Precise Geophysical Detection System for Accessories of Directly Buried Heating Pipelines Based on Multi-Sensor Fusion

Abstract

The present invention belongs to the technical field of identification of heating pipeline accessories, and discloses an accurate geophysical detection system for directly buried heating pipeline accessories based on multi-sensor fusion, including a mobile detection device setting module, a heating pipeline route identification module, a heating pipeline data acquisition module, a heating pipeline data analysis module, an influencing factor data analysis module, a data anomaly identification module, a heating pipeline accessory identification module, and a data repository. When identifying data anomalies, the present invention comprehensively considers the possible impacts of soil factor data and inter-sensor influencing factor data, and then comprehensively judges whether there are pipeline accessories, which can improve the accuracy and reliability of system detection. When analyzing the abnormal situation of the monitoring data of the target heating pipeline in the current monitoring section, the present invention comprehensively analyzes the abnormal situations of data based on electromagnetic sensors, ultrasonic sensors, and geomagnetic sensors, which can improve the detection accuracy and reliability.

Description

Technical Field

[0001] The present invention belongs to the technical field of heat supply pipeline accessory type identification, and relates to an accurate geophysical detection system for buried heat supply pipeline accessories based on multi-sensor fusion. Background Art

[0002] The identification of heat supply pipeline accessory types refers to the process of distinguishing and classifying various accessories in a heat supply pipeline system through various technical means and methods. These accessories include, but are not limited to, valves, compensators, flanges, tees, etc. When a fault occurs in the heat supply system, accurately identifying the type of pipeline accessories can help maintenance personnel quickly locate the problem. Different types of pipeline accessories require specific components and methods for maintenance. Existing heat supply pipelines are buried underground, and it is often difficult to directly identify the types of heat supply pipeline accessories. Therefore, the analysis of accurate geophysical detection of buried heat supply pipeline accessory types based on multi-sensor fusion is of great significance.

[0003] In the prior art, there are also related solutions for energy-saving control of HVAC equipment. For example, a Chinese patent application for an auxiliary identification system and method for sheet metal parts based on multi-sensor data fusion with the publication number CN115684152A includes: a human infrared sensor and an illuminance sensor are fixed at the lower end of a workbench, an industrial camera and an auxiliary light source are fixed at the upper end of the workbench, a temperature sensor and a vibration sensor are fixed on the auxiliary light source, the temperature sensor is used to monitor the temperature of the industrial camera and the auxiliary light source, the vibration sensor is used to monitor whether the workbench is in a stable working environment, the human infrared sensor is used to judge the state of the currently manually placed sheet metal parts, the illuminance sensor is used to feedback the on-site illuminance of the actual sheet metal part identification environment, and the industrial camera is used to collect images of the sheet metal parts.

[0004] In addition, a Chinese patent application for an indoor positioning method and system based on multi-sensor fusion with the publication number CN107478214A includes: using a map creation module to establish an indoor two-dimensional environment map; starting the positioning system to obtain more accurate relative positioning data; starting the positioning system to obtain global positioning data. The purpose of this invention is to quickly build indoor two-dimensional environment information, generate an indoor two-dimensional grid map by means of indoor two-dimensional modeling, indoor positioning, robot control, and visual positioning technologies, and on this basis, use an encoder, a gyroscope, and a visual sensor to obtain the relative offset position information and direction information of the vehicle body, and use a lidar sensor to correct the cumulative error to obtain more accurate global positioning information.

[0005] Although the above two solutions propose some solutions for multi-sensor data fusion analysis, there are still certain limitations: on the one hand, the existing solutions usually only involve the identification of target objects, and lack targeted correction of the influencing factors that may affect the result analysis during the identification process of target objects. This analysis method will reduce the accuracy and efficiency of the target object identification result, leading to the failure of object identification, and then affecting the subsequent maintenance and repair; on the other hand, the existing multi-sensor data fusion analysis process is usually a differential analysis for standard data, lacking dynamic reference analysis for target data. This analysis method may reduce the adaptability of the data analysis process and affect the accuracy of the data analysis result. Summary of the Invention

[0006] In view of this, to solve the problems proposed in the above background technology, a precise geophysical detection system for buried heat supply pipeline accessories based on multi-sensor fusion is proposed.

[0007] The object of the present invention can be achieved by the following technical solutions: A precise geophysical detection system for buried heat supply pipeline accessories based on multi-sensor fusion, including: a mobile detection device setting module for setting mobile monitoring devices, and the mobile detection device includes a mobile device, an infrared thermal imager, and a combined sensor.

[0008] A heat supply pipeline route identification module for using the infrared thermal imager to perform mobile temperature detection on the target heat supply pipeline, analyzing the target heat supply pipeline route based on the temperature detection result, and correcting the travel route of the mobile detection device in real time.

[0009] A heat supply pipeline data acquisition module for using the combined sensor to collect data on the target heat supply pipeline, and obtaining the monitoring sensor data of the target heat supply pipeline, specifically including magnetic field strength, magnetic field direction declination, reflection wave duration, reflection wave intensity, geomagnetic intensity, and magnetic declination.

[0010] A heat supply pipeline data analysis module for analyzing the abnormal situation of the monitoring data of the target heat supply pipeline in the current monitoring section based on the monitoring sensor data of the target heat supply pipeline.

[0011] An influencing factor data analysis module for collecting the influencing factor data of the location where the mobile detection device is located, including soil factor data and inter-sensor influencing factor data, where the soil factor data includes soil conductivity, soil permeability, and soil temperature, and the inter-sensor influencing factor data includes installation spacing and monitoring magnetic field strength, and analyzing the evaluation situation of the influencing factor data.

[0012] A data anomaly situation identification module for judging whether there is an abnormality in the pipeline accessories based on the abnormal situation of the monitoring data of the target heat supply pipeline in the current monitoring section and the evaluation situation of the influencing factor data.

[0013] Recognition of heating pipeline accessories, which is used to obtain the cross-sectional shape of the heating pipeline by using an ultrasonic sensor when it is determined that there is an abnormality in the pipeline accessories, and then identify the specific type of pipeline accessories.

