Monitoring method for tracking operation condition of support and related device

Abstract

The invention discloses a monitoring method for the operation condition of a tracking support and a related device, which can be used in the field of data processing, and the method comprises the following steps: firstly, obtaining the operation data of the tracking support; then, based on the operation data and a fault diagnosis algorithm, operation fault information of the tracking support is obtained; wherein the operation fault information at least comprises operation data of a fault period and a fault diagnosis result; finally, inputting the operation fault information and fault repair information corresponding to the operation fault information into the large language model; the large language model is used for responding to the fault scene indicated by the inquiry instruction, and outputting target fault information in the fault scene and fault repair information corresponding to the target fault information. Therefore, the operation and maintenance personnel can accurately look up the historical experience through a simple question and answer mode, and can timely and rapidly carry out effective maintenance on the faulted tracking support according to the historical experience, thereby improving the maintenance efficiency of the faulted tracking support.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a monitoring method for tracking support operation and related device. BACKGROUND

[0002] With the continuous expansion of the scale of solar photovoltaic power stations, the tracking support system as a key equipment to improve the power generation efficiency of photovoltaic panels has become increasingly important.

[0003] Currently, the monitoring system usually uses a web monitoring webpage to monitor and display the running data of the tracking support in real time. However, when facing complex and variable fault modes, the traditional monitoring system lacks effective historical data management and query mechanisms, and often only relies on the experience of operation and maintenance personnel to determine how to maintain the tracking support that has failed, which leads to difficulty in responding to the running fault information of the tracking support in a timely and rapid manner, effectively maintaining the tracking support that has failed, and low maintenance efficiency.

[0004] Therefore, how to improve the maintenance efficiency of the tracking support that has failed becomes a problem to be solved. SUMMARY

[0005] Based on the above problems, the present application provides a monitoring method for tracking support operation and related device, which can improve the maintenance efficiency of the tracking support that has failed.

[0006] The present application embodiment discloses the following technical scheme:

[0007] In a first aspect, the present application embodiment provides a monitoring method for tracking support operation, which comprises:

[0008] obtaining running data of the tracking support;

[0009] obtaining running fault information of the tracking support based on the running data and a fault diagnosis algorithm; the running fault information at least includes running data of a fault period and a fault diagnosis result;

[0010] inputting the running fault information and fault repair information corresponding to the running fault information into a large language model; the large language model is used to output target fault information under a fault scene indicated by an inquiry instruction and fault repair information corresponding to the target fault information in response to the fault scene.

[0011] Optionally, the inputting the running fault information and fault repair information corresponding to the running fault information into the large language model comprises:

[0012] Performing text slicing processing on the operation fault information and the fault repair information corresponding to the operation fault information through an embedded language model to obtain a knowledge base;

[0013] The knowledge base is input into a large language model.

[0014] Optionally, performing text slicing processing on the operation fault information and the fault repair information corresponding to the operation fault information by using an embedded language model to obtain a knowledge base includes:

[0015] Through the embedded language model, based on the fault cause, fault occurrence time and fault repair method, the operation fault information and the fault repair information corresponding to the operation fault information are subjected to text slicing processing to obtain a structured knowledge base.

[0016] Optionally, after inputting the operation fault information and the fault repair information corresponding to the operation fault information into the large language model, the method further includes:

[0017] Through the large language model, statistical information on the operation status of the tracking bracket within a preset statistical period is output every time a statistical period is preset.

[0018] Optionally, the outputting of the operation statistics of the tracking bracket within a preset statistical period by the large language model includes:

[0019] Outputting, through the large language model, operational status information of the tracking bracket within a preset statistical period at intervals of the statistical period; the operational status information includes at least one of the number of faults corresponding to each type of fault of the tracking bracket, the site where the fault occurred, the duration of the fault repair, and the type of product in which the fault occurred;

[0020] A statistical table is generated based on the operation status information to obtain operation status statistical information of the tracking bracket.

