Abnormity identification and fault detection method for equipment along bracket controller and electronic equipment

Abstract

The invention provides an abnormity identification and fault detection method for equipment along a support controller and electronic equipment, and belongs to the technical field of hydraulic support control, and the method comprises the steps: obtaining the installation state data, the electrical data, the sensor data and the production process data of the equipment along the support controller; according to the installation state data, the electrical data and the sensor data, abnormal identification is carried out on the equipment along the line, and according to the sensor data and the production process data, fault detection is carried out on the equipment along the line; and displaying an abnormal identification result and a fault detection result of the along-line equipment. According to the scheme, the accuracy and efficiency of abnormal recognition and fault detection of the equipment along the bracket controller can be improved.

Description

Technical Field

[0001] This application relates to the field of hydraulic support control technology, specifically to a method and electronic equipment for identifying and detecting abnormalities and faults in equipment along the support controller line. Background Technology

[0002] In coal mining faces, hydraulic supports need to be equipped with various devices such as support controllers, power supplies, couplers, and coupling switches. These devices also carry a wide variety of sensors. Many anomalies and malfunctions can occur during the installation and use of these devices and sensors. When these anomalies and malfunctions occur, they are often not detected in a timely manner, frequently causing production delays.

[0003] Currently, existing technologies primarily rely on manual methods for anomaly identification and fault detection in equipment along the support controller line, resulting in low accuracy and efficiency. Therefore, improving the accuracy and efficiency of anomaly identification and fault detection in equipment along the support controller line remains an unsolved problem. Summary of the Invention

[0004] The purpose of this application is to provide a method for anomaly identification and fault detection of equipment along the support controller line, which can solve the problem of low accuracy and efficiency of anomaly identification and fault detection of equipment along the support controller line in the prior art.

[0005] In a first aspect, embodiments of this application provide a method for identifying and detecting abnormalities and faults in equipment along a support controller line, the method comprising: Acquire installation status data, electrical data, sensor data, and production process data of the equipment along the support controller line; Based on installation status data, electrical data, and sensor data, anomalies are identified in the equipment along the line, and faults are detected in the equipment along the line based on sensor data and production process data. Displays the results of anomaly identification and fault detection for equipment along the line.

[0006] In one possible implementation of the first aspect, anomaly identification of equipment along the line is performed based on installation status data, electrical data, and sensor data, including: Based on installation status data, identify installation anomalies in equipment along the line and determine the location of the anomalies; based on electrical data and sensor data, identify electrical anomalies in equipment along the line.

[0007] In one possible implementation of the first aspect, the equipment along the line includes: a power supply, a coupler, a coupling switch, and a bracket controller; identifying installation anomalies in the equipment along the line based on installation status data includes: A topology diagram is generated based on the installation status data; based on the topology diagram and preset installation logic, installation anomalies are identified in the power supply, coupler, coupled switch, and bracket controller.

[0008] In one possible implementation of the first aspect, the electrical data includes voltage and current, and the equipment along the line further includes a temperature sensor, the sensor data including temperature collected by the temperature sensor; identifying electrical anomalies in the equipment along the line based on the electrical data and the sensor data includes: Based on voltage, current, temperature, and their respective preset ranges, electrical anomalies are identified in the power supply, coupler, coupling switch, and bracket controller.

[0009] In one possible implementation of the first aspect, the equipment along the production line further includes: a support frame, a tilt sensor, a mining height sensor, a stroke sensor, a humidity sensor, and a pressure sensor; based on sensor data and production process data, fault detection is performed on the equipment along the production line, including: Fault detection is performed on the support structure based on sensor data. Fault detection is also performed on the tilt sensor, height sensor, and stroke sensor based on production process data and sensor data. Fault detection is also performed on the temperature sensor, humidity sensor, and pressure sensor based on sensor data. Finally, fault detection is performed on the power supply, coupler, coupling switch, and support controller based on sensor data.