[0014] Data repository, which is used to store the cross-sectional data corresponding to each type of pipeline accessory of the target heating pipeline.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) When the present invention identifies data anomalies, it comprehensively considers the possible impacts of soil factor data and sensor-interference factor data, and then comprehensively determines whether there are pipeline accessory anomalies. This analysis method can improve the accuracy and reliability of system detection, can exclude misjudgments caused by soil characteristics and sensor interference, more accurately judge the true situation of the pipeline accessory type, and can adapt to complex and changeable environments, thereby providing accurate maintenance location information for pipeline maintenance personnel and reducing the blindness of maintenance.

[0016] (2) When the present invention analyzes the anomaly situation of the monitoring data of the target heating pipeline in the current monitoring section, it comprehensively analyzes based on the anomaly situations of the electromagnetic sensor, ultrasonic sensor, and geomagnetic sensor. On the one hand, it can improve the detection accuracy and reliability. Each sensor is sensitive to the electromagnetic characteristics, structural information, and geomagnetic interference situation of the pipeline, and the data can complement each other, reducing misjudgments and missed judgments caused by environmental and performance factors. On the other hand, it is beneficial to comprehensively evaluate the pipeline state and can also dynamically monitor the entire process of pipeline changes. In addition, this method has strong anti-interference ability, can adapt to complex underground environments, and can flexibly respond to different working conditions, meeting the diverse monitoring needs of heating pipelines. Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0018] Figure 1 Schematic diagram of the connection of each module of the system of the present invention.

[0019] Figure 2 Schematic diagram of an embodiment of the planned travel direction analysis provided by the present invention.

[0020] Figure 3 Flowchart corresponding to an embodiment of the determination of the existence of pipeline accessory anomalies provided by the present invention.

[0021] Reference numerals: 1 - previous monitoring section, 2 - current monitoring section, 3 - monitoring direction point, 4 - planned travel direction. Detailed implementation manners

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0023] Please refer to Figure 1 As shown, the present invention provides an accurate geophysical detection system for buried heating pipeline accessories based on multi-sensor fusion, including a mobile detection device setting module, a heating pipeline route identification module, a heating pipeline data acquisition module, a heating pipeline data analysis module, an influencing factor data analysis module, a data anomaly identification module, a heating pipeline accessory identification module, and a data storage library. Among them, the mobile detection device setting module is connected to the heating pipeline route identification module, the heating pipeline route identification module is connected to the heating pipeline data acquisition module, the heating pipeline data acquisition module is connected to the heating pipeline data analysis module, the heating pipeline data analysis module is connected to the influencing factor data analysis module, the influencing factor data analysis module is connected to the data anomaly identification module, the data anomaly identification module is connected to the heating pipeline accessory identification module, and the heating pipeline accessory identification module is connected to the data storage library.

[0024] The mobile detection device setting module is used to set the mobile monitoring device, and the mobile detection device includes a mobile device, an infrared thermal imager, and a combined sensor.

[0025] It should be noted that the combined sensor includes an electromagnetic sensor, an ultrasonic sensor, a geomagnetic sensor, a Hall effect sensor, a magnetoresistive sensor, a soil conductivity sensor, a soil permeability sensor, and a temperature sensor.

[0026] The heating pipeline route identification module is used to perform mobile temperature detection on the target heating pipeline by using the infrared thermal imager, analyze the direction of the target heating pipeline based on the temperature detection result, and correct the travel route of the mobile detection device in real time.

[0027] In a preferred embodiment of the present invention, the specific process of analyzing the direction of the target heating pipeline is as follows: the travel route of the mobile detection device is divided into several monitoring segments based on an equal interval distance, and the mobile distance of the mobile detection device each time after confirming the travel direction is the preset interval distance.

[0028] It should be noted that the setting of the equal interval distance in the present invention is for facilitating a series of operations such as subsequent data collection, analysis, and route regulation, thereby ensuring the efficiency of the target heat supply pipeline route recognition and anomaly recognition processes. While ensuring the accuracy of route recognition and the precision of anomaly situations, it reduces the data processing volume and improves the data processing efficiency.

[0029] Exemplarily, the equal interval distance can be .

[0030] It should be noted that the basis for setting the equal interval distance is as follows: 1. The setting of the equal interval distance needs to refer to the maximum acquisition distance of each sensor and the maximum scanning distance of the infrared thermal imager, so that all parts of the target heat supply pipeline can be ensured to be collected during data processing, so as to avoid omission and resulting in missed recognition of some pipeline accessory types; 2. The setting of the equal interval distance also needs to consider the recognition accuracy of the target heat supply pipeline. While the mobile detection device is moving, avoid excessive movement in a single word, resulting in too large a data deviation between adjacent monitoring segments, thereby increasing the complexity of subsequent correction.

[0031] Please refer to Figure 2 As shown, use the infrared thermal imager to obtain the thermal image corresponding to the target heat supply pipeline in real time, locate each color area in the corresponding thermal image, and then obtain the chromaticity value corresponding to each color area. Compare the chromaticity value corresponding to each color area with the pre-saved relationship between chromaticity value and temperature to obtain the temperature value corresponding to each color area. Then select the color area corresponding to the maximum temperature value as the monitoring color area, and record the center point of the monitoring color area as the monitoring direction point corresponding to the current monitoring segment.

[0032] Record the starting point of the target heat supply pipeline as the initial monitoring point, and record the direction from the initial monitoring point to the direction corresponding to the monitoring direction point of the next monitoring segment as the planned traveling direction corresponding to this monitoring segment. Then use the mobile detection device to move along the planned traveling direction to perform the analysis of the planned traveling direction of the subsequent monitoring segments.

[0033] It should be noted that only when in the first monitoring segment, the planned traveling direction is the direction from the starting point to the direction corresponding to the monitoring direction point of the current monitoring segment. When in other monitoring segments, the planned traveling direction is the direction from the monitoring direction point of the previous monitoring segment to the monitoring direction point of the current monitoring segment.

[0034] Obtain the planned traveling direction corresponding to each monitoring segment, and then connect the head and tail to obtain the target heat supply pipeline route.

