Metal pipeline detection sensor calibration method and system
By acquiring the test area and interference database in the metal pipeline detection equipment, identifying sub-regions and interference items, performing detection tasks, and calculating calibration coefficients, the error problem of sensors in complex environments is solved, and the detection accuracy and adaptability are improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-23
- Publication Date
- 2026-03-31
AI Technical Summary
Existing metal pipeline detection equipment suffers from sensor errors in complex and diverse environments, affecting detection results.
By acquiring the test area, the target object, and the interference database, sub-regions and interference items are determined, the detection task is executed, detection data and standard data are acquired, calibration coefficients are calculated, and accurate calibration of the sensor is achieved.
It significantly improves detection accuracy and environmental adaptability, ensuring that the sensor is better matched in actual working scenarios and reducing the probability of false alarms and missed alarms.
Smart Images
Figure CN121763446A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of underground pipeline detection technology, specifically to a calibration method and system for a metal pipeline detection sensor. Background Technology
[0002] Metal pipelines are widely used in urban infrastructure construction such as water supply, gas, oil transportation, and communications. Their safe and stable operation is directly related to the normal operation of the city and the safety of residents' lives and property. As a key tool to ensure pipeline safety, metal pipeline detection equipment can locate and measure the depth of underground metal pipelines, providing important technical support for pipeline laying, maintenance, and emergency repair.
[0003] Calibration is a crucial step in ensuring the accuracy of metal pipeline detection equipment. Currently, after production, metal pipeline detection equipment is typically calibrated only in a single environment. However, in real-world applications, the environments in which metal pipelines operate are highly complex and diverse, leading to errors in the internal sensors of the detection equipment and affecting the actual detection results. Summary of the Invention The main objective of this invention is to provide a calibration method and system for metal pipeline detection sensors, aiming to solve the problem that the internal sensors of metal pipeline detection equipment have certain errors, which affect the actual detection effect.
[0004] The technical solution proposed in this invention is as follows: A method for calibrating a metal pipeline detection sensor, comprising: Obtain the test area, the task object, and the interference database respectively; Based on the test area and the task object, at least one sub-region is determined: Based on the interference database and the task object, at least one interference item is determined; Based on each of the described sub-regions and each of the described interferences, at least one detection task is determined; Execute each of the aforementioned detection tasks and determine the detection data for each of the aforementioned detection tasks; Obtain the standard library, and determine the standard data for each of the detection tasks based on the standard library and each of the detection tasks; Based on the detection data of each detection task and the standard data of each detection task, the calibration coefficient of each detection task is determined.
[0005] Preferably, the step of determining the calibration coefficient for each of the detection tasks based on the detection data and standard data for each of the detection tasks includes: The detection data and standard data of each detection task are used to determine the error data of each detection task. Based on the error data of each of the detection tasks, the calibration coefficient of each of the detection tasks is determined.
[0006] Preferably, the step of determining at least one sub-region based on the test area and the work object includes: Based on the target object, at least one data acquisition feature is determined; Based on the test area, determine the first standard feature of each of the sub-regions; Determine whether the collected features and the first standard features of each of the sub-regions are the same; When the acquisition feature is different from the first standard feature of any of the sub-regions, the sub-region that does not match the acquisition feature is marked as a candidate region; When the acquisition feature is the same as the first standard feature of any of the sub-regions, the sub-region with the same acquisition feature is marked as the work area; The step of determining at least one detection task based on each of the sub-regions and each of the interferences includes: Based on the described work area and each of the described interference items, at least one detection task is determined.
[0007] Preferably, the step of marking the sub-region with the same acquisition feature as the work area when the acquisition feature is the same as the first standard feature of any of the sub-regions includes: When the collected feature is the same as the first standard feature of any of the sub-regions, it is determined whether the sub-region is greater than a preset number; When the number of sub-regions is less than the preset number, the step of marking the sub-regions with the same collection features as the work area is executed. When the sub-region is greater than or equal to the preset number, obtain the weight value and preset setting of each of the collected features; Based on the weight values of each of the collected features, the sub-regions are sorted to determine the arrangement order of the sub-regions; According to the arrangement order and the preset settings, the sub-regions that conform to the preset settings and have the same collection characteristics are marked as the work areas.
