Geotechnical investigation remote monitoring and data analysis method and system based on internet of things

Abstract

This application discloses a remote monitoring and data analysis method and system for geotechnical exploration based on the Internet of Things, relating to the field of geotechnical exploration technology. The method includes acquiring first image data of a first exploration area collected by a first monitoring unit during the geotechnical exploration process; comparing the first image data with pre-stored historical image data of the first exploration area to obtain a first comparison result; and generating a state adjustment command based on the first comparison result when the first comparison result indicates that the first image data and historical image data are different, and sending the state adjustment command to the first monitoring unit. This application adjusts the state of the first monitoring unit by comparing the first image data collected by the first monitoring unit with historical image data, and when the comparison result indicates that the first image data and historical image data are different, thereby restoring the first monitoring unit to its normal state, ensuring proper exploration monitoring of the corresponding area, and reducing risks during exploration activities.

Description

Technical Field

[0001] This application relates to the field of geotechnical investigation, and in particular to a method and system for remote monitoring and data analysis of geotechnical investigation based on the Internet of Things. Background Technology

[0002] Geotechnical investigation refers to the activities of investigating, researching, analyzing, and evaluating the geological, environmental characteristics, and geotechnical engineering conditions of a construction site by using various surveying techniques and methods, according to the requirements of the construction project. Its purpose is to identify, analyze, and evaluate the geological, environmental characteristics, and geotechnical engineering conditions of the construction site, and on this basis, to prepare investigation documents, solve existing geotechnical engineering problems, and ensure the smooth progress of engineering design and construction.

[0003] To enhance safety during geotechnical investigation, intelligent monitoring devices are installed to monitor the investigation activities. However, during the monitoring process, factors such as line faults, strong electromagnetic interference, wind, and ground vibration can cause problems with the intelligent monitoring devices, affecting the monitoring of the investigation area and greatly increasing the risks in the investigation activities. Summary of the Invention

[0004] The purpose of this application is to provide a method, system, device, equipment, medium and product for remote monitoring and data analysis of geotechnical investigation based on the Internet of Things.

[0005] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for remote monitoring and data analysis of geotechnical investigation based on the Internet of Things, including: During the geotechnical investigation, the first monitoring unit collects first image data of the first investigation area, and the first image data is used to describe the investigation activities in the first investigation area. The first image data is compared with the pre-stored historical image data of the first survey area to obtain the first comparison result; If the first comparison result indicates that the first image data is different from the historical image data, a state adjustment instruction is generated based on the first comparison result and sent to the first monitoring unit to control the first monitoring unit to adjust the state.

[0006] Secondly, this application provides a data analysis system, comprising: a monitoring module, the monitoring module including a communication unit, a command unit, and multiple monitoring units, the command unit including a comparison module, a storage module, and a control module, wherein: The storage module is used to acquire first image data of the first exploration area collected by the first monitoring unit among the plurality of monitoring units during the geotechnical exploration process through the communication unit. The first image data is used to describe the exploration activities in the first exploration area. The comparison module is used to compare the first image data with the historical image data of the first survey area pre-stored in the storage module to obtain a first comparison result; The control module is used to generate a state adjustment instruction based on the first comparison result when the first comparison result indicates that the first image data is different from the historical image data, and send the state adjustment instruction to the first monitoring unit through the communication unit to control the first monitoring unit to adjust the state.

[0007] Thirdly, this application provides a data analysis apparatus, comprising: The storage module is used to acquire the first image data of the first exploration area collected by the first monitoring unit during the geotechnical exploration process. The first image data is used to describe the exploration activities in the first exploration area. The comparison module is used to compare the first image data with pre-stored historical image data of the first survey area to obtain a first comparison result; The control module is used to generate a state adjustment instruction based on the first comparison result when the first comparison result indicates that the first image data is different from the historical image data, and send the state adjustment instruction to the first monitoring unit to control the first monitoring unit to adjust the state.

[0008] Fourthly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the IoT-based remote monitoring and data analysis method for geotechnical exploration as described above.

[0009] Fifthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the IoT-based remote monitoring and data analysis method for geotechnical investigation described above.

[0010] Sixthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the IoT-based remote monitoring and data analysis method for geotechnical exploration described above.