[0035] In a preferred embodiment of the present invention, the specific method for correcting the travel route of the mobile detection device is as follows: Obtain the travel direction of the previous monitoring section corresponding to the current monitoring section and the travel direction of the current monitoring section, and record the included angle between the two as the monitoring included angle corresponding to the current monitoring section.

[0036] Calculate the monitoring included angle anomaly index corresponding to the current monitoring section by taking the ratio of the monitoring included angle corresponding to the current monitoring section to the pre-set reference included angle threshold, and then compare it with the pre-set monitoring included angle anomaly index threshold. If the monitoring included angle anomaly index corresponding to a certain monitoring section is greater than the pre-set monitoring included angle anomaly index threshold, it is determined that the travel route of the mobile detection device needs to be corrected; otherwise, it is determined that the travel route of the mobile detection device does not need to be corrected.

[0037] It should be noted that in the process of analyzing the correction of the travel route of the mobile detection device, the setting basis of the reference included angle threshold is as follows: 1. For relatively straight pipelines, the reference included angle threshold can be set relatively small, because a small angle deviation may mean that the device deviates from the pipeline direction. For pipelines with obvious bends or complex orientations, such as at the turning or branching points of the pipeline, since the device will also detect relatively large angle changes under normal circumstances, the reference included angle threshold needs to be set larger. 2. The single movement distance of the detection device is related to the reference included angle threshold. If the device moves relatively fast in a single movement and the number of angle data points collected per unit time is relatively small, in order to avoid missing important angle change information, the reference included angle threshold can be set relatively small.

[0038] Exemplarily, the reference included angle threshold is 。

[0039] The heat supply pipeline data acquisition module is used to acquire data of the target heat supply pipeline by using a combined sensor, and obtain the monitoring sensor data of the target heat supply pipeline, specifically including magnetic field intensity, magnetic field direction declination, reflection wave duration, reflection wave intensity, geomagnetic intensity, and magnetic declination.

[0040] It should be noted that the geomagnetic intensity mentioned in the present invention refers to the magnitude of the geomagnetic intensity.

[0041] It should be supplemented that the acquisition method of the monitoring sensor data of the target heat supply pipeline is as follows: Use an electromagnetic sensor to acquire the magnetic field intensity of the target heat supply pipeline, and the magnetic field direction declination can be acquired by using a Hall effect sensor. Use an ultrasonic sensor to acquire the reflection wave duration and reflection wave intensity of the target heat supply pipeline. Use a geomagnetic sensor to acquire the geomagnetic intensity and magnetic declination of the target heat supply pipeline. The magnetic declination can be acquired by using a magnetoresistive sensor.

[0042] The heat supply pipeline data analysis module is used to analyze the abnormal situation of the monitoring data of the target heat supply pipeline in the current monitoring section based on the data of the monitoring sensors of the target heat supply pipeline.

[0043] In a preferred embodiment of the present invention, to analyze the abnormal situation of the monitoring data of the target heat supply pipeline in the current monitoring section, it is necessary to construct an abnormal index of the monitoring data of the target heat supply pipeline in the current monitoring section. The specific method is as follows: Extract the magnetic field intensity and magnetic field direction declination corresponding to the current monitoring section of the target heat supply pipeline, and record them as 、 , and analyze to obtain the abnormal index of the electromagnetic sensor in the current monitoring section.

[0044] Extract the reflection wave duration and reflection wave intensity corresponding to the current monitoring section of the target heat supply pipeline, and record them as 、 , and analyze to obtain the abnormal index of the ultrasonic sensor in the current monitoring section.

[0045] Extract the geomagnetic intensity and magnetic declination corresponding to the current monitoring section of the target heat supply pipeline, and record them as 、 , and analyze to obtain the abnormal index of the geomagnetic sensor in the current monitoring section.

[0046] Extract the abnormal index of the electromagnetic sensor, the abnormal index of the ultrasonic sensor, and the abnormal index of the geomagnetic sensor in the current monitoring section, and then perform a weighted summation calculation to obtain the abnormal index of the monitoring data of the target heat supply pipeline in the current monitoring section.

[0047] It should be explained that the reasons for the electromagnetic sensor anomaly index, ultrasonic sensor anomaly index, and geomagnetic sensor anomaly index as the influencing factors of the monitoring data anomaly index of the target heating pipeline in the current monitoring section are as follows: 1. The electromagnetic sensor mainly detects underground metal pipelines and their accessories based on the principle of electromagnetic induction. Under normal circumstances, metal structures of pipeline accessory types will generate induced currents under the action of the alternating magnetic field generated by the sensor, and then form a secondary magnetic field that is received by the sensor. Due to the differences in their structures, materials, and shapes, different types of pipeline accessories will exhibit different electromagnetic characteristics. 2. The ultrasonic sensor works by emitting ultrasonic pulses and receiving reflected waves. For the position recognition of pipeline accessory types, parameters such as the time, intensity, and angle of the ultrasonic reflected wave can provide important information about the shape and boundary of the accessory. Under normal circumstances, the signals reflected by the ultrasonic waves on the pipeline wall and the accessory surface have certain rules, and the presence of pipeline accessory types will change this reflection rule. 3. The geomagnetic sensor uses the interference of underground pipelines and their accessories on the earth's magnetic field for detection. Metal pipeline accessory types will cause local abnormal changes in the surrounding geomagnetism because they will change the conduction path and distribution of the geomagnetism. This geomagnetic anomaly can be an important clue for the position recognition of pipeline accessory types.

[0048] Exemplarily, the weights corresponding to the electromagnetic sensor anomaly index, ultrasonic sensor anomaly index, and geomagnetic sensor anomaly index are .

[0049] It should be explained that the setting basis for the weights corresponding to the electromagnetic sensor anomaly index, ultrasonic sensor anomaly index, and geomagnetic sensor anomaly index: In terms of performance, the electromagnetic sensor has high precision but is vulnerable to electromagnetic interference, the ultrasonic sensor has acceptable precision but is greatly affected by the medium and temperature, and the geomagnetic sensor has relatively low precision but is relatively stable; In terms of characteristics, pipelines and accessories made of metal materials, with strong conductivity and complex structures are conducive to electromagnetic detection, the ultrasonic is sensitive to accessories with special shapes and variable surface conditions, and large accessories are prone to cause obvious geomagnetic anomalies.