[0008] Preferably, the step of sorting the sub-regions according to the weight values of each of the collected features to determine the arrangement order of the sub-regions includes: The first standard feature includes at least one label; Based on the weight values of each of the collected features, determine the first real-time score of each of the labels; The scores of each of the aforementioned tags are used to determine the second real-time scores of each of the first standard features; The arrangement order of each sub-region is determined based on the second real-time score of each of the first standard features.
[0009] Preferably, the step of determining the calibration coefficient for each of the detection tasks based on the error data of each detection task includes: Determine whether the error data of each detection task is greater than a preset value; When the error data of each of the detection tasks is less than the preset value, the interference item in each of the detection tasks is marked with the first feature code; When the error value of any test point is greater than or equal to the preset value, the calibration coefficient of each test point is determined, and the test point whose error value is greater than or equal to the preset value is marked with a second feature code. The first feature code and the second feature code of each interference item are entered into the interference database; Based on the number of times each interference item has entered the first feature code and the number of times each interference item has entered the second feature code, the interference items in the interference database are sorted.
[0010] Preferably, after the step of sorting the interference items in the interference database according to the number of times each interference item has entered the first feature code and the number of times each interference item has entered the second feature code, the method further includes: Determine whether the ratio of the number of times the first feature code of the interference item is less than a preset ratio; When the ratio of the number of times the first feature code of the interference item is less than or equal to the number of times the second feature code of the interference item is less than or equal to a preset ratio, the interference item with a ratio less than the preset ratio is categorized as a non-recommended target. When the ratio of the number of occurrences of the first feature code of the interference item to the number of occurrences of the second feature code of the interference item is greater than a preset ratio, the interference item whose ratio is less than the preset ratio is categorized as the recommended target.
[0011] Preferably, the step of determining at least one interference item based on the interference database and the task object includes: At least one interference term is determined based on the number of times the first feature code of each interference term is used.
[0012] To achieve the above objectives, the present invention also provides a metal pipeline detection sensor calibration system, which applies the metal pipeline detection sensor calibration method as described in any one of the above descriptions, and includes an execution module, a calibration module, and a detection module; The execution module is used to acquire the test area, the task object, and the interference database respectively; acquire the standard library, and determine the standard data for each of the detection tasks based on the standard library and each of the detection tasks. The detection module is used to determine at least one sub-region based on the test area and the work object; determine at least one interference item based on the interference database and the work object; determine at least one detection task based on each sub-region and each interference type; execute each detection task; and determine the detection data of each detection task. The calibration module is used to determine the calibration coefficient for each of the detection tasks based on the detection data and standard data of each of the detection tasks.
[0013] The above technical solution can achieve the following beneficial effects: Professional pre-calibration is performed based on the target to be detected (i.e. the work object), and then combined with the established test area and interference database to achieve adaptability to various complex real-world environments. Finally, the detection data, standard data, and calculated error data are integrated to determine the calibration coefficient, so that the calibrated sensor can better match the actual working scenario, significantly improving detection accuracy and environmental adaptability. Attached Figure Description
[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.
[0015] Figure 1 This is a schematic flowchart illustrating a calibration method for a metal pipeline detection sensor proposed in this invention. Detailed Implementation
[0016] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.
[0017] The following describes a calibration method and system for a metal pipeline detection sensor according to an embodiment of the present invention, with reference to the accompanying drawings.
[0018] Figure 1 This is a calibration method for a metal pipeline detection sensor.
[0019] Please see Figure 1 To achieve the above objectives, a metal pipeline detection sensor calibration method is provided in the first embodiment of the present invention, comprising: Step S10: Obtain the test area, the task object, and the interference database respectively; Step S20: Based on the test area and the task object, determine at least one sub-region: Step S30: Based on the interference database and the job object, determine at least one interference item; Step S40: Determine at least one detection task based on each sub-region and each interference. Step S50: Execute each detection task and determine the detection data for each detection task; Step S60: Obtain the standard library and determine the standard data for each testing task based on the standard library and each testing task; Step S70: Determine the calibration coefficient for each testing task based on the testing data and standard data for each testing task.