[0011] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method, system, device, equipment, medium, and product for remote monitoring and data analysis of geotechnical exploration based on the Internet of Things. By acquiring first image data of a first exploration area collected by a first monitoring unit during the geotechnical exploration process, and using this first image data to describe the exploration activities in the first exploration area, the activity in the geotechnical exploration area is monitored. By comparing the first image data with pre-stored historical image data of the first exploration area, a first comparison result is obtained, enabling timely detection of changes in the status of the monitoring unit. When the first comparison result indicates that the first image data is different from the historical image data, a status adjustment command is generated based on the first comparison result and sent to the first monitoring unit to control the adjustment of the first monitoring unit's status. This achieves status adjustment of the first monitoring unit when the first comparison result indicates that the first image data and historical image data are different, thereby restoring the first monitoring unit to a normal state, ensuring proper exploration monitoring of the corresponding area, and reducing risks during exploration activities. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart illustrating an IoT-based remote monitoring and data analysis method for geotechnical investigation is provided in one embodiment of this application. Figure 2 A flowchart illustrating a remote monitoring and data analysis method for geotechnical investigation based on the Internet of Things, provided as another embodiment of this application; Figure 3 A flowchart illustrating a remote monitoring and data analysis method for geotechnical investigation based on the Internet of Things, provided as another embodiment of this application; Figure 4 A flowchart illustrating a remote monitoring and data analysis method for geotechnical investigation based on the Internet of Things, provided as another embodiment of this application; Figure 5 A flowchart illustrating a remote monitoring and data analysis method for geotechnical investigation based on the Internet of Things is also provided in another embodiment of this application. Figure 6 A flowchart illustrating a remote monitoring and data analysis method for geotechnical investigation based on the Internet of Things is also provided in another embodiment of this application. Figure 7A flowchart illustrating a remote monitoring and data analysis method for geotechnical investigation based on the Internet of Things is also provided in another embodiment of this application. Figure 8 A flowchart illustrating a remote monitoring and data analysis method for geotechnical investigation based on the Internet of Things is also provided in another embodiment of this application. Figure 9 A schematic diagram of the functional modules of a data analysis system provided in an embodiment of this application; Figure 10 A functional module diagram of a data analysis system provided in another embodiment of this application; Figure 11 A schematic diagram of the functional modules of a data analysis system provided in another embodiment of this application; Figure 12 A schematic diagram of the functional modules of a remote monitoring and data analysis system provided in an embodiment of this application; Figure 13 for Figure 12 A detailed functional module diagram of the middle code unit; Figure 14 for Figure 12 A detailed functional module diagram of the processing unit; Figure 15 for Figure 12 A detailed functional module diagram of the analysis unit; Figure 16 A schematic diagram of the functional modules of a data analysis device provided in an embodiment of this application; Figure 17 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0015] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0016] In one exemplary embodiment, such as Figure 1As shown, a remote monitoring and data analysis method for geotechnical investigation based on the Internet of Things (IoT) is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both. The method includes steps 102 to 106. Wherein: Step 102: Acquire the first image data of the first exploration area collected by the first monitoring unit during the geotechnical exploration process. The first image data is used to describe the exploration activities in the first exploration area. In the geotechnical investigation site, multiple monitoring units can be deployed to monitor activities in the geotechnical investigation area. Each monitoring unit is responsible for one investigation area. For example, the first monitoring unit is responsible for monitoring the investigation activities in the first investigation area. The investigation activities may include geological surveys of the construction site, topographic surveys, acquisition of geotechnical samples, measurement of soil density and bearing capacity, extraction of environmental features, and investigation and research on geotechnical engineering conditions. The first image data may include the figures of the investigation personnel, or it may not include the figures of the investigation personnel and only include the environmental background of the investigation area.

[0017] Step 104: Compare the first image data with the pre-stored historical image data of the first survey area to obtain the first comparison result; Specifically, image data of the first exploration area can be collected and stored at preset time intervals. When new image data is acquired, the most recently stored historical image data can be compared with the new image data to obtain a comparison result. The preset time interval can be 5 minutes, 10 minutes, etc. The first image data can be the image data of the first exploration area acquired at the current moment, and the historical image data can be the image data of the first exploration area stored 5 minutes ago.

[0018] Step 106: If the first comparison result indicates that the first image data is different from the historical image data, a state adjustment instruction is generated based on the first comparison result, and the state adjustment instruction is sent to the first monitoring unit to control the first monitoring unit to adjust the state.

[0019] Since the initial state of the monitoring unit is normal, comparing the first image data collected by the monitoring unit with historical image data can promptly detect whether the state of the monitoring unit has changed. If the first image data is different from the historical image data, it can be considered that the state of the monitoring unit has changed, and the state of the monitoring unit needs to be adjusted to restore the monitoring unit to a normal state. The difference between the first image data and the historical image data can be due to differences in the image, viewing angle, field of view, or differences in image brightness and image clarity. The change in the state of the monitoring unit can be a change in its working state or a change in its position. For example, a change in the working state of the monitoring unit can be due to a sudden power outage, line fault, or camera damage resulting in no image display; a blurred image due to lens damage, focus misalignment, or poor quality; or image distortion due to strong nearby electromagnetic interference or damage to internal sensors. A change in the position of the monitoring unit can be due to factors such as wind, ground vibration, physical collision, or improper maintenance, resulting in translation, rotation, tilt, increase or decrease in height, or changes in pitch angle.

[0020] When the status of a monitoring unit changes, it increases the risks during the survey. Therefore, it is necessary to adjust the status of the monitoring unit when a change is detected. The specific type of status change can be determined based on the differences between the first image data and historical image data. For example, if the first image data and historical image data contain different images or fields of view, it can be considered that the position status of the monitoring unit has changed. In this case, a position status adjustment command needs to be generated to control the first monitoring unit to adjust its position. If the image brightness, image clarity, or if the historical image data shows an image while the first image data does not, it can be considered that the working status of the monitoring unit has changed. In this case, a working status adjustment command needs to be generated to control the first monitoring unit to adjust its working status.

