Directly-buried heat supply pipeline accessory accurate geophysical prospecting detection system based on multi-sensor fusion

Abstract

The invention belongs to the technical field of heat supply pipeline accessory identification, and discloses a direct burial heat supply pipeline accessory accurate geophysical prospecting detection system based on multi-sensor fusion. Comprising a mobile detection equipment setting module, a heat supply pipeline route identification module, a heat supply pipeline data acquisition module, a heat supply pipeline data analysis module, an influence factor data analysis module, a data abnormal condition identification module, a heat supply pipeline accessory identification module and a data storage library. According to the method, the influence possibly caused by the soil factor data and the influence factor data between the sensors is comprehensively considered during data abnormal condition identification, so that whether the pipeline accessories exist or not is comprehensively judged, and the accuracy and reliability of system detection can be improved. According to the invention, comprehensive analysis is carried out based on the data abnormal conditions of the electromagnetic sensor, the ultrasonic sensor and the geomagnetic sensor when the abnormal conditions of the monitoring data of the target heat supply pipeline at the current monitoring section are analyzed, so that the detection accuracy and reliability can be improved.

Description

Technical Field

[0001] The invention belongs to the technical field of heating pipeline accessory type identification, and relates to a direct-buried heating pipeline accessory accurate geophysical detection system based on multi-sensor fusion. Background Art

[0002] Identification of heating pipe accessory types refers to the process of identifying and classifying various accessories in the heating pipe system through various technical means and methods. These accessories include but are not limited to valves, compensators, flanges, tees, etc. When a heating system fails, accurate identification of the type of pipe accessories can help maintenance personnel quickly locate the problem. Different types of pipe accessory types require specific components and methods for maintenance. Existing heating pipes are located underground, and it is often difficult to directly identify the type of heating pipe accessories. Therefore, the analysis of accurate geophysical detection of direct-buried heating pipe 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 invention patent application with publication number CN115684152A is a sheet metal auxiliary identification system and method with multi-sensor data fusion, which includes: a human infrared sensor and an illuminance sensor are fixed at the lower end of the 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 determine the current state of manually placed sheet metal, the illuminance sensor is used to feedback the actual on-site illuminance conditions of the sheet metal identification environment, and the industrial camera is used to collect sheet metal images.

[0004] In addition, a Chinese invention patent application for an indoor positioning method and system based on multi-sensor fusion with 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 environmental information and generate indoor two-dimensional grid maps with the help of indoor two-dimensional modeling, indoor positioning, robot control and visual positioning technologies, and on this basis, use encoders, gyroscopes, and visual sensors to obtain the relative offset position information and direction information of the vehicle body, and use laser radar sensors to correct the accumulated errors to obtain more accurate global positioning information.

[0005] Although the above two schemes have proposed some solutions for multi-sensor data fusion analysis, there are still certain limitations: on the one hand, the existing schemes usually only involve the identification of target objects, but lack targeted corrections to the influencing factors that may affect the result analysis during the target object identification process. This analysis method will reduce the accuracy and efficiency of the target object identification results, resulting in object identification failure, which in turn affects subsequent inspection and maintenance; on the other hand, the existing multi-sensor data fusion analysis process is usually a differential analysis of standard data, lacking dynamic reference analysis of target data. This analysis method may reduce the adaptability of the data analysis process and affect the accuracy of the data analysis results. Summary of the invention

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

[0007] The object of the present invention can be achieved through the following technical scheme: a precise geophysical detection system for direct-buried heating pipeline accessories based on multi-sensor fusion, comprising: a mobile detection equipment setting module, used to set up mobile monitoring equipment, and the mobile detection equipment includes a mobile device, an infrared thermal imager and a combined sensor.

[0008] 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.

[0009] 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.

[0010] The 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.

[0011] 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 influencing factor data between sensors, where the soil factor data includes soil conductivity, soil magnetic permeability and soil temperature, and the influencing factor data between sensors includes installation spacing and monitoring magnetic field strength, and analyzes the evaluation of the influencing factor data.

[0012] 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 data evaluation of the influencing factors.

[0013] Heating pipe accessory identification 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.

