, etc., helps to more comprehensively understand the causes and impacts of mutation events.

[0061] Specifically, the data collection methods include but are not limited to:

[0062] a. Arrange multiple sensors in the industrial area to monitor the changes of environmental factors in real time and record the data of mutation events;

[0063] b. Query historical data from the databases such as the enterprise's production management system and environmental monitoring system;

[0064] c. For some data that cannot be automatically recorded, it can be obtained through manual observation and recording.

[0065] In this embodiment, by simultaneously collecting time stamps, event types, intensities, locations, and other associated parameters, a multi-dimensional data system of environment-device-time-space is constructed; the association of such multi-source heterogeneous data can reveal the potential causal relationship between environmental mutations and production activities (for example, the start and stop of equipment leading to sudden temperature changes), providing more comprehensive feature inputs for subsequent prediction models; the combination of precise location information (three-dimensional coordinates) and millisecond-level time stamps supports the modeling of the spatio-temporal propagation law of mutation events (such as the gas leakage diffusion path), which cannot be achieved by traditional single-dimensional data analysis; by arranging multiple sensors in the industrial area (item a), querying historical data from the enterprise's production management system and environmental monitoring system (item b), and manual observation and recording (item c), the comprehensiveness and accuracy of the data can be ensured; the data from different sources complement each other, reducing the bias that may be brought by a single data source.

[0066] In one embodiment of the present invention, the process of obtaining the set of dynamic risk areas includes the following steps:

[0067] Step S21: Extract the location information of each data record from the multi-group environmental mutation event data collected in step S100; these location information may be presented in different forms, such as geographical coordinates (latitude and longitude), relative coordinates based on the internal map of the factory, or specific area numbers, etc.; first, these different forms of location information need to be uniformly converted into a format convenient for processing, for example, mapping all location information to a unified coordinate system with the factory center as the origin to ensure the consistency and comparability of the data.

[0068] There are various algorithms available for spatial clustering analysis, such as DBSCAN (density-based spatial clustering of applications with noise), K-Means++ (improved K-Means algorithm), etc.; according to the characteristics of the environmental mutation event data in the industrial environment, select a suitable clustering algorithm; taking the DBSCAN algorithm as an example, it does not require prior knowledge of the number of clusters to be formed, can discover clusters of any shape, and can identify noise points in the data set; when applying this algorithm, two key parameters need to be set: the neighborhood radius Eps and the minimum number of points MinPts in the neighborhood; by continuously adjusting these two parameters, perform clustering analysis on the location information of the environmental mutation event data, and divide the points that are adjacent in space and where environmental mutations are relatively frequent into the same cluster; at the same time, the setting of the parameters needs to be adjusted according to factors such as the size of the industrial area and the distribution density of environmental mutation events. For example, in a relatively large industrial area with relatively sparse environmental changes, the neighborhood radius can be set larger and the minimum number of points can be appropriately increased to avoid misjudging isolated normal fluctuations as mutation clusters.

[0069] Step S23: After completing the clustering analysis, calculate the occurrence frequency of environmental mutation events within each cluster; compare these frequencies with a preset threshold. When the environmental mutation frequency of a certain cluster exceeds the preset threshold, the area corresponding to this cluster is identified as an area with an environmental mutation frequency exceeding the preset threshold, that is, a dynamic risk area; the setting of the preset threshold needs to comprehensively consider factors such as the actual requirements of industrial production, the statistical characteristics of historical environmental mutation data, and the risk tolerance. For example, if an industrial area has a low tolerance for environmental mutations, the preset threshold can be set relatively low to ensure that potential high-risk areas can be identified in a timely manner.

