Pipe network multi-robot cooperative fault positioning method, system and device and storage medium

Abstract

The invention provides a pipe network multi-robot cooperative fault positioning method, system and device and a storage medium, and relates to the technical field of intelligent detection, and the method comprises the steps: obtaining pipe network state data sent by a plurality of robots deployed in a pipe network, and determining a fault region corresponding to abnormal data when the abnormal data exists in the pipe network state data; the first robot is controlled to inject a detection signal into the pipe network, and a first signal sent by the second robot after receiving the detection signal and a second signal sent by the third robot after receiving the detection signal are obtained; determining an initial fault position in the fault area by combining the first signal and the second signal; obtaining physical characteristic parameters of the pipe network and medium parameters of a transportation medium in the pipe network; and on the basis of the initial fault position, a target fault position is determined in combination with the physical characteristic parameters and the medium parameters. The method has the technical effect that the fault positioning precision in the complex pipe network environment is improved.

Description

Technical Field

[0001] This application relates to the field of intelligent detection technology, specifically to a method, system, device, and storage medium for multi-robot collaborative fault location in pipeline networks. Background Technology

[0002] As cities continue to expand and underground pipe networks become increasingly complex, the timely detection and accurate location of pipe network faults have become crucial for ensuring the safe operation of urban infrastructure. Especially in complex pipe network environments, the ability to quickly and accurately pinpoint fault locations is of great significance for reducing maintenance costs and improving maintenance efficiency.

[0003] Currently, the mainstream methods for locating pipeline faults mainly rely on deploying fixed detection equipment within the pipeline network. The fault location is determined by the pipeline status data transmitted by the detection equipment. While this method can achieve basic fault monitoring, the limited coverage of fixed detection equipment results in insufficient global monitoring capabilities for complex pipeline networks. When multiple faults occur in the pipeline network or the fault area is large, the fixed detection equipment struggles to provide sufficient information, leading to low accuracy in fault location. Summary of the Invention

[0004] This application provides a multi-robot collaborative fault location method, system, device, and storage medium for pipeline networks, which can improve the accuracy of fault location in complex pipeline network environments.

[0005] In a first aspect, this application provides a multi-robot collaborative fault location method for pipeline networks. The method includes: acquiring pipeline network status data sent by multiple robots deployed in the pipeline network; when abnormal data exists in the pipeline network status data, determining the fault area corresponding to the abnormal data; controlling a first robot within the fault area to inject a detection signal into the pipeline network; acquiring a first signal sent by a second robot within a first preset range of the fault area after receiving the detection signal, and a second signal sent by a third robot within a second preset range of the fault area after receiving the detection signal, wherein the first preset range is smaller than the second preset range; combining the first signal and the second signal to determine an initial fault location within the fault area; acquiring physical characteristic parameters of the pipeline network and medium parameters of the transport medium within the pipeline network; and, based on the initial fault location, combining the physical characteristic parameters and the medium parameters to determine a target fault location.

[0006] By employing the aforementioned technical solution and utilizing multiple robots for real-time monitoring of the pipeline network, anomalies can be quickly detected and fault areas identified, improving the timeliness of fault detection. Secondly, by injecting detection signals into the fault area using a first robot, and receiving these signals from second and third robots at different preset ranges, signal characteristics under different distance conditions are obtained. This multi-point collaborative detection method effectively reduces environmental noise interference. By combining the first and second signals for analysis, the initial fault location can be determined more accurately. Finally, by introducing pipeline network physical characteristic parameters and medium parameters to correct the initial fault location, the influence of the actual pipeline network environment on signal propagation is considered, improving the accuracy of fault location in complex pipeline network environments.

[0007] Optionally, when abnormal data exists in the pipeline status data, determining the fault area corresponding to the abnormal data includes: performing spatial clustering analysis on the robots with abnormal data to form at least one abnormal data cluster; calculating the influence radiation range of each abnormal data cluster by combining the pipeline topology and the spatial distribution characteristics of the abnormal data clusters; merging the overlapping influence radiation ranges to generate a fault area boundary that includes the spatial correlation of abnormal data; and outputting the fault area corresponding to the abnormal data according to the mapping relationship between the fault area boundary and the pipeline partition.

[0008] By employing the aforementioned technical solution, and through calculating and merging the impact range of abnormal data clusters, the spatial correlation and continuity characteristics of pipeline network faults are fully considered, resulting in more accurate fault area delineation. Analysis combined with the pipeline network topology ensures that the delineation of fault area boundaries conforms to the actual pipeline network structure, and precise fault area location is achieved through mapping relationships with pipeline network partitions. This method, based on data clustering and spatial correlation analysis, not only improves the accuracy of fault area delineation but also reduces the probability of false alarms and missed alarms, providing a reliable area range for subsequent precise location.

[0009] Optionally, determining the initial fault location within the fault area by combining the first signal and the second signal includes: determining a first attenuation characteristic of the detection signal within a first preset range based on the first signal; determining a second attenuation characteristic of the detection signal within a second preset range based on the second signal; and determining the initial fault location within the fault area by combining the first attenuation characteristic and the second attenuation characteristic.