[0010] In one possible implementation of the first aspect, the sensor data further includes: historical and real-time pressures of the support column acquired by the pressure sensor; and fault detection of the support based on the sensor data, including: Historical pressure data is used to determine whether the support column has experienced a leakage fault, while real-time pressure data is used to determine whether the support column has experienced a pressure fault.

[0011] In one possible implementation of the first aspect, determining whether a leakage fault has occurred in the support column based on historical pressure includes: The historical pressure is smoothed and filtered; a historical pressure curve is generated based on the smoothed historical pressure; based on the evolution of the historical pressure curve, it is determined whether the support column has experienced a leakage fault.

[0012] In one possible implementation of the first aspect, the production process data includes support movement, and the sensor data further includes: tilt angle acquired by the tilt sensor, mining height acquired by the mining height sensor, and stroke acquired by the stroke sensor; fault detection is performed on the tilt sensor, mining height sensor, and stroke sensor based on the production process data and sensor data, including: Fault detection is performed on the tilt sensor based on the support movement and tilt angle, the mining height sensor based on the support movement and mining height, and the stroke sensor based on the support movement and stroke.

[0013] In one possible implementation of the first aspect, the sensor data further includes: humidity collected by a humidity sensor; and fault detection of the temperature sensor, humidity sensor, and pressure sensor based on the sensor data, including: Fault detection is performed on temperature sensors based on temperature, on humidity sensors based on humidity, and on pressure sensors based on historical and / or real-time pressure.

[0014] In one possible implementation of the first aspect, fault detection is performed on the power supply, coupler, coupling switch, and bracket controller based on sensor data, including: Determine whether there are poor contacts and / or circuit board abnormalities in the power supply, coupler, coupling switch, and bracket controller based on temperature, and determine whether there are sealing failures in the power supply, coupler, coupling switch, and bracket controller based on humidity.

[0015] In one possible implementation of the first aspect, the method further includes: An alarm is triggered based on the anomaly identification and fault detection results.

[0016] Secondly, embodiments of this application provide a device for identifying and detecting equipment anomalies along a support controller line, the device comprising: The acquisition unit is used to acquire installation status data, electrical data, sensor data, and production process data of the equipment along the support controller line; The processing unit is used to identify anomalies in the equipment along the line based on installation status data, electrical data, and sensor data, and to detect faults in the equipment along the line based on sensor data and production process data. The display unit is used to display the abnormality identification results and fault detection results of the equipment along the line.

[0017] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the support controller-line equipment anomaly identification and fault detection method described in any of the first aspects above.

[0018] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for identifying and detecting abnormal equipment along the support controller as described in any of the first aspects above.

[0019] Fifthly, embodiments of this application provide a computer program product that, when run on an electronic device, causes the electronic device to execute the bracket controller-line device anomaly identification and fault detection method described in any of the first aspects above.

[0020] The proposed solution first obtains the installation status data, electrical data, sensor data, and production process data of the equipment along the support controller line. Then, it performs anomaly identification based on the installation status data, electrical data, and sensor data, and performs fault detection based on the sensor data and production process data, thereby obtaining the anomaly identification results and fault detection results of the equipment along the line.

[0021] This application uses computer technology to identify anomalies and detect faults in the equipment along the support controller line. Compared with manual methods, it can improve the accuracy and efficiency of anomaly identification and fault detection, and has strong ease of use and practicality.

[0022] Other features and advantages of this application will be described in detail in the following detailed description section. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application, 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the steps of the method for identifying and detecting abnormal equipment along the support controller provided in the embodiments of this application; Figure 2 This is a schematic diagram of the topology link diagram provided in the embodiments of this application; Figure 3 This is a schematic diagram of the historical pressure curve provided in the embodiments of this application; Figure 4 This is a schematic diagram of the structure of the support controller-line equipment anomaly identification and fault detection system provided in this application embodiment; Figure 5 This is a schematic diagram of the structure of the support controller for identifying and detecting equipment abnormalities along the line, provided in an embodiment of this application. Figure 6 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0025] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0026] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or photovoltaic modules, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, photovoltaic modules and / or combinations thereof.