[0050] In a preferred embodiment of the present invention, the specific method for analyzing the electromagnetic sensor anomaly index of the current monitoring section is as follows: Extract the magnetic field intensity and the magnetic field direction declination of the target heating pipeline corresponding to the current monitoring section.

[0051] It should be noted that the reasons for selecting the magnetic field strength and the magnetic field direction deviation angle as the influencing factors of the electromagnetic sensor anomaly index are as follows: 1. The magnetic field strength is a key index detected by the electromagnetic sensor. For underground metal heating pipelines and their accessories, they will generate a magnetic field around them. When the sensor approaches the pipeline or the accessory, the magnetic field strength will change. 2. The magnetic field direction deviation angle is another important parameter. For a straight pipeline, under ideal conditions, the magnetic field direction will show a certain regular change around the pipeline. When the pipeline direction changes (such as bending) or there are accessories with special shapes, the magnetic field direction deviation angle will change accordingly. 3. Combining the magnetic field direction deviation angle with the magnetic field strength can more accurately locate the pipeline and the accessory.

[0052] Using the formula Analyze to obtain the electromagnetic sensor anomaly index of the current monitoring section , where represents the magnetic field strength of the current monitoring section corresponding to the previous monitoring section, represents the magnetic field direction deviation angle of the current monitoring section corresponding to the previous monitoring section, respectively represent the influence weight factors corresponding to the magnetic field strength and the magnetic field direction deviation angle in the process of analyzing the electromagnetic sensor anomaly index set in advance.

[0053] Exemplarily, .

[0054] It should be noted that the setting basis of the influence weight factors corresponding to the magnetic field strength and the magnetic field direction deviation angle in the process of analyzing the electromagnetic sensor anomaly index: For pipeline position judgment, since the magnetic field strength is directly related to the distance from the pipeline and plays a significant role in determining the position, the magnetic field direction deviation angle plays an auxiliary role in refining the direction. When it comes to identifying the type of pipeline accessories, the magnetic field strength can highlight the characteristic changes caused by different accessory materials and structures, and the magnetic field direction deviation angle only has a unique indication at specific special-shaped accessories.

[0055] In a preferred embodiment of the present invention, the specific method for analyzing the ultrasonic sensor anomaly index of the current monitoring section is as follows: Extract the reflection wave duration and the reflection wave intensity of the target heating pipeline corresponding to the current monitoring section.

[0056] It should be noted that the reasons for selecting the reflection wave duration and the reflection wave intensity as the influencing factors of the ultrasonic sensor anomaly index are as follows: 1. Structural changes in the pipeline, such as changes in pipe diameter, bending, branching, and the presence of accessories, etc., will all cause changes in the reflection wave duration. For changes in pipe diameter, due to different ultrasonic propagation path lengths, the reflection wave duration will change accordingly. 2. The presence of pipeline accessories will have a significant impact on the reflection wave intensity. Different types of accessories have different geometric shapes and internal structures, and these factors will cause complex changes in ultrasonic reflection.

[0057] Using the formula Analyze to obtain the ultrasonic sensor anomaly index of the current monitoring section , where represents the reflection wave duration of the current monitoring section corresponding to the previous monitoring section, represents the reflection wave intensity of the current monitoring section corresponding to the previous monitoring section, respectively represent the influence weight factors corresponding to the reflection wave duration and reflection wave intensity in the analysis process of the ultrasonic sensor anomaly index, represents the natural constant.

[0058] Exemplarily, .

[0059] It should be noted that the setting basis of the influence weight factors corresponding to the reflection wave duration and reflection wave intensity in the analysis process of the ultrasonic sensor anomaly index: 1. For the reflection wave duration, it is closely related to the pipeline position and structural changes. The duration can accurately calculate the distance between the sensor and the pipeline, is very sensitive to the pipeline orientation, diameter change, bending and branching, etc., and can also reflect the medium characteristics. Therefore, it has obvious advantages in position detection and structural anomaly judgment, and the weight factor can be set higher. 2. The reflection wave intensity mainly reflects the interface characteristics and pipeline conditions, including the pipeline surface material, roughness, and the presence and type of accessories, and can evaluate the propagation loss and interference. However, in comparison, it is slightly inferior in determining the key information of the pipeline position and structure. Therefore, its weight factor is relatively low.

[0060] In a preferred embodiment of the present invention, the specific method for analyzing the geomagnetic sensor anomaly index of the current monitoring section is as follows: Extract the geomagnetic intensity of the target heating pipeline corresponding to the current monitoring section and magnetic declination .

[0061] It should be noted that the reasons for selecting the geomagnetic intensity and magnetic declination as the influencing factors of the geomagnetic sensor anomaly index: The geomagnetic intensity can intuitively reflect the interference degree of underground pipelines and their accessories on the earth's magnetic field, is closely related to the pipeline position and shape, and its change can be used to infer the pipeline position, orientation and shape. At the same time, after considering the influence of environmental factors, it can more accurately distinguish the geomagnetic anomalies caused by pipelines. The magnetic declination can effectively indicate the pipeline orientation and accessory position, especially obvious changes will occur at the pipeline shape change, and can assist in describing the spatial distribution characteristics of the magnetic field. Combining with the geomagnetic intensity can more comprehensively master the magnetic field changes.

[0062] Using the formula Analyze to obtain the geomagnetic sensor anomaly index of the current monitoring section , where Indicates the geomagnetic intensity of the current monitoring section corresponding to the previous monitoring section. Indicates the magnetic declination of the current monitoring section corresponding to the previous monitoring section. Respectively represent the influence weight factors corresponding to the geomagnetic intensity and magnetic declination during the analysis process of the pre-set geomagnetic sensor anomaly index.

[0063] Exemplarily, .

[0064] It should be explained that the setting basis of the influence weight factors corresponding to the geomagnetic intensity and magnetic declination during the analysis process of the geomagnetic sensor anomaly index: In pipeline positioning, the geomagnetic intensity is directly related to the position and can determine the approximate position of the pipeline. The magnetic declination plays an auxiliary role and is mainly used to clarify the pipeline orientation and shape changes. For the detection of pipeline accessory types, the geomagnetic intensity can effectively detect the presence of accessories and preliminarily judge the type, and the magnetic declination can assist in identifying the position and shape of special-shaped accessories.