[0070] In this embodiment, taking the DBSCAN algorithm as an example, it clusters based on the density of data points; data points within a high-density area are divided into a cluster, while data points in a low-density area are regarded as noise points; this density-based clustering method can well adapt to the characteristic that environmental mutation events in the industrial environment are unevenly distributed in space, and accurately identify dynamic risk areas of different shapes and sizes, unlike some distance-based clustering algorithms (such as the K-Means algorithm) that can only identify spherical clusters; by adjusting the parameters of the clustering algorithm and the preset threshold, it can be flexibly set according to the characteristics and requirements of different industrial scenarios, adapting to different environmental mutation patterns and risk assessment criteria; for example, in an electronic manufacturing factory, since the production environment has strict requirements for temperature and humidity, the environmental mutation pattern and frequency are different from those of chemical enterprises. By reasonably adjusting the parameters and thresholds, spatial clustering analysis can accurately identify the dynamic risk areas within the electronic manufacturing factory.

[0071] In an embodiment of the present invention, in order to accurately identify the inspection nodes on the inspection route of the inspection robot that may face a relatively high environmental mutation risk, the following method is adopted:

[0072] The preset inspection route of the inspection robot is usually determined by factors such as the layout of industrial production, equipment distribution, and production process; the route information is stored in the control system of the robot in the form of a sequence of coordinate points, map markers, or path planning files; for example, in a large factory, the preset route of the inspection robot may be to inspect each key equipment in turn according to the distribution of workshops, and the position of each inspection point is accurately recorded in the route planning data.

[0073] The set of dynamic risk regions obtained from step S102 contains multiple regions identified as having an environmental mutation frequency exceeding a preset threshold; these regions may be represented by polygons, circles, or other geometric shapes, and each region has its corresponding position coordinates and range information; for example, in a chemical production area, a certain dynamic risk region determined by spatial clustering analysis may be a circular region centered on a hazardous chemical storage tank with a certain radius, where environmental factors such as temperature and gas concentration change frequently and significantly within this region.

[0074] Compare the position information of each inspection node in the preset inspection route with the positions of the regions in the set of dynamic risk regions; this is achieved through spatial geometric algorithms, such as determining whether a point is inside a polygon, calculating the distance between a point and a circular region, etc.; for example, for a dynamic risk region in the shape of a polygon, use the ray method to determine whether the inspection node is inside the polygon; for a circular dynamic risk region, calculate the distance between the inspection node and the center of the circle, and if the distance is less than or equal to the radius, it is determined that the inspection node is within this region.

[0075] When there is a spatial overlap between the position of an inspection node and a certain dynamic risk region, it is determined that this inspection node is an inspection node with spatial overlap with the set of dynamic risk regions; in actual operation, it may be the case that multiple dynamic risk regions overlap with the same inspection node, and in this case, it is necessary to comprehensively consider the risk levels and influencing factors of each region to comprehensively evaluate the risk level of this inspection node.

[0076] In this embodiment, by comparing the position of each inspection node in the preset inspection route with the set of dynamic risk regions, it is possible to accurately identify which inspection nodes are located in or close to high-risk regions; not only consider whether the node is completely within the risk region, but also evaluate the proximity of the node through means such as distance calculation to ensure that potential risk points are not missed; through the spatial overlap analysis of the inspection route and the dynamic risk regions, the inspection strategy can be adjusted targeted, improving the overall efficiency of the inspection; timely discovery and response to potential risk points helps to take preventive measures in advance, reducing the likelihood of accidents and ensuring production safety.

[0077] In an embodiment of the present invention, in order to ensure that the inspection robot can perceive environmental changes in advance when approaching a high-risk region, it is necessary to set a mechanism for triggering environmental parameter collection at a preset distance before reaching a high-risk inspection node. The specific implementation is as follows:

[0078] Determine a preset distance, that is, how far before the inspection robot reaches a high-risk inspection node to start collecting real-time environmental parameters; the selection of the preset distance should not only ensure sufficient time for data analysis and sensor parameter adjustment, but also avoid data redundancy caused by premature collection; the determination of the preset distance is usually based on factors such as the size of the industrial area, the speed of environmental change, and the moving speed of the inspection robot; for example, in a large factory, if the environmental change is relatively slow, a larger preset distance can be set; while in an environment with rapid changes, the preset distance should be shortened to ensure the timeliness of data; the preset distance also needs to be dynamically adjusted according to the actual operation situation; for example, through historical data analysis and real-time monitoring results, optimize the preset distance to achieve the best effect.