[0021] It should be noted that the above method can also be used to determine whether the status of other monitoring units that collect image data from other survey areas has changed. If the status has changed, the status of other monitoring units can be adjusted.

[0022] In this embodiment of the application, by comparing the first image data collected by the first monitoring unit with the historical image data, and when the first comparison result indicates that the first image data and the historical image data are different, the state of the first monitoring unit is adjusted, thereby restoring the first monitoring unit to a normal state, enabling the corresponding area to be properly surveyed and monitored, and reducing the risks in the survey activities.

[0023] In another exemplary embodiment of this application, the state adjustment instruction includes a position adjustment instruction; such as Figure 2 As shown, step 106 above is replaced by step 1061: Step 1061: If the first comparison result indicates that the first monitoring unit has been displaced, generate a position adjustment command based on the first comparison result and send the position adjustment command to the first monitoring unit to control the first monitoring unit to return to its original position.

[0024] In cases where the first image data differs from historical image data, the displacement of the first monitoring unit can be determined based on changes in the image's frame, viewing angle, and field of view. For example, multiple fixed markers can be placed in the field of view of the first monitoring unit. If the relative positions of these markers remain unchanged, the first monitoring unit is considered to be stable; if the position of any marker changes, the first monitoring unit is considered to have been displaced. If any displacement—such as translation, rotation, tilt, elevation change, or pitch change—is determined, the first monitoring unit can be repositioned to its original location. Alternatively, if a change in the frame, viewing angle, or field of view of the first image data is detected compared to historical image data, the preset position or automatic calibration function of the first monitoring unit can be directly invoked to restore it to its original position, without analyzing the specific type of change or the exact location after displacement.

[0025] In this embodiment of the application, by comparing the first image data acquired in real time with the historical image data acquired previously, the position of the first monitoring unit that has shifted can be adjusted when the first image data and the historical image data are different, so that the position of the monitoring unit is corrected, the survey and monitoring of the divided area can be carried out effectively, and the risks in the survey activities can be reduced.

[0026] In another exemplary embodiment of this application, such as Figure 3 As shown, step 104 above is replaced by steps 1041 to 1043: Step 1041: Perform feature extraction and data transformation processing on the first image data to obtain a first feature vector; perform feature extraction and data transformation processing on the historical image data to obtain a second feature vector; Specifically, the first image data and historical image data collected by the first monitoring unit can be optimized separately. After optimization, image features are extracted using feature extraction technology to obtain feature image data. The optimization process can include resolution adjustment, color optimization, compression, noise reduction, etc. The optimization process aims to improve image quality, reduce file size, or enhance visual effects.

[0027] The data transformation process is used to convert feature image data into feature vectors and clean the converted feature vectors. Common cleaning methods include handling missing and outlier values, standardization and normalization, handling noisy data and duplicate values, data type conversion, and handling inconsistent data.

[0028] Step 1042: Determine the similarity between the first image data and the historical image data based on the Hamming distance value between the first feature vector and the second feature vector; The Hamming distance value refers to the number of different elements at corresponding positions of two feature vectors of equal length. In the field of image processing, the Hamming distance value is often used to compare the similarity of binary feature vectors. The first feature vector and the second feature vector can be binarized to obtain the first feature vector and the second feature vector in binary form, and then the Hamming distance value between the two binary feature vectors can be calculated. The similarity between the first image data and the historical image data can be calculated by the following formula (1).

[0029] (1); Where S represents the similarity between the first image data and the historical image data, d represents the Hamming distance between the first feature vector and the second feature vector, and D represents the vector length. The fewer different elements the first feature vector and the second feature vector have, the smaller the Hamming distance value, the higher the similarity, and the more similar the images are. The more different elements the first feature vector and the second feature vector have, the larger the Hamming distance value, the lower the similarity, and the less similar the images are.

[0030] It should be noted that the similarity between the current image data and historical image data collected by any monitoring unit can be calculated using the above formula (1).

[0031] Step 1043: If the similarity is greater than or equal to a preset similarity threshold, the first comparison result is obtained that the first image data is the same as the historical image data; if the similarity is less than the preset similarity threshold, the first comparison result is obtained that the first image data is not the same as the historical image data.

[0032] The preset similarity threshold can be 0.75, 0.8, 0.85, etc. Taking a preset similarity threshold of 0.8 as an example, if the similarity is greater than or equal to 0.8 and less than or equal to 1, it indicates that the first image data is the same as the historical image data. If the similarity is less than 0.8, it indicates that the first image data is not the same as the historical image data.

[0033] In this embodiment, feature extraction and data transformation are performed on the first image data and historical image data respectively to obtain a first feature vector and a second feature vector. Based on the Hamming distance between the first feature vector and the second feature vector, the similarity between the first image data and the historical image data is determined. Then, the similarity is used to determine whether the first image data and the historical image data are the same, so as to more accurately and efficiently determine whether the first image data and the historical image data are the same.