[0020] Professional pre-calibration is performed based on the target to be detected (i.e. the work object), and then combined with the established test area and interference database to achieve adaptability to various complex real-world environments. Finally, the detection data, standard data, and calculated error data are integrated to determine the calibration coefficient, so that the calibrated sensor can better match the actual working scenario, significantly improving detection accuracy and environmental adaptability.
[0021] Specifically, standard data must include at least one of the following: soil resistivity, metal pipeline length, metal pipeline depth, metal pipeline diameter, number of metal pipelines, and metal pipeline material. Test data is similar to standard data, but may require less data collection depending on the actual situation.
[0022] Specifically, the sensors in metal pipeline detection equipment are generally electromagnetic sensors.
[0023] Specifically, the testing area is a specially established pipeline inspection zone for calibrating metal pipeline detection equipment before large-scale inspections or after multiple small-scale inspections. The standard library contains relevant calculation data for each metal pipeline and soil test data from the construction of the testing area.
[0024] Specifically, the interference is at least one of the following: a strong alternating magnetic field (power frequency of 50Hz), high-frequency electromagnetic waves (800MHz to 6GHz), and a pulsed magnetic field.
[0025] In the second embodiment of the present invention, based on the first embodiment, step S70 includes: Step S71: Determine the error data for each detection task based on the detection data and standard data for each detection task. Step S72: Determine the calibration coefficient for each detection task based on the error data of each detection task.
[0026] Specifically, calibration coefficients are generated by fitting the standard data, test data, and error data using the least squares method.
[0027] It achieves accurate parameter matching under different environments, and optimizes the sensor's own signal processing algorithm parameters to form a complete closed-loop calibration process. The calibration is efficient and reliable, which can effectively improve the overall performance of metal pipeline detection equipment.
[0028] In the third embodiment of the present invention, based on the second embodiment, step S20 includes: Step S21: Determine at least one data collection feature based on the target object; Step S22: Determine the first standard feature of each sub-region based on the test area; Step S23: Determine whether the collected features and the first standard features of each sub-region are the same; Step S24: When the collected feature is different from the first standard feature of any sub-region, the sub-region that does not match the collected feature is marked as a candidate region. Step S25: When the collected feature is the same as the first standard feature of any sub-region, the sub-region with the same collected feature is marked as the work area; Step S40 includes: Step S41: Determine at least one detection task based on the work area and each of the interference items.
[0029] By collecting data on the target object (such as soil type) and performing calibration in appropriate sub-regions, rapid calibration can be achieved while ensuring accuracy.
[0030] By quickly matching sub-regions and marking them as work areas to establish detection tasks, the optimal combination of calibration test parameters is selected to ensure that the sensor can be in the best working state in the environment where the work object is located. This enables the sensor to receive clearer and more stable electromagnetic signals, further reducing detection errors and the probability of false alarms and missed alarms.
[0031] In the fourth embodiment of the present invention, based on the third embodiment, step S24 includes: Step S26: When the collected feature is the same as the first standard feature of any sub-region, determine whether the sub-region is greater than the preset number; Step S27: When the number of sub-regions is less than the preset number, mark the sub-regions with the same collection features as the work area. Step S28: When the number of sub-regions is greater than or equal to the preset number, obtain the weight value and preset setting of each collected feature; Step S29: Sort the sub-regions according to the weight values of each collected feature to determine the order of the sub-regions; Step S210: According to the arrangement order and preset settings, the sub-regions that meet the preset settings and have the same collection features are marked as the work area.
[0032] Sub-regions are sorted by weight values, and then a suitable sub-region standard is selected as the working area through preset settings. This reduces the amount of industrial calibration required while ensuring the effectiveness of the calibration.
[0033] In the fifth embodiment of the present invention, based on the fourth embodiment, step S29 includes: Step S211, the first standard feature includes at least one label; Step S212: Determine the first real-time score of each label based on the weight values of each collected feature; Step S213: Determine the second real-time score for each first standard feature based on the score of each label. Step S214: Determine the arrangement order of each sub-region based on the second real-time score of each first standard feature.
[0034] By calculating the second real-time score after adding up all the labels within the standard characteristics, the sub-regions are arranged in order so that staff can quickly select the most suitable sub-region for calibration.