[0079] When the inspection robot approaches the high-risk inspection node to the preset distance, trigger the environmental parameter collection mechanism; it is realized through the position trigger method or the time trigger method: specifically, the position trigger uses the positioning system of the inspection robot (such as GPS or indoor positioning system) to real-time monitor its current position, and once the distance between the robot and the target node is less than or equal to the preset distance, immediately trigger the collection of environmental parameters; the time trigger calculates the estimated time to reach the high-risk node according to the inspection route plan and the moving speed of the robot, and triggers the collection at a specific time point before arrival.

[0080] After triggering the collection, start collecting real-time environmental parameters; the real-time environmental parameters include but are not limited to temperature, humidity, gas concentration, etc., which specifically depend on the characteristics and requirements of the industrial environment; more specifically:

[0081] Temperature parameter: Obtain the temperature value of the current position through the high-precision temperature sensor equipped on the inspection robot; this temperature sensor has high sensitivity and a wide measurement range, and can accurately measure the temperature change in the industrial environment. For example, in a chemical production environment with high temperature and high pressure, its measurement range can reach -50°C to 500°C, and the accuracy can reach ±0.1°C;

[0082] Humidity parameter: Use a humidity sensor to collect the air humidity information of the current position; the humidity sensor can adapt to harsh conditions such as high humidity and high dust in the industrial environment, and the measurement range is usually 0%RH to 100%RH, and the accuracy can reach ±2%RH;

[0083] Gas concentration parameter: For different industrial production scenarios, equip corresponding gas sensors to detect the concentration of specific gases; for example, in chemical production, it is necessary to detect the concentration of combustible gases (such as methane, acetylene, etc.) and toxic and harmful gases (such as carbon monoxide, hydrogen sulfide, etc.); the measurement range and accuracy of the gas sensor depend on the specific gas type. For example, the measurement range of a methane gas sensor can be 0-100%LEL (lower explosion limit), and the accuracy can reach ±5%FS (full scale);

[0084] Other relevant parameters: In addition to the above main environmental parameters, other parameters related to the inspection task can be collected according to requirements, such as pressure, wind speed, light intensity, etc.; which are used to assist in analyzing the impact of environmental changes on production equipment and process.

[0085] Data collection is carried out in real-time to ensure that the environmental parameter changes near the high-risk inspection nodes of the inspection robot can be obtained in a timely manner. The collection frequency is adjusted according to the speed of environmental change and the requirements of the inspection task; for example, for relatively stable parameters such as temperature and humidity, the collection frequency can be set to once per second; while for parameters such as gas concentration that change relatively quickly, the collection frequency can be increased to 5 times per second or even higher to capture the instantaneous changes in environmental mutations.