[0034] In another exemplary embodiment of this application, such as Figure 4 As shown, the method may further include steps 108 to 120. Wherein: Step 108: Acquire second image data of the second exploration area collected by the second monitoring unit during the geotechnical exploration process. The second image data is used to describe the exploration behavior of the exploration personnel in the second exploration area. The second monitoring unit and the first monitoring unit can be the same monitoring unit or different monitoring units; the first survey area and the second survey area can be the same survey area or different survey areas; the first image data and the second image data can be the same image data or different image data; the survey activities can include topographic surveying, blasting, sample collection, hydrological surveying, etc.

[0035] Step 110: Extract features from the second image data to obtain the first row of data; Specifically, feature extraction technology can be used to extract features from the second image data to obtain first behavioral data. The first behavioral data can be data corresponding to the surveyor's survey behavior, such as the surveyor's pose data, protective facilities, and surrounding environment data. The pose data includes position and posture data. If the survey behavior is a blasting behavior, the first behavioral data can include the surveyor's pose data, protective facilities, and surrounding environment data before the blasting occurs. If the survey behavior is a topographic surveying behavior, the first behavioral data can include the surveyor's pose data and surrounding environment data when marking topographic features such as rivers, mountains, and buildings, as well as the pose data and surrounding environment data when measuring information such as coordinates and elevation.

[0036] Step 112: Compare the first behavioral data with the pre-stored historical behavioral data of the second survey area to obtain a second comparison result; The historical behavioral data can be normal behavioral data from previous exploration processes, such as the safe position and posture of the exploration personnel during historical blasting, as well as the protective measures that need to be worn; it can also be the safe distance between the exploration personnel and the cliffs in the mountains during topographic surveys; or it can be the safe water level and suitable weather conditions during hydrological surveys, etc.

[0037] Step 114: If the second comparison result indicates that the first behavioral data is not similar to the historical behavioral data, then the first behavioral data is determined to be abnormal behavioral data; Specifically, during blasting, if the distance between the surveyor and the blasting point is too close, the posture is not standardized, or safety equipment is not worn in the first set of data, then the first set of data is considered abnormal. During hydrological surveys, if the water level is too high in the first set of data, or if there is heavy rain or snow, then the first set of data is considered abnormal. During topographic surveys, if the distance between the surveyor and the cliff is too close, the surveyor is near poisonous plants or thorns, or the machinery malfunctions during operation, then the first set of data is considered abnormal.

[0038] Step 116: Determine the risk probability corresponding to the abnormal behavior data; Step 118: Based on the risk probability, predict the risk level; Risk levels can be categorized as no risk, low risk, and high risk. A risk level is considered high when the probability of risk is greater than or equal to 1, low when the probability of risk is greater than 0 and less than 1, and no when the probability of risk is equal to 0.

[0039] Step 120: Based on the risk level, perform risk control.

[0040] Among these methods, the risk probability can be determined based on the exploration activities of the exploration personnel, the risk level of the exploration activities within the exploration area can be predicted based on the risk probability, and risk control can be carried out based on the risk level to reduce the risks in the exploration process.

[0041] In this embodiment, abnormal behavior data can be extracted based on the surveying behavior of surveyors, the risk probability corresponding to the abnormal behavior data can be determined, and risk analysis, risk prediction and risk control can be carried out based on the risk probability. This allows for timely prediction and control of risks, reducing the risks faced by surveyors during the surveying process.

[0042] In another exemplary embodiment of this application, such as Figure 5 As shown, step 110 above is replaced by steps 1101 to 1103: Step 1101: Optimize the second image data to obtain the third image data; In this process, the second image data can be optimized first to ensure its high fidelity.

[0043] Step 1102: Mark the historical behavior data in the historical survey activities; This includes obtaining the correct surveying behavior of past surveyors, extracting historical behavior data from the data, and marking the historical behavior data.

[0044] Step 1103: Based on the marker, extract the first row data from the third image data.

[0045] Specifically, the first row of data can be extracted from the third image data based on the marked behavioral data, and the first row of data can be labeled.

[0046] In this embodiment, by extracting the first behavior data from the third image data based on the historical behavior data extracted from historical exploration activities, it is possible to quickly identify the key steps in the current exploration activities, reduce repetitive work, and improve the efficiency of behavior data extraction.

[0047] In another exemplary embodiment of this application, such as Figure 6 As shown, between steps 108 and 110, the method may further include steps 1091 to 1096. Wherein: Step 1091: Obtain the distance between the central data points in the first dataset corresponding to the second image data; Step 1092: Identify points whose distance is greater than a preset distance threshold as outliers, wherein the preset distance threshold is determined based on the average value and standard deviation of the data points; The distance threshold can be set by adding or subtracting 2 or 3 times the standard deviation from the average value. Points that are greater than the preset distance threshold will be regarded as outliers, i.e., abnormal points.

[0048] Step 1093: Remove outliers from the first dataset to obtain the second dataset; Step 1094: Compare each data point in the second dataset with the pre-stored normal operation data to obtain the third comparison result; This can be achieved by using data traversal techniques to search for each data point in the second dataset and comparing it with the normal operating data.