[0021] Optionally, obtaining operation fault information of the tracking bracket based on the operation data and the fault diagnosis algorithm includes:

[0022] The operating data is diagnosed by a fault diagnosis algorithm to obtain fault diagnosis results corresponding to the abnormal rotation of each row of tracking brackets;

[0023] If there is a target fault diagnosis result indicating abnormal rotation of the target tracking bracket, operation fault information of the tracking bracket is generated based on the identification of the target tracking bracket, the target fault diagnosis result and the operation data of the fault period corresponding to the target fault diagnosis result.

[0024] Optionally, after obtaining the operation fault information of the tracking support based on the operation data and the fault diagnosis algorithm, the method further comprises:

[0025] generating a fault work order based on the operation fault information; the fault work order is used to prompt the operation fault of the tracking support;

[0026] generating a closed-loop work order based on the fault work order and fault repair information corresponding to the fault work order; the fault repair information comprises at least one of a work order type, a fault handling process, and a fault repair duration.

[0027] In a second aspect, an embodiment of the present application provides a monitoring device for tracking support operation, the device comprising: an acquisition module, a diagnosis module, and an input module;

[0028] The acquisition module is configured to acquire operation data of the tracking support.

[0029] The diagnosis module is configured to obtain operation fault information of the tracking support based on the operation data and a fault diagnosis algorithm; the operation fault information comprises at least operation data of a fault period and a fault diagnosis result.

[0030] The input module is configured to input the operation fault information and fault repair information corresponding to the operation fault information into a large language model; the large language model is configured to output target fault information in a fault scenario indicated by an inquiry instruction and fault repair information corresponding to the target fault information.

[0031] In a third aspect, an embodiment of the present application provides a monitoring device for tracking support operation, the device comprising: a memory and a processor;

[0032] The memory is configured to store program code and transmit the program code to the processor.

[0033] The processor is configured to execute steps of the monitoring method for tracking support operation according to the program code.

[0034] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, and when the computer program runs on the monitoring device for tracking support operation, the monitoring device for tracking support operation executes steps of the monitoring method for tracking support operation according to any one of the embodiments of the first aspect.

[0035] Compared with the prior art, the present application has the following beneficial effects:

[0036] The embodiment of the present application provides a monitoring method for tracking support operation condition, in the method, firstly, operation data of the tracking support is acquired; then, operation fault information of the tracking support is obtained based on the operation data and a fault diagnosis algorithm; wherein the operation fault information at least includes operation data of a fault period and a fault diagnosis result; finally, the operation fault information and fault repair information corresponding to the operation fault information are input into a large language model; the large language model is used for outputting target fault information in a fault scene and fault repair information corresponding to the target fault information in response to the fault scene indicated by an inquiry instruction.

[0037] Therefore, the operation condition of the tracking support is automatically diagnosed through the fault diagnosis algorithm, and when the tracking support is found to be faulty, the operation fault information and the corresponding fault repair information are input into the large language model as a knowledge base, so that the operation and maintenance personnel can accurately consult historical experience through a simple question and answer mode, and according to the historical experience, the operation and maintenance personnel can timely and rapidly effectively maintain the faulty tracking support, and improve the maintenance efficiency of the faulty tracking support. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0039] Figure 1 A flow chart of a monitoring method for tracking support operation condition provided by the embodiment of the present application;

[0040] Figure 2 A flow chart of another monitoring method for tracking support operation condition provided by the embodiment of the present application;

[0041] Figure 3 A schematic diagram of a monitoring device for tracking support operation condition provided by the embodiment of the present application;

[0042] Figure 4 A structural diagram of a monitoring device for tracking support operation condition provided by the embodiment of the present application. DETAILED DESCRIPTION

[0043] The monitoring method for tracking support operation condition and the related device provided by the present application can be applied to the field of data processing. The above is only an example, and does not limit the application field of the monitoring method for tracking support operation condition and the related device provided by the present application.

[0044] The terms "first", "second", "third", and "fourth" and the like in the specification and claims of the application and the accompanying drawings are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.

[0045] In the embodiments of the present application, the words such as "as an example" or "for example" are used to represent an example, illustration or description. Any embodiment or design scheme described as "as an example" or "for example" in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the words such as "as an example" or "for example" are intended to present the relevant concept in a specific manner.