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

[0015] Compared with the prior art, the present invention has the following beneficial effects: (1) When identifying data anomalies, the present invention comprehensively considers the possible impact of soil factor data and sensor-to-sensor influencing factor data, and then comprehensively judges whether there are pipeline accessory anomalies. This analysis method can improve the accuracy and reliability of system detection, eliminate misjudgments caused by soil characteristics and mutual interference between sensors, and more accurately judge the actual situation of pipeline accessory types. It can adapt to complex and changing environments, thereby providing pipeline maintenance personnel with accurate maintenance location information and reducing the blindness of maintenance.

[0016] (2) When analyzing the abnormality of the monitoring data of the target heating pipeline in the current monitoring section, the present invention conducts a comprehensive analysis based on the abnormality of the data 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 of the pipeline respectively. The data can complement each other and reduce the misjudgment and missed judgment caused by environmental and performance factors. On the other hand, it is conducive to a comprehensive assessment of the pipeline status 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, which meets the diverse monitoring needs of heating pipelines. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 It is a schematic diagram of the connection of each module of the system of the present invention.

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

[0020] Figure 3 A judgment flow chart corresponding to an embodiment of judging the existence of abnormality of a pipeline accessory provided by the present invention.

[0021] Figure numbers: 1 - previous monitoring section, 2 - current monitoring section, 3 - monitoring direction point, 4 - planned travel direction. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

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

[0024] The mobile detection equipment setting module is used to set up a mobile monitoring equipment, and the mobile detection equipment 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 electrical conductivity sensor, a soil magnetic permeability sensor and a temperature sensor.

[0026] 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 result, and correct the route of the mobile detection equipment 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 sections based on equal intervals, and after the mobile detection device confirms the travel direction, the distance moved each time is a preset interval distance.

[0028] It should be noted that the setting of the equally spaced distances in the present invention is to facilitate a series of operations such as subsequent data collection, analysis and route control, thereby ensuring the efficiency of the target heating pipeline route identification and abnormality identification process, while ensuring the accuracy of route identification and the precision of abnormal situations, reducing the amount of data processing and improving data processing efficiency.

[0029] For example, the equally spaced distances may be .

[0030] It should be noted that the setting basis of the equal-interval distances is as follows: 1. The setting of the equal-interval distances needs to refer to the maximum collection distance of each sensor and the maximum scanning distance of the infrared thermal imager, so that when performing data processing, it can be ensured that all parts of the target heating pipeline are collected to avoid omissions that lead to missed identification of some pipeline accessory types; 2. The setting of the equal-interval distances also needs to consider the recognition accuracy of the target heating pipeline. When the mobile detection equipment moves, it is necessary to avoid the word moving too long, resulting in excessive data deviation of adjacent monitoring segments, which in turn leads to increased complexity of subsequent corrections.

[0031] See also Figure 2 As shown, an infrared thermal imager is used to obtain the thermal image corresponding to the target heating pipeline in real time, and each color area in the corresponding thermal image is located, and then the chromaticity value corresponding to each color area is obtained, and the chromaticity value corresponding to each color area is compared with the pre-saved chromaticity value and temperature relationship to obtain the temperature value corresponding to each color area, and then the color area corresponding to the maximum temperature value is selected as the monitoring color area, and the center point of the monitoring color area is recorded as the monitoring direction point corresponding to the current monitoring section.

[0032] 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 equipment is used to move along the planned travel direction to perform the planned travel direction analysis of the subsequent monitoring sections.

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

[0034] 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.

[0035] In a preferred embodiment of the present invention, 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.

[0036] 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.

[0037] It should be noted that in the process of correcting the route analysis of the mobile detection equipment, the reference angle threshold is set based on the following: 1. For relatively straight pipelines, the reference angle threshold can be set relatively small, because a small angle deviation may mean that the equipment has deviated from the direction of the pipeline. For pipelines with obvious bends or complex directions, such as turns or branches of the pipeline, the reference angle threshold needs to be set larger because the equipment will also detect large angle changes under normal circumstances. 2. The single movement distance of the detection equipment is related to the reference angle threshold. If the single movement distance of the equipment is fast, the number of angle data points collected per unit time is relatively small. In order to avoid missing important angle change information, the reference angle threshold can be set relatively small.

[0038] Exemplarily, the reference angle threshold is .