[0010] By employing the above technical solution, accurate attenuation data for closer distances can be obtained by acquiring the first attenuation feature within a first preset range; simultaneously, by combining it with the second attenuation feature within a second preset range, the signal propagation pattern over a wider range can be obtained. This dual-layer attenuation feature analysis method not only ensures the accuracy of close-range positioning but also provides auxiliary verification using long-range signal features. By combining the complementary advantages of the two attenuation features, the accuracy of initial fault location is significantly improved, effectively overcoming the positioning deviation that may be caused by single-range detection.

[0011] Optionally, determining the initial fault location within the fault area by combining the first attenuation feature and the second attenuation feature includes: comparing the first attenuation feature with the second attenuation feature, and determining the propagation law of the detection signal within the fault area based on the comparison result; calculating the spatial attenuation gradient of the detection signal within the fault area based on the propagation law; determining attenuation anomalies based on the spatial attenuation gradient, and using the attenuation anomalies as the initial fault location within the fault area.

[0012] By adopting the above technical solution and calculating the spatial attenuation gradient based on propagation laws, the changing trend of signal strength in space can be accurately reflected, making fault location more scientifically based. The method of determining the initial fault location by identifying attenuation anomalies fully utilizes the local disturbance characteristics caused by the fault point on signal propagation, avoiding the limitations of traditional single-threshold judgment methods and improving the accuracy and reliability of fault location.

[0013] Optionally, determining the target fault location based on the initial fault location, combined with the physical characteristic parameters and the medium parameters, includes: establishing a correlation between the physical characteristic parameters, the medium parameters, and the fault location offset; the physical characteristic parameters include at least one of pipe material, pipe diameter, pipe wall thickness, pipe connection method, and pipe age; the medium parameters include at least one of medium flow rate, medium pressure, medium temperature, and medium composition; calculating a correction value for the initial fault location based on the correlation; and correcting the initial fault location based on the correction value to obtain the target fault location.

[0014] By adopting the above technical solution, and establishing the correlation between physical characteristic parameters, medium parameters, and fault location offset, the influence of physical characteristics such as pipeline material, size, and connection method, as well as operating parameters such as medium flow rate, pressure, and temperature, on fault location is systematically considered. Based on this correlation, correction values ​​are calculated and the initial fault location is corrected, effectively compensating for the systematic deviation caused by the actual operating environment of the pipeline network on fault location. By quantifying and comprehensively correcting the influence of multiple parameters, the positioning accuracy of the target fault location is significantly improved, making the final positioning result more consistent with the actual situation and providing more accurate location information for fault repair.

[0015] Optionally, establishing the correlation between the physical characteristic parameters, the medium parameters, and the fault location offset includes: normalizing the physical characteristic parameters and the medium parameters to obtain a standardized parameter set; acquiring fault data of the pipeline network within a preset time period; determining the influence weight of each standardized parameter in the standardized parameter set on the fault location offset based on the fault data; constructing a parameter weighted combination function based on the influence weight; calculating the fault location offset under different parameter combinations using the parameter weighted combination function, and establishing the correlation between the physical characteristic parameters, the medium parameters, and the fault location offset.

[0016] By adopting the above technical solution and analyzing historical fault data within a preset time period to determine the influence weights, a quantitative assessment of the degree of influence of each parameter on the fault location offset was achieved. Based on the influence weights, a parameter weighted combination function was constructed, and the location offset under different parameter combinations was calculated, establishing a complete correlation mapping. This data-driven modeling method not only considers the influence of individual parameters but also reflects the interaction between parameters, making the fault location correction more scientific and accurate.

[0017] Optionally, after determining the target fault location, the method further includes: controlling a fourth robot closest to the target fault location to move to the target fault location; controlling the fourth robot to accurately detect the target fault location and obtain detailed fault information; determining the fault type and fault severity based on the detailed fault information; and generating a fault handling plan based on the fault type and fault severity.

[0018] By adopting the above technical solution, and combining the detailed fault information obtained by the fourth robot with the analysis of fault type and severity, a comprehensive and accurate assessment of the fault situation can be achieved. The mechanism for automatically generating fault handling solutions based on the assessment results ensures the relevance and operability of the solutions. This closed-loop processing flow from fault location to solution generation not only improves the accuracy of fault diagnosis but also realizes intelligent fault handling, providing efficient technical support for pipeline maintenance.

[0019] Secondly, this application provides a multi-robot collaborative fault location system for pipeline networks, the system comprising: a first acquisition module, a control module, a combination module, a second acquisition module, and a determination module; wherein, The first acquisition module is used to acquire pipeline status data sent by multiple robots deployed in the pipeline network, and when abnormal data exists in the pipeline status data, to determine the fault area corresponding to the abnormal data; the control module is used to control a first robot in the fault area to inject a detection signal into the pipeline network, acquire a first signal sent by a second robot within a first preset range of the fault area after receiving the detection signal, and a second signal sent by a third robot within a second preset range of the fault area after receiving the detection signal, wherein the first preset range is smaller than the second preset range; the combination module is used to combine the first signal and the second signal to determine an initial fault location in the fault area; the second acquisition module is used to acquire physical characteristic parameters of the pipeline network and medium parameters of the transport medium in the pipeline network; the determination module is used to determine a target fault location based on the initial fault location, combined with the physical characteristic parameters and the medium parameters.