[0046] The terms used in the implementation part of the present application are only used to explain the specific embodiments of the present application, and are not intended to limit the present application.

[0047] As mentioned earlier, conventional tracking support control systems are typically composed of modules such as motors, control boxes (TCU), communication boxes (NCU), etc., which adjust the orientation of photovoltaic panels to maximize sunlight absorption through these components. However, over time and with the influence of environmental factors such as wind and sand, corrosion, etc., the operation of these systems may deviate or fail, affecting power generation efficiency and even causing equipment damage.

[0048] Currently, monitoring systems usually use web monitoring pages to monitor and display the running data of tracking supports in real time. However, in the face of complex and variable fault modes, traditional monitoring systems lack effective historical data management and query mechanisms, often relying on the experience of maintenance personnel to determine how to maintain tracking supports that have failed, making it difficult to respond to tracking support running failure information in a timely and efficient manner, and effectively maintain tracking supports that have failed.

[0049] Therefore, in the embodiments of the present application, first, the running data of the tracking support is obtained; then, based on the running data and the fault diagnosis algorithm, the running failure information of the tracking support is obtained; wherein the running failure information at least includes the running data of the fault period and the fault diagnosis result; finally, the running failure information and the fault repair information corresponding to the running failure information are input into the large language model; the large language model is used to respond to the fault scene indicated by the inquiry instruction, and output the target failure information in the fault scene and the fault repair information corresponding to the target failure information.

[0050] Therefore, the operation of the tracking support is automatically diagnosed by the fault diagnosis algorithm, and when a fault of the tracking support is found, the operation fault information and the corresponding fault repair information are input to the large language model as a knowledge base, so that the operation and maintenance personnel can accurately consult historical experience through a simple question and answer method. According to the historical experience, the operation and maintenance personnel can timely and quickly effectively maintain the tracking support with a fault, and improve the maintenance efficiency of the tracking support with a fault.

[0051] In order to enable personnel in the technical field to better understand the scheme of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0052] Referring to Figure 1 The figure is a flow chart of a monitoring method for tracking support operation provided by an embodiment of the present application. The method comprises:

[0053] S101: Obtain operation data of the tracking support.

[0054] As an example, in a tracking support power station project, the control system of the tracking support can be composed of modules such as motors, control boxes (TCU), communication boxes (NCU), etc. The control box controls the motor to rotate the support shaft to adjust the orientation of the photovoltaic panel. Each tracking support square array can be configured with a communication box, and each row of tracking supports in the tracking support square array is configured with a control box. The communication box and the control box can communicate through a wireless module.

[0055] The monitoring device for monitoring the operation of the tracking support can obtain real-time operation data of the tracking support from modules such as the control box and the communication box. The operation data can include but is not limited to key parameters reflecting the operation of the tracking support, such as the motor current of the control box and the rotation angle of the tracking support.

[0056] S102: Obtain operation fault information of the tracking support based on the operation data and the fault diagnosis algorithm.

[0057] As an example, historical operation data can be collected in advance, and the required feature variables can be extracted from the historical operation data according to the diagnosis requirements. The feature variables can include but are not limited to the trend of the motor current and the deviation of the rotation angle.

[0058] The model training can be performed by using the feature variables to obtain the fault diagnosis algorithm used in the embodiments of the present application, so as to accurately diagnose whether the tracking support exists a running fault through the fault diagnosis algorithm. For example, through the fault diagnosis algorithm, it can be found that the peak-to-valley value of the motor current change jumps and exceeds the preset threshold value, at this time, it can be judged that the motor does not rotate or the rotation is limited, and the corresponding fault reason can be that the motor structure connecting piece is abnormal.

[0059] Specifically, the running data can be diagnosed by the fault diagnosis algorithm to obtain fault diagnosis results respectively corresponding to rotation abnormal conditions of each row of tracking supports; if there is a target fault diagnosis result indicating that the target tracking support rotates abnormally, the running fault information of the tracking support can be generated based on the identifier of the target tracking support, the target fault diagnosis result, and the running data of the fault period corresponding to the target fault diagnosis result.