[0027] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0028] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0029] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [the described condition or event] is detected," or "in response to detection of [the described condition or event]."

[0030] Furthermore, in the description of this application, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0031] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in some other embodiments," "in other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0032] In coal mining faces, hydraulic supports require the installation of various devices such as support controllers, power supplies, couplers, and coupling switches. These devices are equipped with a wide variety of sensors. Numerous anomalies and malfunctions can occur during the installation and use of these devices and sensors. Often, these anomalies and malfunctions cannot be detected in a timely manner; the anomaly is only located after it has already occurred. The speed of location depends heavily on the operator's skill level, frequently resulting in production delays.

[0033] Currently, existing technologies primarily rely on manual methods for anomaly identification and fault detection in equipment along the support controller line, resulting in low accuracy and efficiency. Therefore, improving the accuracy and efficiency of anomaly identification and fault detection in equipment along the support controller line remains an unsolved problem.

[0034] To address the aforementioned deficiencies, this application provides a method for anomaly identification and fault detection of equipment along the support controller line. First, the installation status data, electrical data, sensor data, and production process data of the equipment along the support controller line are acquired. Then, anomaly identification is performed based on the installation status data, electrical data, and sensor data, and fault detection is performed based on the sensor data and production process data, thereby obtaining the anomaly identification results and fault detection results of the equipment along the line.

[0035] This application uses computer technology to identify anomalies and detect faults in the equipment along the support controller line. Compared with manual methods, it can improve the accuracy and efficiency of anomaly identification and fault detection, and has strong ease of use and practicality.

[0036] The specific process implemented in this application is described below through specific embodiments.

[0037] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating the steps of the method for identifying and detecting abnormalities in equipment along the support controller provided in this application embodiment. Figure 1 As shown, the method includes the following steps: S101, acquire installation status data, electrical data, sensor data, and production process data of the equipment along the support controller line.

[0038] In one embodiment, the support controller of the coal mining face typically has equipment interfaces on both the left and right sides, allowing the connection of the support controller, power supply, coupler, coupling switch, and other equipment in series. These devices can be connected to various types of sensors. For example, the support controller can be connected to sensors for tilt angle, pressure, mining height, stroke, temperature, humidity, etc., while the power supply, coupler, and coupling switch are equipped with sensors for temperature, humidity, etc.

[0039] In one embodiment, each bracket controller collects installation status data, electrical data, sensor data, and production process data of various devices. This data is transmitted via a communication bus and uploaded to the cloud for storage.

[0040] S102 identifies anomalies in equipment along the line based on installation status data, electrical data, and sensor data, and performs fault detection on equipment along the line based on sensor data and production process data.

[0041] Currently, existing technologies lack the function of identifying abnormalities (installation abnormality identification and electrical abnormality identification) of equipment along the support controller and a universal abnormality alarm mechanism, making it difficult to detect many abnormal conditions of equipment along the line in a timely manner.

[0042] The equipment along the support controller line must adhere to specific installation rules to ensure stable system operation. However, due to oversights during manual installation or technical limitations, existing systems often lack effective mechanisms to identify installation errors in the equipment along the support controller line, making it difficult to detect installation anomalies in a timely manner. Incorrect installation of equipment along the line may lead to poor system connections or obstructed signal transmission, thereby affecting the stability of the entire hydraulic support system.

[0043] Frequent downtime and repairs due to equipment and sensor malfunctions increase maintenance costs for businesses and may trigger electrical or mechanical failures, increasing safety hazards in coal mine operations. When malfunctions occur, manual troubleshooting is not only time-consuming and labor-intensive but also prone to errors, making it difficult to guarantee the accuracy and comprehensiveness of detection.

[0044] According to one embodiment of this application, anomaly identification is performed on equipment along the line based on installation status data, electrical data, and sensor data, including: Based on installation status data, identify installation anomalies in equipment along the line and determine the location of the anomalies; based on electrical data and sensor data, identify electrical anomalies in equipment along the line.