[0020] Thirdly, this application provides an electronic device that adopts the following technical solution: it includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a computer program such as any of the above-described pipeline multi-robot collaborative fault location methods.

[0021] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution: storing a computer program that can be loaded by a processor and executed any of the above-mentioned pipeline multi-robot collaborative fault location methods.

[0022] In summary, this application includes at least one of the following beneficial technical effects: Utilizing multiple robots for real-time monitoring of the pipeline network enables rapid detection of anomalies and identification of fault areas, improving the timeliness of fault discovery. Secondly, by injecting detection signals into the fault area using a first robot, and receiving these signals from second and third robots at different preset ranges, signal characteristics under varying distance conditions are obtained. This multi-point collaborative detection method effectively reduces environmental noise interference. Combining the first and second signals for analysis allows for a more accurate determination of the initial fault location. Finally, by incorporating pipeline network physical characteristic parameters and medium parameters to correct the initial fault location, the influence of the actual pipeline environment on signal propagation is considered, improving the accuracy of fault location in complex pipeline network environments. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating a multi-robot collaborative fault location method for pipeline networks provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a multi-robot collaborative fault location system for pipeline networks provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0024] Explanation of reference numerals in the attached figures: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. Detailed Implementation

[0025] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0026] In the description of the embodiments in this application, words such as "illustrative," "for example," or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "illustrative," "for example," or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of words such as "illustrative," "for example," or "for example" is intended to present the relevant concepts in a specific manner.

[0027] Figure 1 This is a flowchart illustrating a multi-robot collaborative fault location method for pipeline networks provided in an embodiment of this application. Figure 1 As shown, the method includes S101-S105: S101: Obtain pipeline status data sent by multiple robots deployed in the pipeline network. When there is abnormal data in the pipeline status data, determine the fault area corresponding to the abnormal data.

[0028] To monitor the pipeline network's operational status in real time and promptly detect potential faults, this invention deploys multiple robots within the network for status monitoring. These robots can move and inspect the pipeline network, collecting operational parameters such as pressure, flow rate, and temperature. Each robot is equipped with a corresponding sensor module, enabling it to perceive the pipeline network's status at its location in real time and transmit the collected data to the control center via a wireless communication module.

[0029] When the control center receives pipeline status data sent by the robots, it first compares the data with pre-set normal parameter ranges. If the data collected by one or more robots exceeds the normal range, it is marked as abnormal data. To accurately define the fault area, the system performs spatial clustering analysis on the robots reporting abnormal data. Specifically, robots that are spatially close and have similar abnormal characteristics are grouped together to form an abnormal data cluster. The spatial distance threshold can be adaptively adjusted according to the pipeline scale, and the similarity characteristics are determined based on the type and degree of deviation of the abnormal data.

[0030] After obtaining the abnormal data clusters, the system calculates the impact range of each cluster based on the pipeline network topology. The impact range refers to the pipeline area that the abnormal state may spread to or affect; its calculation needs to consider factors such as pipeline connectivity, media flow direction, and the degree of abnormality. When the impact ranges of multiple clusters overlap, the system merges them to generate fault area boundaries that include the spatial correlation of the abnormal data. Finally, based on the mapping relationship between the fault area boundaries and pipeline partitions, the specific fault area corresponding to the abnormal data is determined.

[0031] Based on the above embodiments, as an optional implementation, in S101, when there is abnormal data in the pipeline status data, determining the fault area corresponding to the abnormal data specifically includes S11-S14: S11, Perform spatial clustering analysis on robots with abnormal data to form at least one abnormal data cluster.

[0032] When the system detects abnormal data, it first performs spatial clustering analysis on the robots that reported the abnormal data. By calculating the spatial distance between robots and the similarity of the abnormal data, a density clustering algorithm is used to group robots that are spatially close and have similar abnormal characteristics into one category, forming at least one abnormal data cluster. This clustering method can effectively identify the spatial distribution pattern of abnormal data, providing a basis for subsequent fault area determination. For example, when a pipeline leaks, multiple robots near that location may simultaneously detect abnormal pressure; clustering analysis can group these robots into the same abnormal data cluster.

[0033] S12, combining the pipeline network topology and the spatial distribution characteristics of abnormal data clusters, calculate the influence range of each abnormal data cluster.

[0034] After obtaining the abnormal data clusters, the system combines the pipeline network topology and the spatial distribution characteristics of the abnormal data clusters to calculate the impact range of each cluster. The impact range refers to the pipeline area that the abnormal state may affect, and its calculation needs to consider multiple factors such as pipeline connectivity, medium flow direction, and the degree of abnormality. For example, for pressure anomalies, the system will calculate the possible propagation range of pressure fluctuations based on the pipeline connectivity and medium flow direction; for water quality anomalies, the possible paths of pollutant diffusion need to be considered.

[0035] S13 merges the overlapping influence ranges to generate fault area boundaries that include spatial correlations of abnormal data.

[0036] Due to the complexity of pipeline systems, the impact ranges of different anomaly data clusters may overlap. The system merges these overlapping impact ranges to generate a fault area boundary that includes the spatial correlations of the anomaly data. This merging process avoids redundant calculations and also reflects the potential correlations between multiple anomaly points. For example, when anomalies are detected both upstream and downstream, the merged fault area boundary can include the pipe segment between the two anomaly points.