[0060] As an example, the identifier of the target tracking support can be an identifier indicating the position of the target tracking support where the fault occurs, such as an identifier indicating that the target tracking support is in the nth row. The collection time of the running data corresponding to the fault diagnosis result indicating the rotation abnormality of the tracking support is denoted as T, and the fault period can be (T-n, T+n), wherein n can be flexibly set according to actual needs; or the fault period can also include multiple collection times of the running data corresponding to the fault diagnosis results respectively indicating the rotation abnormality of the tracking support, and the interval time is less than a preset time threshold. The fault diagnosis result can include, for example, the running state information of each row of tracking supports such as no abnormal rotation of the first row of tracking supports and abnormal rotation of the second row of tracking supports, and can also include, for example, the fault diagnosis basis such as the peak-to-valley value of the motor current change jumping and exceeding the preset threshold value, the fault scene such as the motor not rotating or the rotation being limited, and the fault reason such as the abnormality of the motor structure connecting piece.

[0061] S103: inputting the running fault information and the fault repair information corresponding to the running fault information into the large language model.

[0062] The fault repair information is repair information generated by the operation and maintenance personnel in response to the maintenance of the tracking support based on the running fault information.

[0063] As an example, the running fault information and the fault repair information corresponding to the running fault information can be subjected to text slicing processing by the embedded language model to obtain a knowledge base; and then the knowledge base is input into the large language model.

[0064] Specifically, the running fault information and the fault repair information corresponding to the running fault information can be processed by text slicing based on dimensions such as fault causes, fault occurrence times, and fault repair methods through an embedded language model, to form a structured knowledge base, so as to improve the readability of the data and facilitate subsequent queries and analysis of historical running fault information and fault repair information. The fault repair methods can include, but are not limited to, replacement of parts and parameter adjustment values.

[0065] The knowledge base is input into the large language model, that is, the data processed by text slicing is input into the large language model. The large language model can learn by itself based on the knowledge base, understand and remember various fault cases and corresponding solutions, and then the large language model can output target fault information and corresponding fault repair information under the same fault scene in response to a fault scene indicated by an inquiry instruction. Thus, the operation and maintenance personnel can install an APP on a mobile terminal such as a mobile phone to accurately review historical experience in a simple question-and-answer manner, and can maintain the tracking bracket that has failed according to the historical experience, so as to effectively maintain the tracking bracket that has failed in a timely and rapid manner, and improve the maintenance efficiency of the tracking bracket that has failed.

[0066] In the embodiments of the present application, first, running data of the tracking bracket is obtained; then, based on the running data and a fault diagnosis algorithm, running fault information of the tracking bracket is obtained; wherein the running fault information at least includes running data of a fault period and a fault diagnosis result; finally, the running fault information and fault repair information corresponding to the running fault information are input into a large language model; the large language model is used to output target fault information under a fault scene and fault repair information corresponding to the target fault information in response to a fault scene indicated by an inquiry instruction. Thus, the running condition of the tracking bracket is automatically diagnosed by the fault diagnosis algorithm, and when the tracking bracket fails, the running fault information and the corresponding fault repair information are input into the large language model as a knowledge base, so that the operation and maintenance personnel can accurately review historical experience extracted from the target running fault information and the corresponding fault repair information in a simple question-and-answer manner, and can effectively maintain the tracking bracket that has failed in a timely and rapid manner according to the historical experience, thereby improving the maintenance efficiency of the tracking bracket that has failed.

[0067] Referring to Figure 2 The figure is a flow chart of another monitoring method of the running condition of the tracking bracket provided by the embodiments of the present application, and the method comprises:

[0068] S201: Obtain running data of the tracking bracket.

[0069] S202: Obtain running fault information of the tracking bracket based on the running data and a fault diagnosis algorithm.

[0070] wherein the operation fault information at least comprises operation data of a fault period and a fault diagnosis result

[0071] S203: generating a fault work order based on the operation fault information.