[0065] The influence factor data analysis module is used to collect the influence factor data of the location where the mobile detection device is located, including soil factor data and influence factor data between sensors. The soil factor data includes soil conductivity, soil magnetic permeability, and soil temperature, and the influence factor data between sensors includes installation spacing and monitoring magnetic field intensity, and analyzes the evaluation situation of the influence factor data.

[0066] It should be supplemented that the acquisition method of the soil factor data: The soil conductivity, soil magnetic permeability, and soil temperature can be collected by using a soil conductivity sensor, a soil magnetic permeability sensor, and a temperature sensor respectively.

[0067] It should be supplemented that the acquisition method of the installation spacing: Use a three-dimensional scanning device to obtain the three-dimensional scanning data of the mobile detection device, and then obtain the end positions of the electromagnetic sensor, ultrasonic sensor, and geomagnetic sensor, obtain the spacing between the end of the electromagnetic sensor and the end of the ultrasonic sensor, the spacing between the end of the electromagnetic sensor and the end of the geomagnetic sensor, and the spacing between the end of the geomagnetic sensor and the end of the ultrasonic sensor, and then perform an average calculation to obtain the installation spacing.

[0068] It should be supplemented that the monitoring magnetic field intensity can be collected by using a gaussmeter.

[0069] In a preferred embodiment of the present invention, to analyze the evaluation situation of the influence factor data, it is necessary to construct an influence factor data evaluation coefficient for the target heating pipeline in the current monitoring section. The specific method is as follows: Extract the soil conductivity, soil magnetic permeability, and soil temperature of the current monitoring target heating pipeline, and compare them with the pre-set reference soil conductivity, reference soil magnetic permeability, and reference soil temperature respectively, and analyze to obtain the soil influence evaluation coefficient of the target heating pipeline in the current monitoring section.

[0070] It should be noted that the reasons for selecting soil conductivity, soil permeability, and soil temperature as the influencing factors of the soil impact evaluation coefficient are as follows: 1. Soil conductivity is a physical quantity that measures the ability of soil to conduct electric current. When an electromagnetic sensor is used to detect underground heating pipelines, soil conductivity plays a crucial role. Soils with high conductivity will have a strong attenuation effect on electromagnetic signals. 2. Soil permeability determines the propagation characteristics of magnetic fields in soil. For the detection of geomagnetic sensors and electromagnetic sensors, the influence of soil permeability cannot be ignored. Soils with higher permeability will make it easier for magnetic fields to propagate in them and will change the distribution of magnetic fields. 3. Soil temperature is an important factor affecting the propagation speed of ultrasonic waves in soil. According to the ultrasonic wave propagation theory, there is a certain relationship between temperature and sound speed. At different temperatures, the propagation speed of ultrasonic waves in soil will change.

[0071] It should be supplemented that the specific method for analyzing the soil impact evaluation coefficient of the target heating pipeline in the current monitoring section is as follows: Extract the soil conductivity, soil permeability, and soil temperature of the target heating pipeline in the current monitoring target, and then calculate the absolute values after calculating the differences with the preset reference soil conductivity, reference soil permeability, and reference soil temperature respectively to obtain the soil conductivity deviation value, soil permeability deviation value, and soil temperature deviation value in the current monitoring section. Calculate the ratios with the reference soil conductivity, reference soil permeability, and reference soil temperature respectively to obtain the soil conductivity deviation degree, soil permeability deviation degree, and soil temperature deviation degree in the current monitoring section, and then calculate the average value to obtain the soil impact evaluation coefficient of the target heating pipeline in the current monitoring section.

[0072] Extract the installation spacing and monitoring magnetic field intensity of the target heating pipeline in the current monitoring target, compare them with the preset reference installation spacing and reference magnetic field intensity respectively, and analyze to obtain the sensor - to - sensor impact evaluation coefficient of the target heating pipeline in the current monitoring section.

[0073] It should be noted that the reasons for selecting the installation spacing and monitoring magnetic field intensity as the influencing factors of the sensor - to - sensor impact evaluation coefficient are as follows: 1. The installation spacing directly determines the degree of interaction between the magnetic fields of sensors. When the sensor spacing is too small, the magnetic fields generated by them or the magnetic fields they sense are likely to be superimposed. A smaller installation spacing may also lead to cross - influence of sensor signals. 2. The monitoring magnetic field intensity is a key indicator for measuring electromagnetic coupling between sensors. For electromagnetic sensors and geomagnetic sensors, the magnetic field intensity determines the degree of their mutual influence. When the magnetic field intensity around the sensors is strong, it means that electromagnetic coupling is more likely to occur between them.

[0074] It should be supplemented that the specific method for analyzing the influence evaluation coefficient between sensors of the target heating pipeline in the current monitoring section is as follows: Extract the installation spacing and monitoring magnetic field intensity of the target heating pipeline in the current monitoring target, calculate the differences from the pre-set reference installation spacing and reference magnetic field intensity respectively, and then take the absolute values to obtain the installation spacing deviation amount and monitoring magnetic field intensity deviation amount of the target heating pipeline in the current monitoring section. Then, calculate the ratios with the corresponding reference installation spacing and reference monitoring magnetic field intensity respectively to obtain the installation spacing deviation degree and monitoring magnetic field intensity deviation degree of the target heating pipeline in the current monitoring section. Furthermore, calculate the average value to obtain the influence evaluation coefficient between sensors of the target heating pipeline in the current monitoring section.

[0075] Sum the soil influence evaluation coefficient and the influence evaluation coefficient between sensors of the target heating pipeline in the current monitoring section according to the weights to obtain the influence factor data evaluation coefficient of the target heating pipeline in the current monitoring section.

[0076] Exemplarily, the weights corresponding to the soil influence evaluation coefficient and the influence evaluation coefficient between sensors are respectively .