[0039] The heating pipeline data acquisition module is used to use a combined sensor to collect data on the target heating pipeline to obtain monitoring sensor data of the target heating 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 monitoring sensor data of the target heating pipeline is obtained in the following ways: the magnetic field strength of the target heating pipeline is obtained by using an electromagnetic sensor, and the magnetic field direction deflection angle can be obtained by using a Hall effect sensor. The reflected wave duration and reflected wave intensity of the target heating pipeline are obtained by using an ultrasonic sensor. The geomagnetic intensity and magnetic deflection angle of the target heating pipeline are obtained by using a geomagnetic sensor. The magnetic deflection angle can be obtained by using a magnetoresistive sensor.

[0042] The 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.

[0043] In a preferred embodiment of the present invention, the analysis of the abnormality of the monitoring data of the target heating pipeline in the current monitoring section requires the construction of the 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 strength and magnetic field direction deflection angle of the target heating pipeline corresponding to the current monitoring section, and record them as , , analyze and obtain the abnormal index of electromagnetic sensor in the current monitoring section .

[0044] 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 .

[0045] 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 .

[0046] 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.

[0047] It is necessary to explain that the electromagnetic sensor anomaly index, ultrasonic sensor anomaly index and geomagnetic sensor anomaly index are the reasons for the influencing factors of the monitoring data anomaly index of the target heating pipeline in the current monitoring section: 1. Electromagnetic sensors are mainly based on the principle of electromagnetic induction to detect underground metal pipes and their accessories. Under normal circumstances, the metal structure of the pipe accessory type will generate an induced current under the action of the alternating magnetic field generated by the sensor, and then form a secondary magnetic field to be received by the sensor. Different types of pipe accessory types will show different electromagnetic characteristics due to differences in their structure, material and shape. 2. Ultrasonic sensors work by emitting ultrasonic pulses and receiving reflected waves. For the location identification of pipe accessory types, parameters such as the time, intensity and angle of ultrasonic reflection waves can provide important information about the shape and boundary of the attachment. Under normal circumstances, the signal reflected by the ultrasonic wave on the pipe wall and the surface of the attachment has a certain regularity, and the existence of the pipe accessory type will change this reflection regularity. 3. Geomagnetic sensors use the interference of underground pipes and their accessories on the earth's magnetic field for detection. Metal pipe 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 serve as an important clue for the location identification of pipe accessory types.

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

[0049] It needs to be explained that the weights corresponding to the electromagnetic sensor anomaly index, ultrasonic sensor anomaly index and geomagnetic sensor anomaly index are set based on the following: in terms of performance, electromagnetic sensors have high accuracy but are susceptible to electromagnetic interference, ultrasonic sensors have acceptable accuracy but are greatly affected by the medium and temperature, and geomagnetic sensors have low accuracy but are relatively stable; in terms of characteristics, pipes and accessories made of metal, with strong conductivity and complex structures are conducive to electromagnetic detection, ultrasonic waves are sensitive to accessories with special shapes and changeable surface conditions, and large accessories are prone to cause obvious geomagnetic anomalies.

[0050] In a preferred embodiment of the present invention, the specific method of analyzing and obtaining the abnormal index of the electromagnetic sensor of the current monitoring section is as follows: extracting the magnetic field strength of the target heating pipeline corresponding to the current monitoring section and the magnetic field direction angle .

[0051] It should be explained that the reasons for choosing magnetic field strength and magnetic field direction deflection as influencing factors of electromagnetic sensor anomaly index are: 1. Magnetic field strength is a key indicator for electromagnetic sensor detection. For underground metal heating pipes and their accessories, they will generate a magnetic field around them. When the sensor is close to the pipe or accessories, the magnetic field strength will change. 2. The magnetic field direction deflection is another important parameter. For straight pipes, under ideal conditions, the direction of the magnetic field will show a certain regularity around the pipe. When the direction of the pipe changes (such as bending) or there are accessories of special shapes, the magnetic field direction deflection will change accordingly. 3. The magnetic field direction deflection combined with the magnetic field strength can more accurately locate pipes and accessories.

[0052] 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.

[0053] For example, .

[0054] It is necessary to explain that the influencing weight factors corresponding to the magnetic field strength and magnetic field direction deflection in the analysis of the electromagnetic sensor abnormal index are set based on the following: For pipeline position judgment, the magnetic field strength is directly related to the pipeline distance and plays a significant role in determining the position, while the magnetic field direction deflection plays an auxiliary role in refining the direction. In terms of pipeline accessory type identification, the magnetic field strength can highlight the characteristic changes caused by different accessory materials and structures, and the magnetic field direction deflection only has unique indications at specific special-shaped accessories.