[0086] In this embodiment, the preset distance is set by comprehensively considering factors such as the size of the industrial area, the speed of environmental change, and the moving speed of the robot, ensuring that sufficient time is reserved for data analysis and sensor parameter adjustment while avoiding data redundancy; the way of dynamically adjusting the preset distance is further optimized through historical data analysis and real-time monitoring results, which can better adapt to the complex environmental changes in different industrial scenarios and always maintain the best timing for data collection; two triggering methods, position triggering and time triggering, are provided; position triggering uses the positioning system to monitor the position in real-time and triggers immediately once the distance is reached, with high accuracy; time triggering calculates the triggering time point in advance according to the route planning and moving speed, with foresight; the two methods complement each other and are applicable to different environmental and task requirements to ensure that environmental parameter collection can be triggered in a timely and accurate manner in various situations; the collected parameters cover a variety of key environmental parameters such as temperature, humidity, and gas concentration, and the specific collection content is determined according to the characteristics and requirements of the industrial environment; corresponding high-precision and adaptable sensors are equipped for different parameters, such as wide-range and high-precision temperature sensors suitable for chemical environments, humidity sensors that can accurately measure under harsh conditions, and various gas sensors for detecting different gases, etc., to comprehensively obtain the environmental information affecting production; at the same time, other relevant parameters can also be collected to assist in analysis, providing richer data support for the inspection task; the real-time collection method can timely capture the environmental parameter changes of the inspection robot near the high-risk inspection nodes; the collection frequency is flexibly adjusted according to the speed of parameter change and the requirements of the inspection task, using a lower frequency for relatively stable parameters to reduce the data volume; increasing the frequency for parameters that change quickly to ensure capturing instantaneous mutations, effectively balancing the data collection volume and the timeliness of environmental change monitoring, and improving the effectiveness and utilization rate of data.

[0087] In an embodiment of the present invention, the environmental mutation index is calculated using the environmental mutation prediction model as follows:

[0088] Prepare the input data, including real-time environmental parameters and the environmental mutation frequency corresponding to high-risk inspection nodes. The real-time environmental parameters are obtained from step 106, and the data of the environmental mutation frequency corresponding to high-risk inspection nodes comes from the result obtained by performing spatial clustering analysis on the environmental mutation event data in step S102. Each high-risk inspection node has its corresponding environmental mutation frequency, which represents the frequency of environmental mutations in this area over a past period of time and is an important indicator for evaluating the risk level of this area.

[0089] Use the collected real-time environmental parameters and the environmental mutation frequency of the corresponding high-risk inspection nodes as inputs and provide them to the pre-constructed environmental mutation prediction model. The environmental mutation prediction model is trained based on a large amount of historical environmental data and advanced machine learning algorithms, and can learn the complex relationship between environmental parameters and the environmental mutation frequency, so as to predict future environmental mutation situations.

[0090] The environmental mutation prediction model performs feature extraction and analysis on the input real-time environmental parameters and environmental mutation frequency; analyzes the mutual relationship between different environmental parameters and the correlation degree between real-time environmental parameters and the environmental mutation frequency; for example, the model analyzes and finds that temperature and gas concentration will change simultaneously in some cases, and there is a certain linear relationship between this change and the environmental mutation frequency; by performing multi-dimensional association of real-time parameters and historical frequencies, the model can comprehensively consider the influence of various factors on environmental mutations; it will calculate the corresponding environmental mutation possibility under the current input parameters according to the change law of the environmental mutation frequency under different combinations of environmental parameters in historical data; for example, if the current temperature is high and the gas concentration is also large, the model will refer to the environmental mutation frequency in similar situations in history and combine the specific values of the current parameters to calculate the possibility of this environmental mutation; based on the above analysis and calculation, the environmental mutation prediction model finally generates an environmental mutation index; this index is a numerical value comprehensively reflecting the possibility of environmental mutations when the inspection robot arrives at high-risk inspection nodes, and the value range can be set according to the actual situation, such as 0 - 100, and the higher the value, the greater the possibility of environmental mutations.

[0091] In this embodiment, the environmental mutation prediction model combines the environmental mutation frequency and real-time environmental parameters monitored in real time, and can more accurately predict future environmental change trends. It not only considers the actual situation of the current environment, but also refers to the change law in similar situations in the past, making the prediction result more reliable. By performing multi-dimensional association of real-time parameters and historical frequencies, the model can comprehensively consider the influence of various factors on environmental mutations. For example, the mutual relationship between different environmental parameters such as temperature, humidity, and gas concentration and their correlation degree with the environmental mutation frequency, improving the accuracy of prediction.