[0037] S14, based on the mapping relationship between the fault area boundary and the pipeline network partition, output the fault area corresponding to the abnormal data.

[0038] Finally, based on the generated fault area boundaries and the pre-defined pipeline network partition information, the system determines the specific fault area corresponding to the abnormal data. Pipeline network partitions are typically pre-defined based on factors such as physical barriers and valve distribution within the pipeline network, and have clearly defined management boundaries. By mapping the fault area boundaries to the pipeline network partitions, the system can output fault area information that is easy to manage and handle.

[0039] S102, control the first robot in the fault area to inject a detection signal into the pipeline, and obtain the first signal sent by the second robot after receiving the detection signal within the first preset range of the fault area, and the second signal sent by the third robot after receiving the detection signal within the second preset range of the fault area, wherein the first preset range is smaller than the second preset range.

[0040] After identifying the fault area, active detection needs to be carried out within the fault area to further narrow down the fault location range and improve the location accuracy. This invention employs a multi-robot collaborative detection method based on signal attenuation characteristics, which locates the fault point by analyzing the signal attenuation within different distance ranges.

[0041] Specifically, the system first selects a suitable first robot within the fault area as the signal transmission source. The first robot is equipped with a dedicated signal generator capable of injecting a detection signal of a specific frequency into the pipeline network. This detection signal can be an acoustic signal, an electromagnetic signal, or a pressure wave signal, and its frequency and intensity are optimized to ensure effective propagation within the pipeline network without interfering with its normal operation.

[0042] While injecting the detection signal, the system coordinates other robots within the fault area to receive the signal. These receiving robots are divided into two categories based on their distance from the signal source: a second robot located within a first preset range and a third robot located within a second preset range. The first preset range is typically set to within 30% of the fault area radius, while the second preset range extends to within 60% of the fault area radius. This layered detection layout can better capture the attenuation characteristics of the signal during propagation.

[0043] Both the second and third robots are equipped with corresponding signal receiving modules, capable of accurately measuring the strength, phase, and other characteristic parameters of the received detection signal. Upon receiving the detection signal, these robots generate a first signal and a second signal, respectively, containing signal characteristic information, and transmit them to the control center. The first signal reflects the attenuation characteristics of the detection signal during short-distance propagation, while the second signal reflects its propagation characteristics over relatively long distances.

[0044] S103, combining the first signal and the second signal, determine the initial fault location within the fault area.

[0045] First, the system analyzes the first signal sent by the second robot and extracts the first attenuation characteristics of the detected signal within a first preset range. The first attenuation characteristics include parameters such as the attenuation rate of signal strength with distance and signal phase change. Since the first preset range is small, the signal propagation within this range is relatively less affected by the pipeline environment, so the first attenuation characteristics can better reflect the local abnormal characteristics near the fault point.

[0046] Simultaneously, the system analyzes the second signal sent by the third robot and extracts the second attenuation characteristic of the detected signal within a second preset range. This second attenuation characteristic reflects the propagation pattern of the detected signal over a large range. Because the second preset range is large, this characteristic can reflect the overall impact pattern of the fault on signal propagation.

[0047] By comparing the first and second attenuation characteristics, the system can identify abnormal propagation patterns of the detection signal within the fault area. Under normal circumstances, signal strength should attenuate systematically with increasing propagation distance. However, near the fault point, changes in pipeline structure or medium characteristics can cause abnormal signal attenuation. By analyzing changes in the attenuation pattern, the system calculates the spatial variation trend of the detection signal within the fault area, thereby identifying the locations of abnormal changes in signal propagation.

[0048] Based on the calculated spatial attenuation gradient, the system identifies attenuation anomalies, i.e., locations where the signal attenuation characteristics change abruptly. These anomalies are typically characterized by gradient values ​​significantly deviating from the normal range or exhibiting jumps that do not conform to propagation laws. The system determines the most significant attenuation anomaly as the initial fault location, providing an important reference for subsequent precise localization.

[0049] Based on the above embodiments, as an optional implementation, in S103, determining the initial fault location within the fault area by combining the first signal and the second signal specifically includes S31-S33: S31, based on the first signal, determine the first attenuation characteristic of the detection signal within the first preset range.

[0050] After acquiring detection signals within different ranges, in order to accurately determine the initial fault location, this invention employs a dual-layer attenuation feature analysis method. By comparing the signal attenuation patterns within different ranges, the fault point is located, thereby improving the accuracy of the location.

[0051] The system first processes the first signal sent by the second robot, analyzes the propagation characteristics of the detection signal within a first preset range, and extracts the first attenuation feature. The first attenuation feature mainly includes parameters such as the spatial attenuation rate of signal strength and signal phase change. Since the first preset range is relatively small, typically within 30% of the fault area radius, signal propagation within this range is less affected by the external environment and can more directly reflect the abnormal characteristics near the fault point. For example, when the detection signal passes through the fault point, a sudden attenuation or phase jump may occur; these features can serve as important evidence for fault location.

[0052] S32, based on the second signal, determine the second attenuation characteristic of the detection signal within the second preset range.