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

[0056] It should be noted that the reasons for choosing the reflection wave duration and reflection wave intensity as the influencing factors of the ultrasonic sensor abnormality index are: 1. Structural changes in the pipeline, such as changes in pipe diameter, bends, branches, and the presence of accessories, will cause changes in the reflection wave duration. For changes in pipe diameter, the reflection wave duration will change accordingly due to the different lengths of the ultrasonic propagation path. 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, which will lead to complex changes in ultrasonic reflection.

[0057] 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.

[0058] For example, .

[0059] It should be noted that the setting basis of the influencing weight factors corresponding to the reflection wave duration and reflection wave intensity in the process of the ultrasonic sensor abnormal index analysis is as follows: 1. For the reflection wave duration, it is closely related to the position and structural changes of the pipeline. The duration can accurately calculate the distance between the sensor and the pipeline, and is very sensitive to the direction of the pipeline, changes in pipe diameter, bends and branches, and can also reflect the characteristics of the medium. 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 material and roughness of the pipeline surface, the presence and type of accessories, and can evaluate propagation loss and interference. However, in comparison, it is slightly inferior in determining the key information of the pipeline location and structure, so its weight factor is relatively low.

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

[0061] It should be explained that the reason for choosing geomagnetic intensity and magnetic declination as the factors affecting the geomagnetic sensor anomaly index is that geomagnetic intensity can intuitively reflect the degree of interference of underground pipelines and their accessories on the earth's magnetic field, and is closely related to the location and shape of the pipeline. Its changes can be used to infer the location, direction and shape of the pipeline, and can more accurately distinguish geomagnetic anomalies caused by the pipeline after considering the influence of environmental factors. Magnetic declination can effectively indicate the direction of the pipeline and the location of the accessories, especially where the shape of the pipeline changes, and can assist in describing the spatial distribution characteristics of the magnetic field. Combined with geomagnetic intensity, it can more comprehensively grasp the changes in the magnetic field.

[0062] 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.

[0063] For example, .

[0064] It is necessary to explain that the setting basis of the influence weight factors corresponding to the geomagnetic intensity and magnetic declination in the analysis of the geomagnetic sensor anomaly index is: in pipeline positioning, the geomagnetic intensity is directly related to the position and can determine the approximate position of the pipeline, and the magnetic declination plays an auxiliary role, mainly used to clarify the direction and shape changes of the pipeline. For pipeline accessory type detection, the geomagnetic intensity can effectively detect the existence of accessories and preliminarily determine the type, and the magnetic declination can assist in identifying the position and shape of special shape accessories.

[0065] 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 influencing factor data between sensors, wherein the soil factor data includes soil conductivity, soil magnetic permeability and soil temperature, and the influencing factor data between sensors includes installation spacing and monitoring magnetic field strength, and analyzes the evaluation of the influencing factor data.

[0066] It should be supplemented that the soil factor data is acquired in the following manner: the soil electrical conductivity, soil magnetic permeability and soil temperature can be collected using a soil electrical conductivity sensor, a soil magnetic permeability sensor and a temperature sensor respectively.

[0067] It should be added that the method for obtaining the installation spacing is as follows: a three-dimensional scanning device is used to obtain three-dimensional scanning data of the mobile detection equipment, and then the end positions of the electromagnetic sensor, the ultrasonic sensor and the geomagnetic sensor are obtained, and the distance between the end of the electromagnetic sensor and the end of the ultrasonic sensor, the distance between the end of the electromagnetic sensor and the end of the geomagnetic sensor, and the distance between the end of the geomagnetic sensor and the end of the ultrasonic sensor are obtained, and then the installation spacing is obtained by performing an average calculation.

[0068] It should be supplemented that the monitoring magnetic field strength can be collected using a Gauss meter.

[0069] In a preferred embodiment of the present invention, the analysis of the influencing factor data evaluation situation requires the construction of an influencing factor data evaluation coefficient for the target heating pipeline in the current monitoring section, and the specific method is as follows: extract the soil conductivity, soil magnetic permeability and soil temperature of the target heating pipeline currently monitored, 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 impact evaluation coefficient of the target heating pipeline in the current monitoring section.