[0045] According to one embodiment of this application, the equipment along the line includes: a power supply, a coupler, a coupling switch, and a bracket controller. Installation anomaly identification of the equipment along the line is performed based on installation status data, including: A topology diagram is generated based on the installation status data; based on the topology diagram and preset installation logic, installation anomalies are identified in the power supply, coupler, coupled switch, and bracket controller.

[0046] Please see Figure 2 , Figure 2 This is a schematic diagram of the topological relationship link diagram provided in the embodiments of this application. For example... Figure 2 As shown, the topology link diagram contains 19 support controllers, numbered 1 to 19.

[0047] In one embodiment, a coupler is connected between bracket controllers 1 and 2, a power supply is connected between bracket controllers 4 and 5, a coupling switch is connected between bracket controllers 7 and 8, a coupler is connected between bracket controllers 10 and 11, and a coupler is connected between bracket controllers 16 and 17.

[0048] In one embodiment, the specific location of an incorrectly installed device can be identified based on the topology link diagram and preset installation rules (logic). These preset installation rules include: no more than six rack controllers between power supplies, couplers, or coupling switches; no couplers or coupling switches between two power supplies; inconsistent node types between adjacent rack controllers (a maximum of one device (non-rack controller device) can exist between two rack controllers; exceeding one device will result in inconsistent node types); and the identification of some unknown device types.

[0049] According to one embodiment of this application, the electrical data includes voltage and current, and the equipment along the line further includes a temperature sensor, the sensor data including temperature collected by the temperature sensor. Electrical anomaly identification of the equipment along the line is performed based on the electrical data and sensor data, including: Based on voltage, current, temperature, and their respective preset ranges, electrical anomalies are identified in the power supply, coupler, coupling switch, and bracket controller.

[0050] In one embodiment, real-time data (voltage, current, and temperature) is analyzed simultaneously from multiple dimensions. This includes analyzing real-time data from a single device, grouping devices with different connections, and comparing all similar devices along the line. Logical analysis identifies real-time electrical anomalies. An electrical anomaly is identified if the voltage, current, and / or temperature are outside a preset range.

[0051] Currently, existing technologies typically use sensor data to detect faults in other devices, but lack fault detection capabilities for the sensors themselves.

[0052] According to one embodiment of this application, the equipment along the production line further includes: a support frame, a tilt sensor, a mining height sensor, a stroke sensor, a humidity sensor, and a pressure sensor. Based on sensor data and production process data, fault detection is performed on the equipment along the production line, including: Fault detection is performed on the support structure based on sensor data. Fault detection is also performed on the tilt sensor, height sensor, and stroke sensor based on production process data and sensor data. Fault detection is also performed on the temperature sensor, humidity sensor, and pressure sensor based on sensor data. Finally, fault detection is performed on the power supply, coupler, coupling switch, and support controller based on sensor data.

[0053] In one embodiment, a bracket controller is mounted on the bracket, and the bracket controller can collect data from the bracket, such as production process data.

[0054] This embodiment can analyze the real-time data of the sensor and, in combination with the production process data, reverse analyze the sensor's faults.

[0055] Currently, existing technologies, after collecting data from equipment along the support controller line, typically only perform fault detection based on real-time data, without long-term data analysis and horizontal comparison (lacking comparative and time-dimensional analysis), thus failing to issue alarms for faults. If a fault occurs, it will lead to production process interruption, increased downtime, and consequently reduced production efficiency.

[0056] According to one embodiment of this application, the sensor data further includes: historical and real-time pressures of the support column collected by the pressure sensor. Fault detection of the support is performed based on the sensor data, including: Historical pressure data is used to determine whether the support column has experienced a leakage fault, while real-time pressure data is used to determine whether the support column has experienced a pressure fault.