[0053] Simultaneously, the system analyzes the second signal sent by the third robot and extracts the second attenuation characteristics within a second preset range. This second preset range is typically within 60% of the fault area radius; the signal propagation characteristics within this range reflect the impact of the fault on the surrounding area. Compared to the first preset range, the signal attenuation within the second preset range better reflects the overall impact pattern of the fault. By analyzing the second attenuation characteristics, the propagation pattern of the detection signal over a larger area can be understood, which helps to eliminate interference caused by local environmental factors.

[0054] S33, combining the first attenuation feature and the second attenuation feature, determine the initial fault location within the fault area.

[0055] After obtaining the attenuation characteristics within two ranges, the system determines the initial fault location within the fault area through comparative analysis. Under normal circumstances, the propagation of the detection signal in the pipeline network should follow a certain attenuation pattern. When a fault occurs, the signal will exhibit obvious abnormal characteristics near the fault point. By comparing the differences between the first and second attenuation characteristics, the system can more accurately identify the true fault location. For example, if both drastic attenuation at close range and abnormal propagation patterns at long range are observed simultaneously at a certain location, then this location is very likely the fault location.

[0056] Based on the above embodiments, as an optional implementation, in S33, determining the initial fault location within the fault area by combining the first attenuation feature and the second attenuation feature specifically includes S331-S333: S331, compare the first attenuation feature with the second attenuation feature, and determine the propagation law of the detection signal in the fault area based on the comparison result.

[0057] First, the system performs a detailed comparison of the first and second attenuation characteristics. This comparison primarily focuses on the differences in key parameters such as the rate of change of signal strength and phase shift within two preset ranges. By analyzing these differences, the system can identify the propagation pattern of the detected signal within the fault area. For example, in a normal pipe section, the signal strength typically exhibits a regular attenuation with propagation distance; however, near the fault point, phenomena such as abrupt changes in the attenuation rate and abnormal phase jumps may occur. By comparing the attenuation characteristics at close and long distances, the system can eliminate interference from factors such as changes in the local structure of the pipeline network and determine the true signal propagation pattern.

[0058] S332, based on the propagation law, calculates the spatial attenuation gradient of the detection signal within the fault area.

[0059] Based on established propagation laws, the system calculates the spatial attenuation gradient of the detected signal within the fault region. The spatial attenuation gradient reflects the rate of change of signal strength in space and is an important indicator describing signal propagation characteristics. During the calculation, the system considers the attenuation changes of the signal in different directions, forming an attenuation gradient distribution map within the fault region. Under normal circumstances, the spatial attenuation gradient should exhibit a relatively uniform distribution; however, near the fault point, due to changes in the pipe structure or medium properties, significant changes in the gradient value may occur.

[0060] S333, based on the spatial attenuation gradient, determine the attenuation anomaly point and use the attenuation anomaly point as the initial fault location within the fault area.

[0061] By analyzing the distribution characteristics of the spatial attenuation gradient, the system can identify attenuation anomalies. Attenuation anomalies typically manifest as locations where the gradient value deviates significantly from the normal range; these locations often exhibit a high spatial correlation with the actual fault point. The system identifies the most significant attenuation anomaly as the initial fault location within the fault area. For example, when the attenuation gradient value at a certain point far exceeds the average level of the surrounding area, or when a significant gradient abrupt change occurs, that location is very likely the fault location.

[0062] S104, obtain the physical characteristic parameters of the pipeline network and the medium parameters of the transported medium within the pipeline network.

[0063] To improve the accuracy of fault location, after determining the initial fault location, it is necessary to consider the characteristics of the pipeline network itself and the influence of the transport medium on signal propagation. This invention provides necessary data support for subsequent location correction by acquiring the physical characteristic parameters of the pipeline network and the medium parameters of the transport medium.

[0064] The system first retrieves the physical characteristic parameters of the pipeline network from the pipeline network information database. These parameters include information such as pipe material, pipe diameter, pipe wall thickness, pipe connection method, and pipe age. Different pipe materials affect signal propagation characteristics; for example, metal pipes and plastic pipes have significantly different levels of signal attenuation. Pipe diameter and wall thickness affect the signal propagation path and reflection characteristics. Pipe connection methods (such as flange connections, welding, etc.) will cause signal attenuation or reflection at the connection points. The pipe age reflects the degree of aging of the pipeline, which will affect signal propagation loss.

[0065] Meanwhile, the system collects real-time data on the transport medium's parameters, including flow rate, pressure, temperature, and composition, using sensors deployed throughout the pipeline network. Flow rate affects the speed and direction of signal propagation; changes in pressure cause signal attenuation; temperature affects propagation characteristics; and composition determines energy loss during propagation. The real-time nature of these parameters is crucial for accurately locating faults.

[0066] The purpose of acquiring these parameters is to eliminate interference from the physical environment and media conditions on signal propagation and improve the accuracy of fault location. Due to the complexity of the pipeline network operating environment, these parameters often change over time and space. Therefore, the system needs to continuously update these parameter data to ensure the accuracy of subsequent fault location corrections.

[0067] S105. Based on the initial fault location, the target fault location is determined by combining physical characteristic parameters and medium parameters.

[0068] To eliminate the influence of the pipeline network's physical environment and media conditions on fault location, this invention, based on the initial fault location, analyzes the influence of physical characteristic parameters and media parameters on the fault location and performs location correction to determine a more accurate target fault location.