[0077] It should be noted that the setting basis of the weights corresponding to the soil influence evaluation coefficient and the influence evaluation coefficient between sensors: From the perspective of the contribution to the accuracy of pipeline detection, soil factors directly interfere with the signal propagation and reception of sensors due to conductivity, permeability and temperature, which is related to the detection accuracy; the influence factors between sensors can be alleviated by layout and technical means. Considering stability and adjustability, soil factors are relatively stable and difficult to adjust in the short term, and the weights need to be considered emphatically; the factors between sensors can be improved by adjusting the spacing and strengthening shielding, and the weights are relatively flexible.

[0078] The data anomaly identification module is used to judge whether there is an anomaly in pipeline accessories based on the monitoring data anomaly situation and the influence factor data evaluation situation of the target heating pipeline in the current monitoring section.

[0079] For the preferred embodiment of the present invention, please refer to Figure 3 As shown, the specific method for judging whether there is an anomaly in pipeline accessories is as follows: Extract the monitoring data anomaly index and the influence factor data evaluation coefficient of the target heating pipeline in the current monitoring section, calculate the product to obtain the corrected monitoring data anomaly index amount of the target heating pipeline in the current monitoring section, and calculate the difference between the monitoring data anomaly index of the target heating pipeline and the corrected monitoring data anomaly index amount to obtain the corrected monitoring data anomaly index of the target heating pipeline in the current monitoring section.

[0080] Compare the corrected monitoring data anomaly index of the target heating pipeline in the current monitoring section with the pre-set monitoring data anomaly index threshold. If the corrected monitoring data anomaly index is greater than the monitoring data anomaly index threshold, it is judged that there is an anomaly in pipeline accessories; otherwise, it is judged that there is no anomaly in pipeline accessories.

[0081] Exemplarily, the monitoring data anomaly index threshold is .

[0082] It should be noted that the setting basis of the monitoring data anomaly index threshold is as follows: 1. The setting of the monitoring data anomaly index threshold should first be based on the performance of the sensor itself. Different sensors have different measurement error ranges. 2. By collecting historical monitoring data of the pipeline in the normal operating state, the fluctuation range of the data can be determined. For example, for the reflection wave duration monitored by an ultrasonic sensor, when there is no anomaly in the pipeline, there may be certain fluctuations due to factors such as ambient temperature and slight changes in the medium.

[0083] The heating pipeline accessory identification is used to obtain the cross-sectional shape of the heating pipeline by using an ultrasonic sensor when it is determined that there is an anomaly in the pipeline accessory, and then identify the specific type of pipeline accessory.

[0084] In a preferred embodiment of the present invention, the specific method for identifying the specific type of pipeline accessory is as follows: Extract the cross-sectional shape of the target heating pipeline in the current monitoring section, and then obtain the cross-sectional area and cross-sectional perimeter of the target heating pipeline.

[0085] It should be added that the method for obtaining the cross-sectional shape of the target heating pipeline in the current monitoring section is as follows: Locate the center point of the target heating pipeline in the current monitoring section, and based on the center point position, use an ultrasonic sensor to measure the target heating pipeline multiple times to obtain a number of ultrasonic reflection data, and then construct the cross-section of the target heating pipeline to obtain the cross-sectional shape of the target heating pipeline.

[0086] It should be noted that the reasons for selecting the cross-sectional area and cross-sectional perimeter as the influencing factors of the matching coefficient between the target heating pipeline and various pipeline accessory types are as follows: 1. In the process of identifying the type of pipeline accessory, the cross-sectional area is a key factor. Different types of pipeline accessories with different functions often have different requirements for the cross-sectional area. By comparing the cross-sectional area, these different functional accessory types can be initially distinguished. 2. For some special-shaped pipeline accessory types, the cross-sectional perimeter can provide important identification clues. Like a four-way pipe fitting, its cross-sectional perimeter will have obvious change rules at the four branches, which is different from a three-way pipe or other simple-shaped accessories. By analyzing this change in the cross-sectional perimeter, the types of these special-shaped accessories can be accurately identified.

[0087] Extract the cross-sectional data corresponding to each pipeline accessory type of the target heating pipeline stored in the data repository to obtain the standard cross-sectional area and standard cross-sectional perimeter of each pipeline accessory type.

[0088] Compare the cross-sectional area and cross-sectional perimeter of the target heating pipeline with the standard cross-sectional area and standard cross-sectional perimeter of each pipeline accessory type respectively to obtain the area matching coefficient and perimeter matching coefficient corresponding to the cross-section of the target heating pipeline and each pipeline accessory type.

[0089] It should be supplemented that the obtaining methods of the area matching coefficient and perimeter matching coefficient corresponding to the cross-section of the target heating pipeline and each pipeline accessory type are as follows: Compare the cross-sectional area of the target heating pipeline with the standard cross-sectional area of each pipeline accessory type, and calculate the ratio of the smaller cross-sectional area to the larger cross-sectional area among the cross-sectional area of the target heating pipeline and the standard cross-sectional area of each pipeline accessory type to obtain the area matching coefficient corresponding to the cross-section of the target heating pipeline and each pipeline accessory type.

[0090] It should be noted that the obtaining methods of the smaller cross-sectional area and the larger cross-sectional area are as follows: Compare the cross-sectional area of the target heating pipeline with the standard cross-sectional area of a certain pipeline accessory type. If the cross-sectional area of the target heating pipeline is greater than the standard cross-sectional area of this pipeline accessory type, record the cross-sectional area of the target heating pipeline as the larger cross-sectional area and the standard cross-sectional area of this pipeline accessory type as the smaller cross-sectional area. On the contrary, record the cross-sectional area of the target heating pipeline as the smaller cross-sectional area and the standard cross-sectional area of this pipeline accessory type as the larger cross-sectional area.

[0091] Exemplarily, if the cross-sectional area of the target heating pipeline is and the standard cross-sectional area of a certain pipeline accessory type is , then the smaller cross-sectional area is the standard cross-sectional area of this pipeline accessory type , and the larger cross-sectional area is the cross-sectional area of the target heating pipeline .

[0092] Compare the cross-sectional perimeter of the target heating pipeline with the standard cross-sectional perimeter of each pipeline accessory type, and calculate the ratio of the smaller cross-sectional perimeter to the larger cross-sectional perimeter among the cross-sectional perimeter of the target heating pipeline and the standard cross-sectional perimeter of each pipeline accessory type to obtain the perimeter matching coefficient corresponding to the cross-section of the target heating pipeline and each pipeline accessory type.