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

[0071] It should be added that the specific method of the analysis to obtain the soil impact evaluation coefficient of the target heating pipeline in the current monitoring section is as follows: the soil conductivity, soil magnetic permeability and soil temperature of the target heating pipeline in the current monitoring section are extracted, and then the soil conductivity deviation value, soil magnetic permeability deviation value and soil temperature deviation value in the current monitoring section are obtained by performing difference calculation with the preset reference soil conductivity, reference soil magnetic permeability and reference soil temperature, and the soil conductivity deviation degree, soil magnetic permeability deviation degree and soil temperature deviation degree in the current monitoring section are obtained by performing ratio calculation with the reference soil conductivity, reference soil magnetic permeability and reference soil temperature, and then the soil impact evaluation coefficient of the target heating pipeline in the current monitoring section is obtained by performing mean calculation.

[0072] The installation spacing and monitoring magnetic field strength of the current monitored target heating pipeline are extracted, and compared with the preset reference installation spacing and reference magnetic field strength respectively, and the inter-sensor impact evaluation coefficient of the target heating pipeline in the current monitoring section is analyzed and obtained.

[0073] It should be noted that the reasons for selecting installation spacing and monitoring magnetic field strength as factors affecting the impact evaluation coefficient between sensors are: 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 they generate or sense are prone to superposition. Smaller installation spacing may also cause cross-influence of sensor signals. 2. Monitoring magnetic field strength is a key indicator for measuring electromagnetic coupling between sensors. For electromagnetic sensors and geomagnetic sensors, the magnetic field strength determines the degree of mutual influence between them. When the magnetic field strength around the sensor is strong, it means that electromagnetic coupling is more likely to occur between them.

[0074] It should be supplemented that the specific method of analyzing and obtaining the inter-sensor impact evaluation coefficient of the target heating pipeline in the current monitoring section is as follows: extract the installation spacing and monitoring magnetic field strength of the current monitored target heating pipeline, perform difference calculation with the preset reference installation spacing and reference magnetic field strength, and then take the absolute value to obtain the installation spacing deviation and monitoring magnetic field strength deviation of the target heating pipeline in the current monitoring section, and then perform ratio calculation with the corresponding reference installation spacing and reference monitoring magnetic field strength to obtain the installation spacing deviation and monitoring magnetic field strength deviation of the target heating pipeline in the current monitoring section, and then perform mean calculation to obtain the inter-sensor impact evaluation coefficient of the target heating pipeline in the current monitoring section.

[0075] 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 influencing factor data evaluation coefficient of the target heating pipeline in the current monitoring section.

[0076] For example, the weights corresponding to the soil impact evaluation coefficient and the sensor-to-sensor impact evaluation coefficient are: .

[0077] It should be noted that the weights corresponding to the soil impact evaluation coefficient and the inter-sensor impact evaluation coefficient are set based on the following: from the perspective of contribution to pipeline detection accuracy, soil factors directly interfere with sensor signal transmission and reception due to conductivity, magnetic permeability and temperature, which is related to detection accuracy; inter-sensor impact factors 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 their weights need to be considered; inter-sensor factors can be improved by adjusting spacing and strengthening shielding, and their weights are relatively flexible.

[0078] 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.

[0079] For the preferred embodiment of the present invention, please refer to Figure 3 As shown, the specific method for determining whether there is an abnormality in the pipeline accessories is as follows: extract the monitoring data abnormality index and the influencing factor data evaluation coefficient of the target heating pipeline in the current monitoring section, perform multiplication calculation to obtain the monitoring data abnormality index correction amount of the target heating pipeline in the current monitoring section, and perform difference calculation on the monitoring data abnormality index of the target heating pipeline and the monitoring data abnormality index correction amount to obtain the corrected monitoring data abnormality index of the target heating pipeline in the current monitoring section.

[0080] 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.

[0081] Exemplarily, the monitoring data abnormality 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 must 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 under normal operating conditions, the fluctuation range of the data can be determined. For example, for the reflection wave duration monitored by the ultrasonic sensor, when there is no abnormality in the pipeline, it may fluctuate to a certain extent due to factors such as ambient temperature and slight changes in the medium.

[0083] The heating pipe accessory identification 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 the pipe accessory.

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

[0085] It should be supplemented that the cross-sectional shape of the target heating pipe in the current monitoring section is obtained by locating the center point of the target heating pipe in the current monitoring section, and using an ultrasonic sensor to perform multiple measurements on the target heating pipe based on the center point position to obtain a number of ultrasonic reflection data, and then constructing the cross-sectional shape of the target heating pipe.