[0069] The system first normalizes the acquired physical characteristic parameters and medium parameters to obtain a standardized parameter set. Normalization eliminates dimensional differences between parameters, making them comparable. By analyzing historical pipeline fault data, the system extracts the deviation data between the actual fault location and the initial location under different parameter conditions. Based on this historical data, the system calculates the influence weight of each standardized parameter on the fault location offset. For example, when pipelines are old, corrosion and structural aging often alter signal propagation characteristics, affecting positioning accuracy; higher medium flow velocities significantly influence signal propagation direction.

[0070] To accurately describe the combined impact of various parameters on the fault location, the system constructs a parameter weighted combination function. This function uses standardized parameters as independent variables and influence weights as coefficients to calculate the location offset under the current parameter combination conditions. This weighted combination method can reflect the interaction between different parameters, making the correction results more consistent with the actual situation.

[0071] The system substitutes the currently acquired physical characteristic parameters and medium parameters into a parameter weighting combination function to calculate a correction value for the initial fault location. This correction value reflects the location offset under the current pipeline network environment. By applying the correction value to the initial fault location, the system ultimately determines the target fault location.

[0072] Based on the above embodiments, as an optional implementation, in S105, determining the target fault location based on the initial fault location, combined with physical characteristic parameters and medium parameters, specifically includes S51-S53: S51, establish the correlation between physical characteristic parameters and medium parameters and fault location offset; physical characteristic parameters include at least one of pipe material, pipe diameter, pipe wall thickness, pipe connection method and pipe age; medium parameters include at least one of medium flow rate, medium pressure, medium temperature and medium composition.

[0073] First, the system establishes the correlation between physical characteristic parameters, media parameters, and fault location offset. Physical characteristic parameters include pipe material (e.g., metal, plastic), pipe diameter, pipe wall thickness, pipe connection method (e.g., welding, flange connection), and pipe age. These parameters affect the propagation characteristics of the detection signal. For example, different pipe materials attenuate signals to varying degrees, and increased pipe age leads to thinning of the pipe wall due to corrosion, thus altering the signal propagation path. Media parameters include media flow velocity, media pressure, media temperature, and media composition. These parameters determine the signal propagation characteristics within the media. For example, a higher media flow velocity can cause a shift in the signal propagation direction, and changes in media pressure affect the degree of signal attenuation.

[0074] By analyzing historical fault data, the system summarizes the offset impact of these parameters on fault location. For example, under specific pipe material and medium conditions, the initial location results may exhibit systematic deviations. Through statistical analysis, the system establishes a quantitative relationship between parameter combinations and location offsets, forming a parameter influence matrix. This correlation considers the interactions between parameters, making the correction process more consistent with actual conditions.

[0075] Based on the above embodiments, as an optional implementation, in S51, establishing the correlation between physical characteristic parameters, medium parameters, and fault location offset specifically includes S511-S514: S511 normalizes the physical characteristic parameters and medium parameters to obtain a standardized parameter set.

[0076] First, the system normalizes the collected physical characteristic parameters and medium parameters to obtain a standardized parameter set. The purpose of normalization is to eliminate dimensional differences between different parameters, making them comparable. For example, parameters with different dimensions, such as pipe diameter (unit: millimeters) and medium pressure (unit: megapascals), are uniformly converted to the [0,1] interval. This standardization process uses a maximum-minimum normalization method to ensure that all parameters are compared and calculated within the same numerical range.

[0077] S512: Obtain fault data of the pipeline network within a preset time period, and determine the influence weight of each standardized parameter in the standardized parameter set on the fault location offset based on the fault data.

[0078] Next, the system acquires fault data from the pipeline network over a preset period (e.g., the past year). This data includes the parameter values ​​at the time of the fault and the deviation between the actual fault location and the initial location. By analyzing this historical data, the system can identify the degree of influence of each parameter in the standardized parameter set on the fault location shift. For example, statistical analysis shows that every 10-year increase in pipeline age may lead to a 0.5-meter upstream shift in fault location; every 1-meter increase in medium flow velocity may lead to a 0.3-meter downstream shift. Based on these analysis results, the system assigns a corresponding influence weight to each standardized parameter.

[0079] S513, construct a parameter weighted combination function based on the influence weights.

[0080] After determining the influence weights of each parameter, the system constructs a parameter weighted combination function. This function uses each standardized parameter as an independent variable and its influence weight as coefficients, describing the comprehensive impact of parameter combinations on fault location offset through a mathematical model. The function's construction considers the interactions between parameters; for example, the combined effect of pipe material and medium pressure may produce additional location offsets. This weighted combination method can more accurately reflect the actual impact of parameter changes on fault location.

[0081] S514 calculates the fault location offset under different parameter combinations using a parameter weighted combination function, and establishes the correlation between physical characteristic parameters, medium parameters and fault location offset.

[0082] Finally, the system calculates the fault location offset under different parameter combinations using a parameter weighted combination function. By changing the input values ​​of different parameters, the system can obtain a series of correspondences between parameter combinations and location offsets. These calculation results form a complete correlation mapping, which can accurately predict the location offset under any parameter combination. For example, when the system detects a fault, it can directly obtain the corresponding location offset from this correlation based on the current parameter status.