[0057] According to one embodiment of this application, determining whether a support column has experienced a leakage fault based on historical pressure includes: The historical pressure is smoothed and filtered; a historical pressure curve is generated based on the smoothed historical pressure; based on the evolution of the historical pressure curve, it is determined whether the support column has experienced a leakage fault.

[0058] Please see Figure 3 , Figure 3 This is a schematic diagram of the historical pressure curve provided in an embodiment of this application. This historical pressure curve is the pressure curve on the right side of the stent's anterior column, and is a typical pressure curve for stent leakage. Figure 3As shown, the pressure curve is plotted with time t on the horizontal axis and pressure P (in MPa) on the vertical axis. The pressure continuously decreases from time t1 to t2. At time t2, the support performs a column raising action, and the front column rises to the top of the coal face, causing a short-term increase in pressure. After the column raising action ends, the pressure continues to decrease from time t2 to t3, indicating that a leakage fault has occurred in the front column of the support.

[0059] In one embodiment, a pressure sensor monitors the pressure data of the support column in real time. When the pressure of the support column is lower than the normal range, an alarm is triggered to indicate that the pressure of the support column is too low, prompting timely pressure replenishment.

[0060] According to one embodiment of this application, the production process data includes support movement, and the sensor data further includes: tilt angle collected by the tilt sensor, mining height collected by the mining height sensor, and stroke collected by the stroke sensor. Fault detection is performed on the tilt sensor, mining height sensor, and stroke sensor based on the production process data and sensor data, including: Fault detection is performed on the tilt sensor based on the support movement and tilt angle, the mining height sensor based on the support movement and mining height, and the stroke sensor based on the support movement and stroke.

[0061] In one embodiment, sensor faults are analyzed in reverse by combining production process data and sensor data. For example, the faults of the tilt sensor are analyzed by combining the support movement and tilt angle. The correspondence between support movement and tilt angle change trends is shown in Table 1. Table 1 is as follows: Table 1

[0062] In one embodiment, the support controller controls the support to perform actions. If the tilt sensor does not detect a corresponding change in tilt angle, it indicates that the tilt sensor has malfunctioned. To improve accuracy, multiple tests can be performed.

[0063] In one embodiment, if the mining height is determined to remain unchanged based on the lifting action of the support column, it indicates that the mining height sensor has malfunctioned.

[0064] In one embodiment, the mining height of all supports on the working face is compared at a certain moment. If the mining height of adjacent supports differs too much (exceeding a preset threshold) and this situation is maintained for more than a preset threshold (e.g., 3 minutes), a roof leak disaster warning is issued.

[0065] In one embodiment, if the stroke of the stroke sensor exceeds the stroke when the push rod moves to its maximum limit, it indicates that the stroke sensor has malfunctioned. In another embodiment, considering the pushing and pulling actions of the bracket, if the trend of the stroke sensor value does not conform to a certain pattern, it indicates that the stroke sensor has malfunctioned.

[0066] The variation pattern of the stroke sensor values ​​is as follows: During the pushing phase, the stroke exhibits a linear growth pattern: when the jack is extended, the stroke sensor output value increases linearly from the initial value, which is directly proportional to the pushing distance; during the pulling phase, the stroke exhibits a non-linear change pattern: in the initial stage of pulling, the stroke sensor output value decreases rapidly, and when approaching the end point, it decelerates due to the weight of the support frame.

[0067] In one embodiment, spatial analysis, combined with the coal mining machine's location, compares the stroke sensor values ​​at a certain moment during working face production to locate the S-bend position near the push rod, and analyzes the stroke sensor values ​​at the S-bend position. If the push rod strokes of adjacent supports differ significantly, exceeding the preset range for rigid bending of the scraper conveyor, it may damage the scraper conveyor. Simultaneously, continuous temporal analysis is performed; if this phenomenon occurs multiple times within an hour, an early warning is issued, prompting adjustments to production process parameters.

[0068] According to one embodiment of this application, the sensor data further includes humidity data collected by the humidity sensor. Fault detection is performed on the temperature sensor, humidity sensor, and pressure sensor based on the sensor data, including: Fault detection is performed on temperature sensors based on temperature, on humidity sensors based on humidity, and on pressure sensors based on historical and / or real-time pressure.