[0072] Exemplarily, a fault work order for prompting tracking bracket operation fault can be generated based on the operation fault information, and the fault work order can be assigned to a corresponding operation and maintenance personnel based on a preset work order process. Further, the corresponding operation and maintenance personnel can perform online or offline tracking bracket maintenance based on information such as the identification of the target tracking bracket, the fault diagnosis basis, the fault scene, and the fault cause, etc. displayed by the fault work order, such as adjusting parameters online or replacing parts offline, etc., to realize the closed loop of the fault work order, and generate a closed loop work order based on the fault work order and the fault repair information corresponding to the fault work order.

[0073] In the process of tracking bracket maintenance, the operation and maintenance personnel can manually upload fault type information such as mechanical failure, fault handling process information such as replacement parts list, and / or fault repair time to obtain fault repair information; or the monitoring device can automatically record fault type information such as electrical failure, fault handling process information such as fault period and operation data change after maintenance, and / or fault repair time to obtain fault repair information.

[0074] Optionally, the fault work order can be classified according to characteristics such as fault scene, fault cause or fault handling method, the work order type can be configured for the fault work order, and the work order type can be added to the fault repair information, so that the target fault information and the fault repair information can be quickly found according to the corresponding characteristics in subsequent.

[0075] As an example, the fault work order can include but is not limited to labels such as basic information, fault details, diagnosis results, fault handling process, fault handling results, and related documents. The basic information can include any one or more of information such as work order number, work order creation time, work order priority, and work order status; the fault details can include any one or more of information such as fault location, fault period, and fault scene; the diagnosis result can be the fault diagnosis result in the operation fault information; and the related documents can include but are not limited to operation data of the fault period. That is, the fault work order can include information for describing the operation fault, and one or more fault repair information labels to be supplemented after the operation and maintenance personnel maintain the tracking bracket.

[0076] S204: generating a closed loop work order based on the fault work order and the fault repair information corresponding to the fault work order.

[0077] In the process of maintenance of the target tracking support with a fault by the operation and maintenance personnel, the contents of the labels such as the fault handling process, the fault handling result and the related documents in the fault work order can be supplemented based on the fault repair information, and a closed-loop work order is generated.

[0078] S205: input the operation fault information and the fault repair information corresponding to the operation fault information into the large language model.

[0079] Exemplarily, the information in the closed-loop work order can be subjected to text slicing processing, and the information in the same closed-loop work order is associated, and then input into the large language model, so as to facilitate subsequent quick query of the historical operation fault information and the fault repair information.

[0080] Optionally, a front-end framework such as React or Vue.js can be used in combination with a chart library such as ECharts or Chart.js to build the interface elements of the statistical table, so that the target fault information and the fault repair information obtained by querying the large language model can be intuitively displayed in the form of a table, facilitating the operation and maintenance personnel to quickly understand the historical experience and further improve the maintenance efficiency.

[0081] S206: output the operation condition statistical information of the tracking support in each statistical period by the large language model.

[0082] Specifically, the statistical period can be set in advance, and the large language model is called regularly based on the statistical period to output the operation condition statistical information of the tracking support. By regularly outputting the operation condition statistical information of the tracking support, the user can intuitively understand the health status of the tracking support and even the entire photovoltaic power station, so as to timely discover potential risks and take preventive measures to reduce maintenance costs, prolong the service life of the equipment and ultimately improve the overall economic benefit of the photovoltaic power station.

[0083] Exemplarily, the operation condition information of the tracking support in each statistical period can be output by the large language model every interval of the preset statistical period; wherein the operation condition information includes at least one of the fault occurrence frequency, the fault occurrence site, the fault repair time and the product type corresponding to each type of fault of the tracking support; and then the statistical table is generated based on the operation condition information to obtain the operation condition statistical information of the tracking support.

[0084] Optionally, the operation condition statistical information in the form of the statistical table can be used to display information such as the occurrence frequency of a certain type of fault in a certain photovoltaic power station, the occurrence frequency and the corresponding fault repair time of a certain type of fault work order, the frequency of a certain type of fault occurring in a plurality of power stations in a certain region, or the type of fault with the highest occurrence frequency of different types of control systems.