[0049] Step 1095: Based on the third alignment result, determine the missing points in the second dataset, and fill in the missing values ​​corresponding to the missing points to obtain the third dataset; Based on the comparison results, missing points in the second dataset can be identified. Activity data from the surveyed area can be retrieved again to supplement the missing values ​​corresponding to the missing points. Alternatively, interpolation can be used to supplement the missing values ​​of the missing points.

[0050] Step 1096: Determine the third dataset as the dataset corresponding to the second image data.

[0051] In this embodiment of the application, by deleting outliers and supplementing missing values, data integrity can be enhanced, data utilization can be improved, and data quality can be improved.

[0052] In another exemplary embodiment of this application, such as Figure 7 As shown, step 116 above is replaced by steps 1161 to 1164: Step 1161: Determine the actual severity level of the accident caused by the abnormal behavior corresponding to the abnormal behavior data; The actual level of harm caused by an accident involving abnormal behavior data is usually classified according to its scope of influence, severity, and potential consequences. For example, the actual level of harm caused by an accident involving abnormal behavior can be determined based on factors such as the estimated recovery time, casualties, economic losses, environmental impact, social impact, and legal liability.

[0053] Step 1162: Obtain the maximum permissible hazard level of the accident within the second survey area; Step 1163: Obtain the actual number and maximum allowable number of hazard sources and / or risk points within the second survey area; The hazards and risk points include natural environmental risks, equipment and tool risks, human factor risks, fire and explosion risks, radiation hazards, high-altitude operation hazards, and underground operation hazards. For example, the natural environmental risks may include terrain risks such as steep slopes, cliffs, and swamps that are prone to slipping, falling, or getting stuck, as well as risks that may threaten personnel safety such as extreme weather, geological disasters, and threats from animals and plants. The equipment and tool risks may include survey equipment failures, improper use of tools, and electrical equipment risks.

[0054] Step 1164: Determine the risk probability based on the actual quantity, the maximum quantity, the actual hazard level, and the maximum hazard level.

[0055] Wherein, the risk probability can be represented as P, the actual quantity can be represented as N, the maximum quantity can be represented as M, the actual degree of harm can be represented as n, and the maximum degree of harm can be represented as m. The risk probability can be calculated by the following formula (2).

[0056] (2); In this embodiment, the risk probability is determined by considering the actual number and maximum allowable number of hazardous sources or risk points in the survey area, the actual severity level of an accident caused by abnormal behavior data, and the maximum allowable severity level in the survey area. This comprehensive approach, taking into account multiple dimensions such as the number of hazardous sources or risk points and the severity level of abnormal behavior, more fully reflects potential risks and avoids the bias of a single factor. Combining actual and allowable maximum values ​​allows for a more accurate assessment of risk levels, helping to identify high-risk areas or behaviors and improving the accuracy of risk assessment. It also allows for dynamic adjustments based on actual conditions, promptly reflecting changes in the number or severity of hazardous sources and ensuring the timeliness of risk assessment. Furthermore, it possesses strong risk prevention capabilities and operability.

[0057] In another exemplary embodiment of this application, such as Figure 8 As shown, step 120 above is replaced by steps 1201 to 1202: Step 1201: Determine the alert method based on the risk level; Step 1202: Based on the aforementioned reminder method, issue a risk reminder.

[0058] The corresponding reminder methods and content may vary depending on the risk level. For example, in a high-risk situation, one or more reminder methods such as sound, flashing lights, telephone, on-site notification, and pop-up notifications may be used to ensure users take immediate action, suitable for emergencies. In a low-risk situation, any one of the reminder methods such as SMS, email, and WeChat push may be used to ensure users receive information promptly with minimal disruption, suitable for daily reminders. In a no-risk situation, no reminder may be required. The reminder content may include guiding steps or warnings to guide surveyors to perform correct operations or stop current operations. For example, reminding surveyors to stay away from blasting points and cliffs, reminding them of rising water levels, reminding them to pay attention to weather changes and the presence of poisonous plants, or reminding them of mechanical malfunctions requiring them to stop operations.

[0059] For example, text message push technology can be used to send information to surveyors via SMS, email, or WeChat, reminding them of potential hazards and providing feedback on the analysis results.

[0060] In this embodiment, different reminders are given according to different risk levels, so that the risk reminders can meet the user's needs in a targeted manner while ensuring user safety; the guiding steps or warning content in the reminder can guide the user to operate and help the user complete the survey task more efficiently and safely.

[0061] Based on the same inventive concept, this application also provides a data analysis system for implementing the IoT-based remote monitoring and data analysis method for geotechnical investigation described above. The solution provided by this system is similar to the implementation scheme described in the above method; therefore, the specific limitations of one or more data analysis system embodiments provided below can be found in the limitations of the IoT-based remote monitoring and data analysis method for geotechnical investigation described above, and will not be repeated here.