[0035] In the sixth embodiment of the present invention, based on any one of the second to fifth embodiments, step S72 includes: Step S73: Determine whether the error data of each detection task is greater than the preset value; Step S74: When the error data of each detection task is less than the preset value, mark the interference item in each detection task with the first feature code. Step S75: When the error value of any test point is greater than or equal to the preset value, determine the calibration coefficient of each test point, and mark the test points with error values greater than or equal to the preset value with the second feature code. Step S76: Enter the first feature code and the second feature code of each interference item into the interference database; Step S77: Sort the interference items in the interference database according to the number of times each interference item enters the first feature code and the number of times each interference item enters the second feature code.
[0036] After each test, each interference item is labeled according to its actual condition. The interference items are then sorted according to the frequency of the first and second signatures. This allows staff to quickly select interference items with a higher frequency of the second signature for testing, enabling efficient calibration based on the actual situation.
[0037] Specifically, after step S77, the following steps are included: Step S78: Create at least one target group in the interference database, set a second standard feature for each target group, and store at least one interference item in each target group; Step S79: After determining the calibration coefficients for each detection task, the third feature code is labeled according to the interference items in each detection task, and the number of times the first feature code and the number of times the third feature code are displayed in the target group.
[0038] By further subdividing the interference database, interference items that are prone to affecting sensor errors are moved to the relevant target groups according to the type of each target group. The number of times the third feature code is marked is used to show the number of times the interference item is triggered in a single target group, while the first feature code shows the sum of the number of times the interference item is triggered in all target groups.
[0039] In the seventh embodiment of the present invention, based on the sixth embodiment, after step S77, the following is included: Step S710: Determine whether the ratio of the number of times the first feature code of the interference item is less than a preset ratio. Step S711: When the ratio of the number of times the first feature code of the interference item is less than or equal to the number of times the second feature code of the interference item is less than or equal to a preset ratio, the interference item with a ratio less than the preset ratio is categorized as a non-recommended target. Step S712: When the ratio of the number of times the first feature code of an interference item is greater than a preset ratio, the interference item whose ratio is less than the preset ratio is categorized as the recommended target.
[0040] By recommending relevant distractors based on different ratios, the recommendation efficiency can be effectively improved.
[0041] In the eighth embodiment of the present invention, based on the sixth embodiment, step S30 includes: Step S31: Determine at least one interference term based on the frequency of the first feature code of each interference term.
[0042] To achieve the above objectives, the present invention also discloses a metal pipeline detection sensor calibration system, which applies a metal pipeline detection sensor calibration method as described in any of the above claims, including an execution module, a calibration module, and a detection module; The execution module is used to acquire the test area, the task object, and the interference database; acquire the standard library, and determine the standard data for each test task based on the standard library and each test task. The detection module is used to determine at least one sub-region based on the test area and the work object; determine at least one interference item based on the interference database and the work object; determine at least one detection task based on each sub-region and each interference; execute each detection task and determine the detection data of each detection task. The calibration module is used to determine the calibration coefficients for each testing task based on the testing data and standard data for each testing task.
[0043] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0044] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.
Claims
1. A method for calibrating a metal pipeline detection sensor, characterized in that, include: Obtain the test area, the task object, and the interference database respectively; Based on the test area and the task object, at least one sub-region is determined: Based on the interference database and the task object, at least one interference item is determined; Based on each of the described sub-regions and each of the described interferences, at least one detection task is determined; Execute each of the aforementioned detection tasks and determine the detection data for each of the aforementioned detection tasks; Obtain the standard library, and determine the standard data for each of the detection tasks based on the standard library and each of the detection tasks; Based on the detection data of each detection task and the standard data of each detection task, the calibration coefficient of each detection task is determined.
2. The calibration method for a metal pipeline detection sensor according to claim 1, characterized in that, The step of determining the calibration coefficient for each of the detection tasks based on the detection data and standard data for each of the detection tasks includes: The detection data and standard data of each detection task are used to determine the error data of each detection task. Based on the error data of each of the detection tasks, the calibration coefficient of each of the detection tasks is determined.