[0086] It should be explained that the reasons for selecting cross-sectional area and cross-sectional perimeter as factors affecting the matching coefficient between the target heating pipe and each type of pipe accessory are: 1. In the process of identifying pipe accessory types, cross-sectional area is a key factor. Pipe accessory types with different functions often have different cross-sectional area requirements. By comparing the cross-sectional areas, these accessory types with different functions can be preliminarily distinguished. 2. For some special-shaped pipe accessory types, the cross-sectional perimeter can provide important identification clues. For example, a four-way pipe fitting has an obvious change pattern in the cross-sectional perimeter at the four branches. Unlike a tee or other simple-shaped accessories, the types of these special-shaped accessories can be accurately identified by analyzing this change in the cross-sectional perimeter.

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

[0088] The cross-sectional area and cross-sectional perimeter of the target heating pipeline are respectively compared 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.

[0089] It should be supplemented that the area matching coefficient and circumference matching coefficient of the target heating pipe cross-section and each pipe accessory type are obtained by comparing the cross-sectional area of ​​the target heating pipe with the standard cross-sectional area of ​​each pipe accessory type, and calculating the ratio of the smaller cross-sectional area to the larger cross-sectional area of ​​the cross-sectional area of ​​the target heating pipe and the standard cross-sectional area of ​​each pipe accessory type to obtain the area matching coefficient of the target heating pipe cross-section and each pipe accessory type.

[0090] It should be noted that the smaller cross-sectional area and the larger cross-sectional area are obtained by comparing the cross-sectional area of ​​the target heating pipe with the standard cross-sectional area of ​​a certain pipe accessory type. If the cross-sectional area of ​​the target heating pipe is larger than the standard cross-sectional area of ​​the pipe accessory type, the cross-sectional area of ​​the target heating pipe is recorded as the larger cross-sectional area, and the standard cross-sectional area of ​​the pipe accessory type is recorded as the smaller cross-sectional area; otherwise, the cross-sectional area of ​​the target heating pipe is recorded as the smaller cross-sectional area, and the standard cross-sectional area of ​​the pipe accessory type is recorded as the larger cross-sectional area.

[0091] For example, if the cross-sectional area of ​​the target heating pipeline is , the standard cross-sectional area of ​​a certain type of pipe accessory is , then the smaller cross-sectional area is the standard cross-sectional area of ​​the pipe accessory type , the larger cross-sectional area is the cross-sectional area of ​​the target heating pipe .

[0092] The cross-sectional circumference of the target heating pipe is compared with the standard cross-sectional circumference of each pipe accessory type, and the ratio of the smaller cross-sectional circumference of the target heating pipe and the larger cross-sectional circumference of each pipe accessory type is calculated to obtain the circumference matching coefficient corresponding to the target heating pipe cross-section and each pipe accessory type.

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

[0094] 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 weights to obtain the matching coefficient between the target heating pipeline and each pipeline accessory type.

[0095] For example, the weights corresponding to the area matching coefficient and the perimeter matching coefficient corresponding to the target heating pipe cross section and each pipe accessory type are respectively .

[0096] It should be noted that the weights corresponding to the area matching coefficient and the perimeter matching coefficient of the target heating pipe section and each pipe accessory type are set based on the following: 1. In the identification of pipe accessory types, the area matching coefficient contributes more to the recognition accuracy. Because the internal structure and function of different types of pipe accessories determine the obvious differences in the characteristics of the cross-sectional area. 2. The perimeter matching coefficient also makes an important contribution to the recognition accuracy. It plays a key role in identifying the sealing structure, connection method and special-shaped accessories of the accessories.

[0097] 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.

[0098] Exemplarily, the types of pipe accessories include but are not limited to compensators, fixed joints, reducers, tees and blind joints.

[0099] The data repository is used to store cross-sectional data corresponding to each pipe accessory type of the target heating pipe.

[0100] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall 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 device 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, wherein 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; 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 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 abnormality 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 analysis of the influencing factor data evaluation situation requires the construction of the influencing factor data evaluation coefficient of the target heating pipeline in the current monitoring section, and the specific method is as follows: 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 influencing factor data evaluation coefficient of the target heating pipeline in the current monitoring section.

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 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.

10. The direct buried heating pipeline accessories accurate geophysical detection system based on multi-sensor fusion as claimed in claim 9, 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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