[0085] For example, taking a quarter as a statistical period, through the running condition statistical information, the occurrence frequency of each type of fault in a certain photovoltaic power station and the average fault repair time are shown, and the statistical table can be as shown in Table 1:

[0086] Table 1

[0087]

[0088] Among them, 2025Q1 represents the first quarter of 2025.

[0089] Through Table 1, it can be directly seen that in the first quarter of 2025, the frequency of motor failure in A station is relatively high, and then the motor can be comprehensively overhauled to reduce the frequency of motor failure, thereby reducing the subsequent maintenance cost.

[0090] Therefore, the running condition statistical information is regularly output in the form of a statistical table, which can make the running condition statistical information more intuitively presented to the user, and the user can quickly find that the frequency of a certain type of fault is rising, or the frequency of a certain photovoltaic power station is rising, or which types of faults in a certain photovoltaic power station have a higher frequency, etc. so as to discover potential risks in time and take targeted prevention or improvement measures to reduce maintenance costs and prolong equipment service life.

[0091] Referring to Figure 3 , the figure is a schematic diagram of a monitoring device for tracking the running condition of a support, which comprises an acquisition module 301, a diagnosis module 302 and an input module 303;

[0092] The acquisition module 301 is configured to acquire running data of the tracking support;

[0093] The diagnosis module 302 is configured to obtain running fault information of the tracking support based on the running data and a fault diagnosis algorithm; the running fault information at least comprises running data of a fault period and a fault diagnosis result;

[0094] The input module 303 is configured to input the running fault information and fault repair information corresponding to the running fault information into a large language model; the large language model is configured to output target fault information in a fault scene and fault repair information corresponding to the target fault information in response to a fault scene indicated by an inquiry instruction.

[0095] Therefore, the operation of the tracking support is automatically diagnosed by the fault diagnosis algorithm, and when a fault of the tracking support is found, the operation fault information and the corresponding fault repair information are input to the large language model as a knowledge base, so that the operation and maintenance personnel can accurately consult historical experience through a simple question and answer mode, and according to the historical experience, the operation and maintenance personnel can timely and quickly effectively maintain the tracking support with a fault, thereby improving the maintenance efficiency of the tracking support with a fault.

[0096] Optionally, the input module 303 includes a text slicing unit and an input unit, where the text slicing unit is specifically configured to perform text slicing processing on the operation fault information and the fault repair information corresponding to the operation fault information by using the embedded language model to obtain a knowledge base; and the input unit is specifically configured to input the knowledge base to the large language model.

[0097] Optionally, the text slicing unit is specifically configured to perform text slicing processing on the operation fault information and the fault repair information corresponding to the operation fault information by using the embedded language model based on the fault reason, the fault occurrence time, and the fault repair mode to obtain a structured knowledge base.

[0098] Optionally, the other monitoring device for the operation of the tracking support provided in the embodiment of the present application further includes a statistical module configured to output operation condition statistical information of the tracking support in each statistical period by using the large language model every interval of a preset statistical period.

[0099] Optionally, the statistical module is specifically configured to output operation condition information of the tracking support in each statistical period by using the large language model every interval of a preset statistical period; the operation condition information includes at least one of a fault occurrence number, a fault occurrence site, a fault repair time length, and a product type corresponding to each type of fault of the tracking support; and a statistical table is generated based on the operation condition information to obtain the operation condition statistical information of the tracking support.

[0100] Optionally, the diagnosis module 302 is specifically configured to diagnose the operation data by using a fault diagnosis algorithm to obtain fault diagnosis results corresponding to abnormal rotation conditions of each row of tracking supports; and if there is a target fault diagnosis result indicating abnormal rotation of a target tracking support, operation fault information of the tracking support is generated based on the identifier of the target tracking support, the target fault diagnosis result, and operation data of a fault period corresponding to the target fault diagnosis result.