[0092] More specifically, the method for constructing an environmental mutation prediction model includes collecting historical industrial environment data, covering various environmental parameters and records of environmental mutation events, determining the environmental mutation frequencies at high-risk inspection nodes, cleaning the data to remove anomalies and duplicate values, and standardizing different parameters; extracting features such as statistics and correlations from the environmental parameters and mutation frequencies, such as mean, rate of change, and correlations between parameters, and screening key features using methods such as correlation analysis to reduce the data dimension; selecting a model according to the data characteristics, such as linear regression, random forest, or neural network, dividing the data into training, validation, and test sets, training the model with the training set, and adjusting the hyperparameters using the validation set to prevent overfitting; evaluating the model with the test set, measuring the performance using metrics such as MSE, and optimizing the model according to the evaluation results through regularization, increasing or decreasing data, or adjusting the structure to improve the prediction accuracy. Through the above steps, the model can comprehensively consider various environmental parameters and corresponding environmental mutation frequencies, and accurately predict the possibility of environmental mutations when the inspection robot reaches high-risk inspection nodes.

[0093] In an embodiment of the present invention, the sensor operating parameter reference library is a pre-constructed data structure or database that contains the optimal operating parameters of ultrasonic sensors corresponding to different environmental mutation indices; the design and construction of the sensor operating parameter reference library need to comprehensively consider multiple factors, including but not limited to the following:

[0094] Environmental mutation index: The environmental mutation index obtained from step S108, which represents the possibility and severity of mutations in the current environment;

[0095] Sensor parameters: The operating parameters of the ultrasonic sensor, such as transmission frequency, detection range, sensitivity, etc.; the sensor parameters determine the detection performance of the sensor in a specific environment;

[0096] The design of the reference library adopts a hierarchical mapping strategy, discretizing the continuous environmental index into optimal parameter combinations, which not only ensures the adjustment accuracy but also reduces the real-time calculation load.

[0097] According to the environmental mutation index, query and match in the sensor operating parameter reference library to find the most suitable combination of sensor operating parameters; divide the continuous environmental mutation index into several intervals (such as low risk, medium risk, high risk), and each interval corresponds to a set of optimal operating parameters; for example:

[0098] Low-risk interval: The environmental mutation index is between 0 and 30, and the corresponding sensor parameters are low frequency, short detection range, and low sensitivity;

[0099] Medium-risk interval: The environmental mutation index is between 31 and 70, and the corresponding sensor parameters are medium frequency, medium detection range, and medium sensitivity;

[0100] High-risk range: The environmental mutation index is between 71 and 100, and the corresponding sensor parameters are high frequency, long detection range, and high sensitivity.

[0101] To further improve the selection accuracy of sensor parameters, a multi-objective optimization function can be constructed, comprehensively considering factors such as detection accuracy and system energy efficiency to determine the optimal operating parameters; by adjusting the operating parameters of the sensor (such as frequency, sensitivity, etc.), ensure high detection accuracy can still be maintained in a complex environment; optimize the operating parameters of the sensor, reduce unnecessary energy consumption, and ensure the stability of the system during long-term operation.

[0102] In this embodiment, through a pre-constructed comparison library of sensor operating parameters, the most suitable sensor operating parameters can be quickly queried and selected according to the environmental mutation index, ensuring high detection accuracy and reliability in a complex environment; according to different environmental mutation indices, dynamically adjust the operating parameters of the ultrasonic sensor, enabling the sensor to flexibly adapt to the coupled changes of various factors such as temperature and humidity, and improving the overall adaptability of the system; when selecting sensor parameters, not only consider detection accuracy but also take into account system energy efficiency, and determine the optimal operating point through a multi-objective optimization function to ensure optimizing the energy efficiency of the system while guaranteeing detection accuracy and extending the service life of the equipment; through the "prediction-response" two-stage mechanism, anticipate risks in advance and adjust sensor parameters before reaching high-risk inspection nodes, significantly shortening the system's response delay and improving the overall inspection efficiency.