[0083] S52, Calculate the correction value for the initial fault location based on the correlation.

[0084] Based on the established correlation, the system calculates a correction value for the initial fault location. Specifically, the system first acquires real-time physical characteristic parameters and medium parameters near the fault point, substitutes these parameters into the correlation, and calculates the location offset under the current conditions. This correction value reflects the degree and direction of the combined effect of various parameters on the initial fault location.

[0085] S53, correct the initial fault location based on the correction value to obtain the target fault location.

[0086] Finally, the system corrects the initial fault location based on the calculated correction value to obtain a more accurate target fault location. This correction process takes into account the continuity of spatial location and the constraints of the pipeline network structure, ensuring that the corrected location remains within a reasonable pipeline network range. For example, if the correction value indicates that the fault location should be offset upstream by 2 meters, the system will make the correction while ensuring that an actual pipe segment exists at that location.

[0087] After determining the target fault location, the process also includes: controlling the fourth robot, which is closest to the target fault location, to move to the target fault location; controlling the fourth robot to accurately detect the target fault location and obtain detailed fault information; determining the fault type and severity based on the detailed fault information; and generating a fault handling plan based on the fault type and severity.

[0088] The system first calculates the distance between each inspection robot and the target fault location using a positioning algorithm, and then selects the fourth robot, which is closest, to perform the precise inspection task. This proximity-based selection strategy minimizes response time and improves inspection efficiency. Based on the pipeline topology, the system plans the optimal movement path for the fourth robot, controlling it to reach the target fault location quickly and safely. During the movement, the system monitors the robot's motion status in real time to ensure it accurately reaches the designated location.

[0089] Once the fourth robot arrives at the target fault location, it immediately begins precise detection. Multiple sensors equipped on the robot (such as high-precision pressure sensors, acoustic sensors, and image sensors) work simultaneously to collect detailed information about the fault point from all angles. This information includes specific parameters such as the degree of pipe wall damage, crack size, leakage rate, and local pressure changes. Through multi-dimensional data acquisition, the system can obtain a complete characteristic description of the fault, providing detailed data support for subsequent analysis.

[0090] Based on the collected detailed fault information, the system uses a fault feature recognition algorithm to analyze and determine the fault type and severity. Fault type determination is based on a pre-set feature pattern library; by comparing the current fault features with standard patterns, the specific fault type is identified, such as pipe cracks, loose joints, or corrosion perforation. Fault severity assessment comprehensively considers multiple factors, including the extent of damage, degree of leakage, and development trend, and is quantitatively graded according to pre-set rating standards.

[0091] After determining the type and severity of the fault, the system automatically generates a targeted fault handling plan based on its built-in knowledge base of handling solutions and the current operating status of the pipeline network. The handling plan includes specific operating steps, required materials and equipment, and safety precautions. For example, for minor pipe cracks, the system may recommend repairing them with a specific type of sealing material; while for severe corrosion perforation, it may suggest replacing the damaged pipe section. The system also prioritizes handling plans based on the importance and difficulty of repairing the fault location, ensuring that faults in critical locations are addressed promptly.

[0092] Based on the above method, this application also discloses a multi-robot collaborative fault location system for pipeline networks, such as... Figure 2 As shown, Figure 2 This is a schematic diagram of the structure of a multi-robot collaborative fault location system for pipeline networks provided in an embodiment of this application. The system includes: a first acquisition module, a control module, a combination module, a second acquisition module, and a determination module; wherein, The first acquisition module is used to acquire pipeline status data sent by multiple robots deployed in the pipeline network. When abnormal data is found in the pipeline status data, the fault area corresponding to the abnormal data is determined. The control module is used to control the first robot in the fault area to inject detection signals into the pipeline network, acquire the first signal sent by the second robot after receiving the detection signal within the first preset range of the fault area, and the second signal sent by the third robot after receiving the detection signal within the second preset range of the fault area, where the first preset range is smaller than the second preset range. The combination module is used to combine the first signal and the second signal to determine the initial fault location within the fault area. The second acquisition module is used to acquire the physical characteristic parameters of the pipeline network and the medium parameters of the transport medium in the pipeline network. The determination module is used to determine the target fault location based on the initial fault location and by combining the physical characteristic parameters and the medium parameters.

[0093] It should be noted that the system provided in the above embodiments is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0094] Please see Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3 As shown, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0095] The communication bus 1002 is used to realize the connection and communication between these components.

[0096] The user interface 1003 may include a display screen and a camera. Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0097] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0098] The processor 1001 may include one or more processing cores. The processor 1001 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling data stored in the memory 1005. Optionally, the processor 1001 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 1001 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 1001 and may be implemented as a separate chip.

[0099] The memory 1005 may include random access memory (RAM) or read-only memory. Optionally, the memory 1005 may include a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 1005 may also be at least one storage device located remotely from the aforementioned processor 1001. Figure 3 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a multi-robot collaborative fault location method for pipeline networks.

[0100] exist Figure 3 In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 1001 can be used to call the application program of a pipeline multi-robot collaborative fault location method stored in the memory 1005. When executed by one or more processors, the electronic device performs one or more of the methods described in the above embodiments.

[0101] An electronic device readable storage medium stores instructions that, when executed by one or more processors, cause the electronic device to perform one or more of the methods described in the above embodiments.