[0069] In one embodiment, if the temperature, humidity, or pressure exceeds the normal range, or remains at a specific value (e.g., 0) for an extended period, it indicates that the temperature sensor, humidity sensor, or pressure sensor has malfunctioned.

[0070] According to one embodiment of this application, fault detection is performed on the power supply, coupler, coupling switch, and bracket controller based on sensor data, including: Determine whether there are poor contacts and / or circuit board abnormalities in the power supply, coupler, coupling switch, and bracket controller based on temperature, and determine whether there are sealing failures in the power supply, coupler, coupling switch, and bracket controller based on humidity.

[0071] In one embodiment, a temperature sensor is used to analyze and compare the temperatures of similar devices (power supplies, couplers, coupling switches, or bracket controllers) at a given moment in space. If a device's temperature differs significantly from the other devices' temperatures (the difference exceeds a preset threshold), it indicates that the device is experiencing poor contact and / or a circuit board malfunction.

[0072] In one embodiment, a humidity sensor is used to analyze and compare the humidity of the same type of equipment at a given time in a spatial dimension. If the humidity of one equipment differs significantly from that of other equipment (the difference exceeds a preset threshold), it indicates that the equipment has a sealing failure. In a temporal dimension, the humidity of each equipment is monitored over a long period of time. If the humidity of one equipment gradually increases, it indicates that the equipment has a sealing failure.

[0073] S103 displays the results of anomaly identification and fault detection for equipment along the line.

[0074] According to one embodiment of this application, the method further includes: triggering an alarm, such as an audible and visual alarm, based on the anomaly identification result and the fault detection result.

[0075] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of the support controller-based equipment anomaly identification and fault detection system provided in this application embodiment. For example... Figure 4 As shown, the system includes: a support controller, a cloud platform, a display interface, and a method upgrade and expansion module.

[0076] In one embodiment, the support frame controller is used to upload installation status data, electrical data, sensor data, and production process data of the equipment along the production line to the cloud. The cloud includes a data storage module, an anomaly identification module, a fault detection module, and an alarm module. The data storage module stores the data uploaded by the support frame controller, the anomaly identification module identifies anomalies in the equipment along the production line, the fault detection module detects faults in the equipment along the production line, and the alarm module triggers an alarm based on the anomaly identification and fault detection results.

[0077] In one embodiment, the cloud sends analysis results (anomaly identification results and fault detection results) to a display interface, which then displays these results. The method upgrade and extension module is used to upgrade and extend the anomaly identification and fault detection methods based on an upgrade interface.

[0078] In one embodiment, the anomaly identification module and the fault detection module can upgrade their corresponding methods through a specific upgrade interface. These upgraded methods have undergone extensive data analysis and simulation verification in the laboratory, and the method upgrades also facilitate the expansion of new identification and detection functions and continuous optimization.

[0079] The method for anomaly identification and fault detection of equipment along the support controller provided in this application first obtains the installation status data, electrical data, sensor data, and production process data of the equipment along the support controller. Then, anomaly identification is performed based on the installation status data, electrical data, and sensor data, and fault detection is performed based on the sensor data and production process data, so as to obtain the anomaly identification result and fault detection result of the equipment along the support controller.

[0080] This application uses computer technology to identify anomalies and detect faults in the equipment along the support controller line. Compared with manual methods, it can improve the accuracy and efficiency of anomaly identification and fault detection, and has strong ease of use and practicality.

[0081] This application's solution obtains real-time equipment installation status data loaded by each support controller on the working face, and determines the topological relationship link diagram of the equipment along the support controller line. The topological relationship link diagram can clearly show the connection relationship of the equipment along the support controller line, including the physical connection and logical connection between the equipment. Then, by comparing the topological relationship link diagram with the preset installation logic, the location of abnormal equipment along the support controller line can be quickly located, thereby saving troubleshooting time, improving fault handling efficiency and equipment utilization efficiency, minimizing redundancy, and having wide applicability.