[0103] In an embodiment of the present invention, when the inspection robot reaches the high-risk inspection node marked in step S104, step S112 is triggered; this trigger condition is clear and precise, ensuring that the sensor starts to work with optimized parameters at the position where flexible adjustment is most needed; in a complex industrial environment, high-risk inspection nodes have a high probability of environmental mutation and extremely high requirements for sensor adaptability; only by adjusting parameters and collecting data when reaching this specific position can it best meet the actual needs, timely capture environmental change information, and provide strong guarantee for safe production.

[0104] Implement control based on the optimal working parameters of the ultrasonic sensor determined in step S110; these optimal working parameters are obtained by traversing and matching in the sensor operation parameter comparison library considering the environmental mutation index comprehensively, covering key parameters such as transmission frequency, detection range, and sensitivity; for example, if the environmental mutation index is in the high-risk range, a high-frequency transmission frequency is correspondingly set, which can enhance the propagation and reflection effects of ultrasonic signals, making the sensor more sensitive to environmental details; a long detection range can expand the detection coverage to ensure timely discovery of potential abnormalities within a large range; a high-sensitivity setting enables the sensor to respond quickly to minor environmental changes, thereby more accurately detecting environmental factors that may affect production safety; through precise control of these parameters, the ultrasonic sensor can operate in an optimal state in a complex and changeable high-risk environment, ensuring the accuracy and reliability of data collection.

[0105] Controlling the ultrasonic sensor for data collection with the optimal working parameters can accurately obtain environmental information at high-risk inspection nodes; in industrial production, minor changes in environmental factors such as temperature, humidity, and gas concentration may have a significant impact on the production process; for example, in a chemical production area, a slight change in the concentration of certain gases may imply a potential leakage risk; through high-precision data collection at this time, these key information can be captured in a timely manner, providing a reliable basis for subsequent risk assessment and decision-making; accurate data collection helps to detect environmental abnormalities in a timely manner and avoid production accidents caused by environmental mutations; for example, in an industrial scenario with high temperature and high pressure, if the sensor can accurately detect an abnormal upward trend in temperature, production personnel can take measures such as cooling in advance to prevent equipment from being damaged due to overheating, ensuring the safe and stable operation of the production process.

[0106] According to an embodiment of another aspect, there is also provided a computer-readable storage medium having a computer program stored thereon, which when executed by a computer, causes the computer to execute the method described in combination with Figure 3 the method described.

[0107] According to an embodiment of still another aspect, there is also provided an electronic device including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, the method described in combination with Figure 3 the method described is implemented.

[0108] Each embodiment in the present invention is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the differences between each embodiment and other embodiments are emphasized. In particular, for the device embodiments, since they are basically similar to the method embodiments, they are described relatively simply, and the relevant parts can be referred to the partial description of the method embodiments.

[0109] Those skilled in the art should be able to realize that in one or more of the above examples, the functions described in the present invention can be implemented by hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium.

[0110] The specific embodiments described above further elaborate on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only for the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solution of the present invention should be included within the protection scope of the present invention.

Claims

1. An industrial inspection robot used for ultrasonic testing, characterized in that: include: An automatic guided vehicle, the automatic guided vehicle is used to navigate according to a preset inspection route; A lifting mechanism and a sensor integrated module, wherein the sensor integrated module can be lifted and installed on an automatic guided vehicle by means of the lifting mechanism, and is used to collect data from equipment at different heights; A sensor control unit, wherein the sensor control unit generates optimal operating parameters of the sensor according to historical data and environmental data collected by the sensor integrated module, and controls the sensor integrated module to collect ultrasonic data according to the optimal operating parameters of the sensor.

2. The industrial inspection robot for ultrasonic testing according to claim 1, characterized in that: The sensor integrated module is integrated with an ultrasonic sensor, a temperature sensor, a humidity sensor and a gas sensor.