[0062] In one exemplary embodiment, such as Figure 9 As shown, a data analysis system 200 is provided, including: Monitoring module 21, comprising a communication unit 211, a command unit 212, and multiple monitoring units 213, wherein the command unit 212 comprises a storage module 2121, a comparison module 2122, and a control module 2123, wherein: The storage module 2121 is used to acquire first image data of the first exploration area collected by the first monitoring unit 2131 among the plurality of monitoring units 213 during the geotechnical exploration process through the communication unit 211. The first image data is used to describe the exploration activities in the first exploration area. Among them, the monitoring module 21 can also be called the remote monitoring module. The monitoring unit 213 is used to monitor the activities in the geotechnical exploration area. The monitoring unit 213 may include a first monitoring unit 2131, other monitoring units 2132 and other monitoring units 2133. The monitoring unit 213 may be a high-definition camera. The command unit 212 is used to detect and control the operation of the monitoring unit 213. The communication unit 211 is used to connect the monitoring unit 213 and the command unit 212. The communication unit 211 may use satellite communication or wireless communication for data transmission.

[0063] The comparison module 2122 is used to compare the first image data with the historical image data of the first survey area pre-stored in the storage module 2121 to obtain a first comparison result; The comparison module 2122 can be used to compare the first image data collected by the monitoring unit 2131 with the historical image data collected by the monitoring unit 2131 five minutes ago; the storage module 2121 is used to store the location data of the monitoring unit 2131 that was previously monitored normally.

[0064] The control module 2123 is used to generate a state adjustment instruction based on the first comparison result when the first comparison result indicates that the first image data is different from the historical image data, and send the state adjustment instruction to the first monitoring unit through the communication unit to control the first monitoring unit to adjust the state.

[0065] The control module 2123 can remotely control the operation of the first monitoring unit 2131 based on the calculation results of the comparison module 2122, and control the first monitoring unit 2131 to align the monitoring position.

[0066] As an optional implementation, the state adjustment instruction includes a position adjustment instruction. The control module 213 is used to generate a position adjustment instruction based on the first comparison result when the first comparison result indicates that the first monitoring unit 2131 has shifted, and send the position adjustment instruction to the first monitoring unit 2131 to control the first monitoring unit 2131 to return to its original position.

[0067] As an optional implementation, the comparison module 2122 is configured to: perform feature extraction and data transformation processing on the first image data to obtain a first feature vector; perform feature extraction and data transformation processing on the historical image data to obtain a second feature vector; determine the similarity between the first image data and the historical image data based on the Hamming distance value between the first feature vector and the second feature vector; if the similarity is greater than or equal to a preset similarity threshold, obtain a first comparison result that the first image data and the historical image data are the same; if the similarity is less than the preset similarity threshold, obtain a first comparison result that the first image data and the historical image data are not the same.

[0068] As an optional implementation method, such as Figure 10 As shown, the data analysis system 200 further includes: a data analysis module 22, which includes a processing unit 221, an analysis unit 222, and a feedback unit 223. The processing unit 221 includes an access module 2211, and the analysis unit 222 includes a behavior analysis module 2221 and a risk prediction module 2222, wherein: The access module 2211 is used to acquire second image data of the second exploration area collected by the second monitoring unit 2132 among the multiple monitoring units 213 during the geotechnical exploration process through the communication unit 211. The second image data is used to describe the exploration behavior of the exploration personnel in the second exploration area. The behavior analysis module 2221 is used to extract features from the second image data to obtain the first behavior data; The risk prediction module 2222 is used to compare the first behavioral data with the historical behavioral data of the second survey area pre-stored in the storage module 2121 to obtain a second comparison result; if the second comparison result indicates that the first behavioral data is not similar to the historical behavioral data, the first behavioral data is determined to be abnormal behavioral data; the risk probability corresponding to the abnormal behavioral data is determined; and the risk level is predicted based on the risk probability. The risk prediction module 2222 can analyze behavioral data during the exploration process using uncertainty analysis.

[0069] The feedback unit 223 is used to perform risk control based on the risk level.

[0070] The monitoring module 21 is connected to the data analysis module 22. The data analysis module 22 can perform risk analysis and prediction on the behavior and activities of the survey personnel, and will feed back the analysis results to the survey personnel to reduce the risks to the survey personnel during the survey process.

[0071] As an optional implementation, the behavior analysis module 2221 is used to: optimize the second image data to obtain third image data; mark the historical behavior data in the historical survey behavior; and extract the first behavior data in the third image based on the marking.

[0072] As an optional implementation method, such as Figure 11 As shown, the processing unit further includes a missing module 2212, used for: Obtain the distance between data points in the first dataset corresponding to the second image data; Points whose distance is greater than a preset distance threshold are identified as outliers. The preset distance threshold is determined based on the average value and standard deviation of the data points. The outliers in the first dataset are removed to obtain the second dataset; Each data point in the second dataset is compared with the pre-stored normal operation data to obtain the third comparison result; This involves using data traversal technology to search for each data point in the dataset to be calculated and comparing the data with the normal operating data of the remote monitoring module.