[0102] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0103] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0104] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some service interfaces; indirect couplings or communication connections between devices or units may be electrical or other forms.

[0105] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0106] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0107] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0108] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A multi-robot collaborative fault location method for pipeline networks, characterized in that, The method includes: The system acquires pipeline status data sent by multiple robots deployed in the pipeline network. When abnormal data is found in the pipeline status data, the system determines the fault area corresponding to the abnormal data. The system controls a first robot within the fault area to inject a detection signal into the pipeline network, and obtains a first signal sent by a second robot within a first preset range of the fault area after receiving the detection signal, and a second signal sent by a third robot within a second preset range of the fault area after receiving the detection signal, wherein the first preset range is smaller than the second preset range. By combining the first signal and the second signal, the initial fault location is determined within the fault area; Obtain the physical characteristic parameters of the pipeline network and the medium parameters of the transport medium within the pipeline network; Based on the initial fault location, the target fault location is determined by combining the physical characteristic parameters and the medium parameters.

2. The multi-robot collaborative fault location method for pipeline networks according to claim 1, characterized in that, When abnormal data exists in the pipeline status data, determining the fault area corresponding to the abnormal data includes: Perform spatial clustering analysis on robots with abnormal data to form at least one abnormal data cluster; Based on the pipeline network topology and the spatial distribution characteristics of the abnormal data clusters, the influence range of each abnormal data cluster is calculated. By merging the overlapping influence ranges, a fault region boundary containing the spatial correlation of abnormal data is generated. Based on the mapping relationship between the fault area boundary and the pipeline network partition, the fault area corresponding to the abnormal data is output.

3. The multi-robot collaborative fault location method for pipeline networks according to claim 1, characterized in that, The step of combining the first signal and the second signal to determine the initial fault location within the fault area includes: Based on the first signal, determine the first attenuation characteristic of the detection signal within a first preset range; Based on the second signal, determine the second attenuation characteristic of the detection signal within the second preset range; By combining the first attenuation feature and the second attenuation feature, the initial fault location within the fault area is determined.

4. The multi-robot collaborative fault location method for pipeline networks according to claim 3, characterized in that, The step of determining the initial fault location within the fault area by combining the first attenuation feature and the second attenuation feature includes: By comparing the first attenuation feature with the second attenuation feature, the propagation pattern of the detection signal in the fault area is determined based on the comparison result; Based on the propagation law, the spatial attenuation gradient of the detection signal within the fault region is calculated; Based on the spatial attenuation gradient, attenuation anomaly points are determined, and these attenuation anomaly points are used as the initial fault locations within the fault area.

5. The multi-robot collaborative fault location method for pipeline networks according to claim 1, characterized in that, The step of determining the target fault location based on the initial fault location, combined with the physical characteristic parameters and the medium parameters, includes: Establish the correlation between the physical characteristic parameters, the medium parameters, and the fault location offset; the physical characteristic parameters include at least one of pipe material, pipe diameter, pipe wall thickness, pipe connection method, and pipe age; the medium parameters include at least one of medium flow rate, medium pressure, medium temperature, and medium composition. Based on the aforementioned correlation, calculate the correction value for the initial fault location; The initial fault location is corrected based on the correction value to obtain the target fault location.

6. The multi-robot collaborative fault location method for pipeline networks according to claim 5, characterized in that, Establishing the correlation between the physical characteristic parameters, the medium parameters, and the fault location offset includes: The physical characteristic parameters and the medium parameters are normalized to obtain a standardized parameter set; Obtain fault data of the pipeline network within a preset time period, and determine the influence weight of each standardized parameter in the standardized parameter set on the fault location offset based on the fault data; Based on the influence weights, construct a parameter weighted combination function; The fault location offset is calculated under different parameter combinations using the parameter weighting combination function, and the correlation between the physical characteristic parameters, the medium parameters and the fault location offset is established.

7. The multi-robot collaborative fault location method for pipeline networks according to claim 1, characterized in that, After determining the target fault location, the process also includes: Control the fourth robot, which is closest to the target fault location, to move to the target fault location; The fourth robot is controlled to accurately detect the target fault location and obtain detailed fault information; Based on the detailed fault information, determine the fault type and severity. A fault handling plan is generated based on the fault type and the fault severity.

8. A multi-robot collaborative fault location system for pipeline networks, characterized in that, The system includes: a first acquisition module, a control module, a combination module, a second acquisition module, and a determination module; wherein... The first acquisition module is used to acquire pipeline status data sent by multiple robots deployed in the pipeline network, and when there is abnormal data in the pipeline status data, to determine the fault area corresponding to the abnormal data; The control module is used to control the first robot in the fault area to inject a detection signal into the pipeline, and to obtain a first signal sent by the second robot within a first preset range of the fault area after receiving the detection signal, and a second signal sent by the third robot within a second preset range of the fault area after receiving the detection signal, wherein the first preset range is smaller than the second preset range. The combining module is used to combine the first signal and the second signal to determine the initial fault location within the fault area; The second acquisition module is used to acquire the physical characteristic parameters of the pipeline network and the medium parameters of the transport medium within the pipeline network; The determining module is used to determine the target fault location based on the initial fault location, combined with the physical characteristic parameters and the medium parameters.

9. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1-7.

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