3. The industrial inspection robot for ultrasonic testing according to claim 2, characterized in that: The sensor control unit comprises: A historical data collection module, used to collect multiple sets of environmental mutation event data in industrial areas in historical records; The spatial cluster analysis module is used to perform spatial cluster analysis on the environmental mutation event data according to the occurrence locations of the environmental mutation event data collected by the historical data collection module, identify the areas where the environmental mutation frequency exceeds the preset threshold, and form a dynamic risk area set; A high-risk node marking module is used to obtain the preset inspection route of the inspection robot, identify the inspection nodes that have spatial overlap with the dynamic risk area set, and mark them as high-risk inspection nodes; An environmental parameter collection module collects real-time environmental parameters in response to the inspection robot reaching a preset distance before the high-risk inspection node; The environmental mutation index calculation module is used to input the real-time environmental parameters and the environmental mutation frequency corresponding to the high-risk inspection node into the pre-built environmental mutation prediction model to obtain the environmental mutation index when the inspection robot reaches the high-risk inspection node; An optimal parameter determination module is used to traverse the environmental mutation index in a pre-built sensor operation parameter reference library to determine the optimal working parameters of the ultrasonic sensor of the inspection robot when it reaches a high-risk inspection node; The data acquisition control module controls the ultrasonic sensor to collect data based on the optimal working parameters determined by the optimal parameter determination module in response to the inspection robot arriving at the high-risk inspection node.

4. A control method for an industrial inspection robot applied to ultrasonic testing, characterized in that: The method is applied to the industrial inspection robot applied to ultrasonic detection as claimed in claim 3, and the method comprises: Collect multiple sets of environmental mutation event data in industrial areas in historical records; According to the occurrence location of the environmental mutation event data, spatial cluster analysis is performed on the environmental mutation event data to identify areas where the environmental mutation frequency exceeds a preset threshold, thereby forming a dynamic risk area set; Obtaining a preset inspection route of the inspection robot, identifying inspection nodes that spatially overlap with the dynamic risk area set, and marking them as high-risk inspection nodes; In response to the inspection robot reaching a preset distance before the high-risk inspection node, collecting real-time environmental parameters; Inputting the real-time environmental parameter and the environmental mutation frequency corresponding to the high-risk inspection node into a pre-constructed environmental mutation prediction model to obtain the environmental mutation index when the inspection robot reaches the high-risk inspection node; Traversing the environmental mutation index in a pre-built sensor operation parameter comparison library to determine the optimal working parameters of the ultrasonic sensor of the inspection robot when it reaches the high-risk inspection node; When the inspection robot reaches the high-risk inspection node, the ultrasonic sensor is controlled to collect data based on the optimal working parameters.

5. The control method of an industrial inspection robot for ultrasonic testing according to claim 4, characterized in that: The environmental mutation event data at least includes the timestamp of the environmental mutation event, the type of the mutation event, the intensity of the mutation event and the location information of the mutation event.

6. The control method of an industrial inspection robot for ultrasonic testing according to claim 5, characterized in that: According to the occurrence location of the environmental mutation event data, a spatial cluster analysis is performed on the environmental mutation event data, including: Set the neighborhood radius and the minimum number of points in the neighborhood; By adjusting the neighborhood radius and the minimum number of points in the neighborhood, cluster analysis is performed on the location information of environmental mutation event data; Points whose spatial distance and environmental mutation frequency meet the set requirements are divided into the same cluster; The setting of the neighborhood radius and the minimum number of points in the neighborhood is adjusted according to the size of the industrial area and the distribution density of environmental mutation events.

7. The control method of an industrial inspection robot for ultrasonic testing according to claim 6, characterized in that: The real-time environmental parameters include at least temperature, humidity and gas concentration.

8. The control method of an industrial inspection robot for ultrasonic testing according to claim 7, characterized in that: In the sensor operating parameter comparison library, different optimal sensor operating parameters are set according to different environmental mutation indexes; the operating parameters include emission frequency, detection range and sensitivity.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 4 to 8 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the method according to any one of claims 4 to 8.

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