[0093] Similarly, the obtaining method of the smaller cross-sectional perimeter and the larger cross-sectional perimeter refers to the obtaining method of the smaller cross-sectional area and the larger cross-sectional area.

[0094] Sum up the area matching coefficient and perimeter matching coefficient corresponding to the cross-section of the target heating pipeline and each pipeline accessory type according to the weight to obtain the matching coefficient between the target heating pipeline and each pipeline accessory type.

[0095] Exemplarily, the weights corresponding to the area matching coefficient and the perimeter matching coefficient for the target heat supply pipeline cross-section corresponding to each pipeline accessory type are respectively .

[0096] It should be noted that the setting basis for the weights corresponding to the area matching coefficient and the perimeter matching coefficient for the target heat supply pipeline cross-section corresponding to each pipeline accessory type: 1. In the identification of pipeline accessory types, the area matching coefficient contributes greatly to the identification accuracy. Because for different types of pipeline accessories, their internal structures and functions determine that the cross-sectional area characteristics are significantly different. 2. The perimeter matching coefficient also makes an important contribution to the identification accuracy. It plays a key role in identifying the sealing structure, connection method, and special-shaped accessories of the accessories.

[0097] Compare the matching coefficients of the target heat supply pipeline with each pipeline accessory type, and select the pipeline accessory type corresponding to the maximum matching coefficient as the pipeline accessory type of the target heat supply pipeline.

[0098] Exemplarily, the pipeline accessory types include but are not limited to compensators, fixed joints, reducers, tees, and blind ends.

[0099] The data repository is used to save the cross-sectional data corresponding to each pipeline accessory type of the target heat supply pipeline.

[0100] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. The accurate geophysical detection system for direct buried heating pipeline accessories based on multi-sensor fusion is characterized by: include: A mobile detection equipment setting module, used to set up a mobile monitoring device, wherein the mobile detection device includes a mobile device, an infrared thermal imager and a combined sensor; The heating pipeline route identification module is used to use an infrared thermal imager to perform mobile temperature detection on the target heating pipeline, analyze the direction of the target heating pipeline based on the temperature detection results, and correct the route of the mobile detection equipment in real time; The heating pipeline data acquisition module is used to collect data from the target heating pipeline using a combined sensor to obtain the monitoring sensor data of the target heating pipeline, including magnetic field intensity, magnetic field direction declination, reflection wave duration, reflection wave intensity, geomagnetic intensity and magnetic declination; A heating pipeline data analysis module is used to analyze abnormal monitoring data of the target heating pipeline in the current monitoring section based on the monitoring sensor data of the target heating pipeline; The influencing factor data analysis module is used to collect the influencing factor data of the location of the mobile detection equipment, including soil factor data and inter-sensor influencing factor data, where the soil factor data includes soil conductivity, soil magnetic permeability and soil temperature, and the inter-sensor influencing factor data includes installation spacing and monitoring magnetic field strength, and analyze the evaluation of the influencing factor data. Extract the soil conductivity, soil magnetic permeability and soil temperature of the target heating pipeline currently being monitored, and compare them with the preset reference soil conductivity, reference soil magnetic permeability and reference soil temperature respectively, and analyze and obtain the soil impact evaluation coefficient of the target heating pipeline in the current monitoring section; The installation spacing and monitoring magnetic field strength of the current monitoring target heating pipeline are extracted, and compared with the preset reference installation spacing and reference monitoring magnetic field strength, respectively, to analyze and obtain the sensor-to-sensor impact evaluation coefficient of the current monitoring section target heating pipeline; The soil impact evaluation coefficient and the inter-sensor impact evaluation coefficient of the target heating pipeline in the current monitoring section are summed up according to the weights to obtain the impact factor data evaluation coefficient of the target heating pipeline in the current monitoring section; The data anomaly identification module is used to determine whether there is an anomaly in the pipeline accessories based on the monitoring data anomaly of the target heating pipeline in the current monitoring section and the evaluation of the influencing factor data; Heating pipe accessory identification, which is used to obtain the cross-sectional shape of the heating pipe using an ultrasonic sensor when it is determined that there is an abnormality in the pipe accessory, and then identify the specific type of pipe accessory; The data repository is used to store the cross-sectional data corresponding to each pipe accessory type of the target heating pipeline.

2. The direct buried heating pipeline accessories accurate geophysical detection system based on multi-sensor fusion as claimed in claim 1, characterized in that: The specific process of analyzing the direction of the target heating pipeline is as follows: The route of the mobile detection device is divided into several monitoring sections based on equal interval distances, and the mobile detection device moves each time with a preset interval distance after confirming the direction of travel; Use an infrared thermal imager to obtain the thermal image corresponding to the target heating pipeline in real time, locate each color area in the corresponding thermal image, and then obtain the chromaticity value corresponding to each color area. Compare the chromaticity value corresponding to each color area with the pre-saved chromaticity value and temperature relationship to obtain the temperature value corresponding to each color area, and then select the color area corresponding to the maximum temperature value as the monitoring color area, and record the center point of the monitoring color area as the monitoring direction point corresponding to the current monitoring section; The starting point of the target heating pipeline is recorded as the initial monitoring point, and the direction corresponding to the monitoring direction point corresponding to the next monitoring section pointed by the initial monitoring point is recorded as the planned travel direction corresponding to the monitoring section, and then the mobile detection device is used to move along the planned travel direction, and then the planned travel direction analysis of the subsequent monitoring sections is performed; Obtain the planned travel direction corresponding to each monitoring section, and then connect them end to end to obtain the direction of the target heating pipeline.

3. The direct buried heating pipeline accessories accurate geophysical detection system based on multi-sensor fusion as claimed in claim 1, characterized in that: The specific method of correcting the route of the mobile detection device is as follows: Obtain the travel direction of the previous monitoring segment and the travel direction of the current monitoring segment corresponding to the current monitoring segment, and record the angle between the two as the monitoring angle corresponding to the current monitoring segment; The monitoring angle corresponding to the current monitoring segment is ratioed with the preset reference angle threshold to obtain the monitoring angle anomaly index corresponding to the current monitoring segment, which is then compared with the preset monitoring angle anomaly index threshold. If the monitoring angle anomaly index corresponding to a monitoring segment is greater than the preset monitoring angle anomaly index threshold, it is determined that the mobile detection device needs to correct its route; otherwise, it is determined that the mobile detection device does not need to correct its route.