[0073] Based on the third comparison result, the missing points in the second dataset are determined, and the missing values ​​corresponding to the missing points are filled in to obtain the third dataset; The third dataset is determined to be the dataset corresponding to the second image data.

[0074] The missing value module 2212 can perform calculations on the data accessed by the access module 2211 using outlier and missing value identification technology.

[0075] As an optional implementation, the risk prediction module 2222 is used for: Determine the actual severity level of the accident caused by the abnormal behavior corresponding to the abnormal behavior data; Obtain the maximum permissible hazard level for an accident within the second survey area; Obtain the actual number of hazards and / or risk points within the second survey area, as well as the maximum permissible number. The risk probability is determined based on the actual quantity, the maximum quantity, the actual hazard level, and the maximum hazard level.

[0076] As an optional implementation, the feedback unit 223 is used to: determine the reminder method based on the risk level; and provide a risk reminder based on the reminder method.

[0077] The feedback unit 223 can be established using text message push technology, and the feedback unit can send information to the survey personnel via SMS, email, or WeChat push.

[0078] In one exemplary embodiment, such as Figure 12 As shown, a remote monitoring and data analysis system 300 is provided, including a remote monitoring module 31 and a data analysis module 32. The remote monitoring module 31 includes a communication unit 311, a command unit 312 and a monitoring unit 313. The data analysis module includes a processing unit 321, an analysis unit 322 and a feedback unit 323.

[0079] In one exemplary embodiment, such as Figure 13 As shown, the instruction unit 312 includes a storage module 3121, a comparison module 3122, and a control module 3123.

[0080] In one exemplary embodiment, such as Figure 14 As shown, the processing unit 321 includes an access module 3211 and a missing module 3212.

[0081] In one exemplary embodiment, such as Figure 15 As shown, the analysis unit 322 includes a behavior analysis module 3221 and a risk prediction module 3222.

[0082] When the application system 300 monitors the surveyed area, multiple monitoring units 313 of the remote monitoring module 31 can be deployed in the surveyed area. During subsequent monitoring, the monitoring units 313 will monitor the surveyed area. The data collected by the monitoring units 313 will be transmitted through the communication unit 311 to the storage module 3121 in the command unit 312. Simultaneously, the comparison module 3122 will compare the data collected by the monitoring units 313 with the image data collected by the monitoring units 313 five minutes prior. Based on the comparison result, the control module 3123 will remotely send a command to the monitoring units 313, causing the monitoring units 313 to adjust, return to their original position, and align their position. The communication unit 311 will also... The data collected by the monitoring unit 313 is transmitted to the data analysis module 32. The access module 3211 in the subsequent processing unit 321 receives the data transmitted by the communication unit 311. Then, the missing data module 3212 analyzes whether there are any outliers or missing values ​​in the data and processes them. The data processed by the processing unit 321 is then transmitted to the analysis unit 322. The behavior analysis module 3221 in the analysis unit 322 extracts behavioral data from the data based on normal behaviors during previous surveys. Then, the risk prediction module 3222 analyzes the behavioral data during the survey process using uncertainty analysis. The feedback unit 323 sends information to the survey personnel through text message push technology to reduce the risks to the survey personnel during the survey process.

[0083] Based on the same inventive concept, this application also provides a data analysis device for implementing the IoT-based remote monitoring and data analysis method for geotechnical investigation described above. The solution provided by this device is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more data analysis device embodiments provided below can be found in the limitations of the IoT-based remote monitoring and data analysis method for geotechnical investigation described above, and will not be repeated here.

[0084] In one exemplary embodiment, such as Figure 16 As shown, a data analysis device 400 is provided, comprising: Storage module 401 is used to acquire first image data of the first exploration area collected by the first monitoring unit during the geotechnical exploration process. The first image data is used to describe the exploration activities in the first exploration area. The comparison module 402 is used to compare the first image data with the pre-stored historical image data of the first survey area to obtain a first comparison result; The control module 403 is used to generate a state adjustment instruction based on the first comparison result when the first comparison result indicates that the first image data is different from the historical image data, and send the state adjustment instruction to the first monitoring unit to control the first monitoring unit to adjust the state.

[0085] The function of the storage module 401 in the device 400 can be realized by the storage module 3121 in the remote monitoring and data analysis system 300, the function of the comparison module 402 can be realized by the comparison module 3122 in the remote monitoring and data analysis system 300, and the function of the control module 403 can be realized by the control module 3123 in the remote monitoring and data analysis system 300.

[0086] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 17 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores video tag processing data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When the computer program is executed by the processor, it implements a remote monitoring and data analysis method for geotechnical investigation based on the Internet of Things (IoT).

[0087] Those skilled in the art will understand that Figure 17 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0088] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0089] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0090] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0091] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0092] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0093] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0094] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0095] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A remote monitoring and data analysis method for geotechnical investigation based on the Internet of Things, characterized in that, The IoT-based remote monitoring and data analysis method for geotechnical investigation includes: During the geotechnical investigation, the first monitoring unit collects first image data of the first investigation area, and the first image data is used to describe the investigation activities in the first investigation area. The first image data is compared with the pre-stored historical image data of the first survey area to obtain the first comparison result; If the first comparison result indicates that the first image data is different from the historical image data, a state adjustment instruction is generated based on the first comparison result and sent to the first monitoring unit to control the first monitoring unit to adjust the state.