[0082] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0083] Corresponding to the method in the above embodiments, Figure 5 This is a schematic diagram of the structure of the support controller for identifying and detecting equipment anomalies along the line, provided in an embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown.

[0084] Please see Figure 5 The device includes: The acquisition unit 501 is used to acquire installation status data, electrical data, sensor data, and production process data of the equipment along the support controller line; The processing unit 502 is used to identify anomalies in the equipment along the line based on installation status data, electrical data, and sensor data, and to detect faults in the equipment along the line based on sensor data and production process data. Display unit 503 is used to display the abnormality identification results and fault detection results of equipment along the line.

[0085] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0086] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments 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. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0087] Figure 6 This is a schematic diagram of the structure of the electronic device 6 provided in an embodiment of this application. Figure 6 As shown, the electronic device 6 of this embodiment includes: at least one processor 601 ( Figure 6 Only one is shown in the diagram), memory 603, and computer program 602 stored in memory 603 and executable on at least one processor 601, wherein processor 601 executes computer program 602 to implement the steps in the above method embodiments.

[0088] Electronic device 6 can be a computing device such as a desktop computer, laptop, handheld computer, or mobile phone. This electronic device 6 may include, but is not limited to, a processor 601 and a memory 603. Those skilled in the art will understand that... Figure 6 This is merely an example of electronic device 6 and does not constitute a limitation on electronic device 6. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0089] The processor 601 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware photovoltaic modules, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0090] In some embodiments, memory 603 may be an internal storage unit of electronic device 6, such as a hard disk or memory of electronic device 6. In other embodiments, memory 603 may be an external storage device of electronic device 6, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on electronic device 6. Furthermore, memory 603 may include both internal and external storage units of electronic device 6. Memory 603 is used to store operating system, application programs, boot loader, data, and other programs, such as program code of computer programs. Memory 603 may also be used to temporarily store data that has been output or will be output.

[0091] If the integrated units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, when implementing all or part of the processes in the methods of the above embodiments, this application can use a computer program to instruct related hardware. This computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps applied in the method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable storage medium can include at least: any entity or device capable of carrying computer program code to a computing device / electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, such as a USB flash drive, a portable hard drive, a magnetic disk, or an optical disk.

[0092] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the various method embodiments described above.

[0093] This application provides a computer program product that, when run on an electronic device, causes the electronic device to execute the steps described in the various method embodiments above.

[0094] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0095] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0096] In the embodiments provided in this application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. The device / electronic device embodiments described above are merely illustrative, and the division of modules or units described above is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple units or photovoltaic modules may be combined or integrated into another system, and some features may be ignored. Furthermore, the indirect coupling, direct coupling, or communication connection shown or discussed may be through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0097] The units described above 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.

[0098] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the above embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for identifying and detecting equipment anomalies along a support controller line, characterized in that, The method includes: Acquire installation status data, electrical data, sensor data, and production process data of the equipment along the support controller line; Based on installation status data, electrical data, and sensor data, anomalies are identified in the equipment along the line, and faults are detected in the equipment along the line based on sensor data and production process data. The display shows the anomaly identification results and fault detection results of the equipment along the line.

2. The method for abnormal identification and fault detection of equipment along the support controller as described in claim 1, characterized in that, Based on installation status data, electrical data, and sensor data, anomaly identification is performed on the equipment along the line, including: Based on the installation status data, installation anomalies are identified in the equipment along the line, and the location of the installation anomaly is determined; based on the electrical data and the sensor data, electrical anomalies are identified in the equipment along the line.

3. The method for abnormal identification and fault detection of equipment along the support controller according to claim 2, characterized in that, The equipment along the route includes: power supplies, couplers, coupling switches, and bracket controllers; installation anomaly identification is performed on the equipment along the route based on the installation status data, including: Generate a topology relationship link diagram based on the installation status data; Based on the aforementioned topology diagram and preset installation logic, installation anomalies are identified in the power supply, coupler, coupling switch, and bracket controller.