3. The calibration method for a metal pipeline detection sensor according to claim 2, characterized in that, The step of determining at least one sub-region based on the test area and the work object includes: Based on the target object, at least one data acquisition feature is determined; Based on the test area, determine the first standard feature of each of the sub-regions; Determine whether the collected features and the first standard features of each of the sub-regions are the same; When the acquisition feature is different from the first standard feature of any of the sub-regions, the sub-region that does not match the acquisition feature is marked as a candidate region; When the acquisition feature is the same as the first standard feature of any of the sub-regions, the sub-region with the same acquisition feature is marked as the work area; The step of determining at least one detection task based on each of the sub-regions and each of the interferences includes: Based on the described work area and each of the described interference items, at least one detection task is determined.
4. The calibration method for a metal pipeline detection sensor according to claim 3, characterized in that, The step of marking the sub-region with the same acquisition feature as the work area when the acquisition feature is the same as the first standard feature of any sub-region includes: When the collected feature is the same as the first standard feature of any of the sub-regions, it is determined whether the sub-region is greater than a preset number; When the number of sub-regions is less than the preset number, the step of marking the sub-regions with the same collection features as the work area is executed. When the sub-region is greater than or equal to the preset number, obtain the weight value and preset setting of each of the collected features; Based on the weight values of each of the collected features, the sub-regions are sorted to determine the arrangement order of the sub-regions; According to the arrangement order and the preset settings, the sub-regions that conform to the preset settings and have the same collection characteristics are marked as the work areas.
5. The calibration method for a metal pipeline detection sensor according to claim 4, characterized in that, The step of sorting the sub-regions according to the weight values of each of the collected features and determining the arrangement order of the sub-regions includes: The first standard feature includes at least one label; Based on the weight values of each of the collected features, determine the first real-time score of each of the labels; The scores of each of the aforementioned tags are used to determine the second real-time scores of each of the first standard features; The arrangement order of each sub-region is determined based on the second real-time score of each of the first standard features.
6. A calibration method for a metal pipeline detection sensor according to any one of claims 2-5, characterized in that, The step of determining the calibration coefficient for each detection task based on the error data of each detection task includes: Determine whether the error data of each detection task is greater than a preset value; When the error data of each of the detection tasks is less than the preset value, the interference item in each of the detection tasks is marked with the first feature code; When the error value of any test point is greater than or equal to the preset value, the calibration coefficient of each test point is determined, and the test point whose error value is greater than or equal to the preset value is marked with a second feature code. The first feature code and the second feature code of each interference item are entered into the interference database; Based on the number of times each interference item has entered the first feature code and the number of times each interference item has entered the second feature code, the interference items in the interference database are sorted.
7. The calibration method for a metal pipeline detection sensor according to claim 6, characterized in that, After the step of sorting the interference items in the interference database according to the number of times each interference item has entered the first feature code and the number of times each interference item has entered the second feature code, the method includes: Determine whether the ratio of the number of times the first feature code of the interference item is less than a preset ratio; When the ratio of the number of times the first feature code of the interference item is less than or equal to the number of times the second feature code of the interference item is less than or equal to a preset ratio, the interference item with a ratio less than the preset ratio is categorized as a non-recommended target. When the ratio of the number of occurrences of the first feature code of the interference item to the number of occurrences of the second feature code of the interference item is greater than a preset ratio, the interference item whose ratio is less than the preset ratio is categorized as the recommended target.
8. The calibration method for a metal pipeline detection sensor according to claim 6, characterized in that, The step of determining at least one interference item based on the interference database and the job object includes: At least one interference term is determined based on the number of times the first feature code of each interference term is used.
9. A calibration system for a metal pipeline detection sensor, characterized in that, The calibration method for a metal pipeline detection sensor as described in any one of claims 1-8 includes an execution module, a calibration module, and a detection module; The execution module is used to acquire the test area, the task object, and the interference database respectively; acquire the standard library, and determine the standard data for each of the detection tasks based on the standard library and each of the detection tasks. The detection module is used to determine at least one sub-region based on the test area and the work object; determine at least one interference item based on the interference database and the work object; determine at least one detection task based on each sub-region and each interference type; execute each detection task; and determine the detection data of each detection task. The calibration module is used to determine the calibration coefficient for each of the detection tasks based on the detection data and standard data of each of the detection tasks.