4. The direct buried heating pipeline accessories accurate geophysical detection system based on multi-sensor fusion as claimed in claim 1, characterized in that: The analysis of the abnormality of the monitoring data of the target heating pipeline in the current monitoring section requires the construction of an abnormality index of the monitoring data of the target heating pipeline in the current monitoring section, and the specific method is as follows: Extract the magnetic field intensity and magnetic field direction angle of the target heating pipeline corresponding to the current monitoring section, which are recorded as , , analyze and obtain the abnormal index of electromagnetic sensor in the current monitoring section ; Extract the reflection wave duration and reflection wave intensity of the target heating pipeline corresponding to the current monitoring section, which are recorded as , , analyze and obtain the abnormal index of ultrasonic sensor in the current monitoring section ; Extract the geomagnetic intensity and magnetic declination of the target heating pipeline corresponding to the current monitoring section, which are recorded as , , analyze and obtain the geomagnetic sensor anomaly index of the current monitoring section ; The electromagnetic sensor anomaly index, ultrasonic sensor anomaly index and geomagnetic sensor anomaly index of the current monitoring section are extracted, and then the monitoring data anomaly index of the target heating pipeline in the current monitoring section is obtained by summing them up according to the weights.

5. The direct buried heating pipeline accessories accurate geophysical detection system based on multi-sensor fusion as claimed in claim 4, characterized in that: The specific method of analyzing and obtaining the abnormal index of the electromagnetic sensor in the current monitoring section is as follows: Extract the magnetic field strength of the target heating pipeline corresponding to the current monitoring section and the magnetic field direction angle ; Using the formula Analyze and obtain the abnormal index of electromagnetic sensors in the current monitoring section ,in Indicates the magnetic field strength of the current monitoring segment corresponding to the previous monitoring segment. Indicates the magnetic field direction deflection angle of the current monitoring segment corresponding to the previous monitoring segment. They respectively represent the influencing weight factors corresponding to the magnetic field intensity and the magnetic field direction angle in the preset electromagnetic sensor anomaly index analysis process.

6. The direct buried heating pipeline accessories accurate geophysical detection system based on multi-sensor fusion as claimed in claim 4, characterized in that: The specific method of analyzing and obtaining the abnormal index of the ultrasonic sensor in the current monitoring section is as follows: Extract the reflection wave duration of the target heating pipeline corresponding to the current monitoring section and reflected wave intensity ; Using the formula Analyze and obtain the abnormal index of ultrasonic sensor in the current monitoring section ,in Indicates the reflection wave duration of the current monitoring segment corresponding to the previous monitoring segment. Indicates the reflected wave intensity of the current monitoring segment corresponding to the previous monitoring segment. They represent the influence weight factors corresponding to the reflection wave duration and reflection wave intensity in the preset ultrasonic sensor abnormal index analysis process, Represents a natural constant.

7. The direct buried heating pipeline accessories accurate geophysical detection system based on multi-sensor fusion as claimed in claim 4, characterized in that: The specific method of analyzing and obtaining the geomagnetic sensor anomaly index of the current monitoring section is as follows: Extract the geomagnetic intensity of the target heating pipeline corresponding to the current monitoring section and magnetic declination ; Using the formula Analyze and obtain the geomagnetic sensor anomaly index of the current monitoring section ,in Indicates the geomagnetic intensity of the current monitoring section corresponding to the previous monitoring section. Indicates the magnetic declination of the current monitoring segment corresponding to the previous monitoring segment. They respectively represent the influence weight factors corresponding to the geomagnetic intensity and magnetic declination in the pre-set geomagnetic sensor anomaly index analysis process.

8. The direct buried heating pipeline accessories accurate geophysical detection system based on multi-sensor fusion as claimed in claim 4, characterized in that: The specific method of determining whether there is an abnormality in the pipeline attachment is as follows: Extract the monitoring data anomaly index and the influencing factor data evaluation coefficient of the target heating pipeline in the current monitoring section, perform product calculation to obtain the monitoring data anomaly index correction amount of the target heating pipeline in the current monitoring section, perform difference calculation on the monitoring data anomaly index of the target heating pipeline and the monitoring data anomaly index correction amount to obtain the corrected monitoring data anomaly index of the target heating pipeline in the current monitoring section; The corrected monitoring data anomaly index of the target heating pipeline in the current monitoring section is compared with the pre-set monitoring data anomaly index threshold. If the corrected monitoring data anomaly index is greater than the monitoring data anomaly index threshold, it is judged that there is an abnormality in the pipeline accessories. Otherwise, it is judged that there is no abnormality in the pipeline accessories.

9. The direct buried heating pipeline accessories accurate geophysical detection system based on multi-sensor fusion as claimed in claim 8, characterized in that: The specific method of identifying the specific pipe attachment type is as follows: Extract the cross-sectional shape of the target heating pipeline in the current monitoring section, and then obtain the cross-sectional area and cross-sectional perimeter of the target heating pipeline; Extract the cross-sectional data corresponding to each pipe accessory type of the target heating pipe stored in the data repository to obtain the standard cross-sectional area and standard cross-sectional perimeter of each pipe accessory type; Compare the cross-sectional area and cross-sectional perimeter of the target heating pipeline with the standard cross-sectional area and standard cross-sectional perimeter of each type of pipeline accessories to obtain the area matching coefficient and perimeter matching coefficient corresponding to the cross-sectional area of ​​the target heating pipeline and each type of pipeline accessories; The area matching coefficient and the perimeter matching coefficient corresponding to the target heating pipeline cross section and each pipeline accessory type are summed up according to the weight to obtain the matching coefficient between the target heating pipeline and each pipeline accessory type; The matching coefficients of the target heating pipeline and each pipeline accessory type are compared, and the pipeline accessory type corresponding to the maximum matching coefficient is selected as the pipeline accessory type of the target heating pipeline.

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