4. The method for abnormal identification and fault detection of equipment along the support controller according to claim 3, characterized in that, The electrical data includes voltage and current. The equipment along the line also includes a temperature sensor, and the sensor data includes temperature collected by the temperature sensor. Based on the electrical data and the sensor data, electrical anomaly identification is performed on the equipment along the line, including: Based on voltage, current, temperature, and their respective preset ranges, electrical anomalies are identified in the power supply, coupler, coupling switch, and bracket controller.

5. The method for abnormal identification and fault detection of equipment along the support controller according to claim 4, characterized in that, The equipment along the production line also includes: supports, tilt sensors, mining height sensors, stroke sensors, humidity sensors, and pressure sensors; based on sensor data and production process data, fault detection is performed on the equipment along the production line, including: Fault detection is performed on the support based on sensor data. Fault detection is performed on the tilt sensor, height sensor, and stroke sensor based on the production process data and sensor data. Fault detection is performed on the temperature sensor, humidity sensor, and pressure sensor based on sensor data. Fault detection is performed on the power supply, coupler, coupling switch, and support controller based on sensor data.

6. The method for abnormal identification and fault detection of equipment along the support controller according to claim 5, characterized in that, The sensor data also includes: historical and real-time pressures of the support column collected by the pressure sensor; fault detection of the support is performed based on the sensor data, including: The historical pressure is used to determine whether the support column has experienced a leakage fault, and the real-time pressure is used to determine whether the support column has experienced a pressure fault.

7. The method for abnormal identification and fault detection of equipment along the support controller as described in claim 6, characterized in that, Determining whether the support column has experienced a leakage fault based on the historical pressure includes: The historical pressure is smoothed and filtered. Based on the smoothed and filtered historical pressure, a historical pressure curve is generated; Based on the evolution of the historical pressure curves, it is determined whether the support column has experienced a leakage failure.

8. The method for abnormal identification and fault detection of equipment along the support controller according to claim 5, characterized in that, The production process data includes support movement, and the sensor data further includes: tilt angle collected by the tilt sensor, mining height collected by the mining height sensor, and stroke collected by the stroke sensor; fault detection is performed on the tilt sensor, mining height sensor, and stroke sensor based on the production process data and sensor data, including: Fault detection is performed on the tilt sensor based on the support movement and tilt angle, the mining height sensor based on the support movement and mining height, and the stroke sensor based on the support movement and stroke.

9. The method for abnormal identification and fault detection of equipment along the support controller according to claim 6, characterized in that, The sensor data also includes: humidity collected by the humidity sensor; and fault detection of the temperature sensor, humidity sensor, and pressure sensor based on the sensor data, including: Fault detection is performed on the temperature sensor based on temperature, on the humidity sensor based on humidity, and on the pressure sensor based on the historical pressure and / or the real-time pressure.

10. The method for abnormal identification and fault detection of equipment along the support controller according to claim 9, characterized in that, Fault detection is performed on the power supply, coupler, coupling switch, and bracket controller based on sensor data, including: Determine whether there are poor contacts and / or circuit board abnormalities in the power supply, coupler, coupling switch, and bracket controller based on temperature, and determine whether there are sealing failures in the power supply, coupler, coupling switch, and bracket controller based on humidity.

11. The method for identifying and detecting equipment anomalies along the support controller line according to any one of claims 1-10, characterized in that, The method further includes: An alarm is triggered based on the anomaly identification results and the fault detection results.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for abnormal identification and fault detection of equipment along the support controller as described in any one of claims 1-11.

13. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for abnormal identification and fault detection of equipment along the support controller as described in any one of claims 1-11.

14. A computer program product, characterized in that, When the computer program product is run on the electronic device, the electronic device performs the bracket controller line equipment anomaly identification and fault detection method as described in any one of claims 1-11.

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