2. The method for remote monitoring and data analysis of geotechnical investigation based on the Internet of Things as described in claim 1, characterized in that, The status adjustment command includes a position adjustment command; When the first comparison result indicates that the first image data is different from the historical image data, generating a state adjustment command based on the first comparison result and sending the state adjustment command to the first monitoring unit to control the first monitoring unit to adjust its state includes: If the first comparison result indicates that the first monitoring unit has been displaced, a position adjustment command is generated based on the first comparison result and sent to the first monitoring unit to control the first monitoring unit to return to its original position.

3. The method for remote monitoring and data analysis of geotechnical investigation based on the Internet of Things as described in claim 1, characterized in that, The step of comparing the first image data with pre-stored historical image data of the first survey area to obtain a first comparison result includes: The first image data is subjected to feature extraction and data transformation processing to obtain a first feature vector; the historical image data is subjected to feature extraction and data transformation processing to obtain a second feature vector. Based on the Hamming distance between the first feature vector and the second feature vector, the similarity between the first image data and the historical image data is determined. If the similarity is greater than or equal to a preset similarity threshold, the first comparison result is that the first image data is the same as the historical image data; if the similarity is less than the preset similarity threshold, the first comparison result is that the first image data is not the same as the historical image data.

4. The method for remote monitoring and data analysis of geotechnical investigation based on the Internet of Things according to any one of claims 1 to 3, characterized in that, The IoT-based remote monitoring and data analysis method for geotechnical investigation also includes: During the geotechnical investigation, the second monitoring unit collects second image data of the second investigation area, which is used to describe the investigation behavior of the investigators in the second investigation area. Feature extraction is performed on the second image data to obtain the first row of data; The first behavioral data is compared with the pre-stored historical behavioral data of the second survey area to obtain a second comparison result; If the second comparison result indicates that the first behavioral data is not similar to the historical behavioral data, the first behavioral data is determined to be abnormal behavioral data. Determine the risk probability corresponding to the abnormal behavior data; Based on the aforementioned risk probability, the risk level is predicted; Risk control measures will be implemented based on the aforementioned risk level.

5. The method for remote monitoring and data analysis of geotechnical investigation based on the Internet of Things as described in claim 4, characterized in that, The step of extracting features from the second image data to obtain the first row of data includes: The second image data is optimized to obtain the third image data; Mark historical data from historical survey activities; Based on the markers, the first row of data is extracted from the third image data.

6. The method for remote monitoring and data analysis of geotechnical investigation based on the Internet of Things according to claim 4, characterized in that, The IoT-based remote monitoring and data analysis method for geotechnical investigation also includes: Obtain the distance between data points in the first dataset corresponding to the second image data; Points whose distance is greater than a preset distance threshold are identified as outliers. The preset distance threshold is determined based on the average value and standard deviation of the data points. The outliers in the first dataset are removed to obtain the second dataset; Each data point in the second dataset is compared with the pre-stored normal operation data to obtain the third comparison result; Based on the third comparison result, the missing points in the second dataset are determined, and the missing values ​​corresponding to the missing points are filled in to obtain the third dataset; The third dataset is determined to be the dataset corresponding to the second image data.

7. The method for remote monitoring and data analysis of geotechnical investigation based on the Internet of Things according to claim 4, characterized in that, Determining the risk probability corresponding to the abnormal behavior data includes: Determine the actual severity level of the accident caused by the abnormal behavior corresponding to the abnormal behavior data; Obtain the maximum permissible hazard level for an accident within the second survey area; Obtain the actual number of hazards and / or risk points within the second survey area, as well as the maximum permissible number. The risk probability is determined based on the actual quantity, the maximum quantity, the actual hazard level, and the maximum hazard level.

8. The method for remote monitoring and data analysis of geotechnical investigation based on the Internet of Things according to claim 4, characterized in that, The risk control based on the risk level includes: Based on the aforementioned risk level, determine the notification method; Risk warnings are issued based on the aforementioned reminder method.

9. A remote monitoring and data analysis system for geotechnical investigation based on the Internet of Things, characterized in that, The data analysis system includes: a monitoring module, which comprises a communication unit, a command unit, and multiple monitoring units. The command unit includes a comparison module, a storage module, and a control module, wherein: The storage module is used to acquire first image data of the first exploration area collected by the first monitoring unit among the plurality of monitoring units during the geotechnical exploration process through the communication unit. The first image data is used to describe the exploration activities in the first exploration area. The comparison module is used to compare the first image data with the historical image data of the first survey area pre-stored in the storage module to obtain a first comparison result; The control module is used to generate a state adjustment instruction based on the first comparison result when the first comparison result indicates that the first image data is different from the historical image data, and send the state adjustment instruction to the first monitoring unit through the communication unit to control the first monitoring unit to adjust the state.

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