[0101] Optionally, another monitoring device for tracking operation of a support provided by an embodiment of the application further comprises a work order generation module configured to generate a fault work order based on the operation fault information; the fault work order is configured to prompt the operation fault of the support; a closed-loop work order is generated based on the fault work order and fault repair information corresponding to the fault work order; the fault repair information comprises at least one of a work order type, a fault handling process, and a fault repair duration.

[0102] Referring to Figure 4 The figure is a structure diagram of a monitoring device for tracking operation of a support provided by an embodiment of the application, which comprises a memory 401 and a processor 402.

[0103] The memory 401 is configured to store program codes and transmit the program codes to the processor.

[0104] The processor 402 is configured to execute the steps of the monitoring method for tracking operation of a support according to the instructions in the program codes.

[0105] In addition, the application further provides a computer readable storage medium, which stores computer instructions, and when the computer instructions are run on the monitoring device for tracking operation of a support, the monitoring device for tracking operation of a support executes the steps of the monitoring method for tracking operation of a support.

[0106] It should be noted that each embodiment in the specification is described in a progressive manner, and the same and similar parts between each embodiment can be referred to each other, and each embodiment focuses on the difference from other embodiments. Especially, the device and storage medium embodiments are described more simply because they are basically similar to the method embodiments, and the relevant parts can be referred to the part of the description of the method embodiments. The device and storage medium embodiments described above are only illustrative, and the units described as separate components can be or can not be physically separated, and the components indicated as units can be or can not be physical units, that is, they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment according to the actual needs. Those skilled in the art can understand and implement it without creative labor.

[0107] The above is only a specific embodiment of the application, but the protection scope of the application is not limited to this. Any changes or replacements within the technical range disclosed by the application can be easily thought of by those skilled in the art, which should be covered in the protection scope of the application. Therefore, the protection scope of the application should be subject to the protection scope of the claims.

Claims

1. A monitoring method for tracking the operation of a support, characterized in that, The method comprises: Obtaining the operation data of the tracking bracket; Based on the operation data and the fault diagnosis algorithm, operation fault information of the tracking bracket is obtained; the operation fault information at least includes the operation data of the fault period and the fault diagnosis result; The operation fault information and the fault repair information corresponding to the operation fault information are input into a large language model; the large language model is used to respond to the fault scenario indicated by the query instruction, and output the target fault information under the fault scenario and the fault repair information corresponding to the target fault information.

2. The method of claim 1, wherein, The inputting the operation fault information and the fault repair information corresponding to the operation fault information into the large language model includes: Performing text slicing processing on the operation fault information and the fault repair information corresponding to the operation fault information through an embedded language model to obtain a knowledge base; The knowledge base is input into a large language model.

3. The method of claim 2, wherein, The embedded language model is used to perform text slicing processing on the operation fault information and the fault repair information corresponding to the operation fault information to obtain a knowledge base, including: Through the embedded language model, based on the fault cause, fault occurrence time and fault repair method, the operation fault information and the fault repair information corresponding to the operation fault information are subjected to text slicing processing to obtain a structured knowledge base.

4. The method of claim 1, wherein, After inputting the operation fault information and the fault repair information corresponding to the operation fault information into the large language model, the method further includes: Through the large language model, statistical information on the operation status of the tracking bracket within a preset statistical period is output every time a statistical period is preset.

5. The method of claim 4, wherein, The method outputs the operation statistics of the tracking bracket within a preset statistical period through the large language model, including: Outputting, through the large language model, operational status information of the tracking bracket within a preset statistical period at intervals of the statistical period; the operational status information including at least one of the number of faults corresponding to each type of fault of the tracking bracket, the site where the fault occurred, the duration of the fault repair, and the type of product in which the fault occurred; A statistical table is generated based on the operation status information to obtain operation status statistical information of the tracking bracket.

6. The method of claim 1, wherein, The obtaining of operation fault information of the tracking bracket based on the operation data and the fault diagnosis algorithm includes: The operating data is diagnosed by a fault diagnosis algorithm to obtain fault diagnosis results corresponding to the abnormal rotation of each row of tracking brackets; If there is a target fault diagnosis result indicating abnormal rotation of the target tracking bracket, operation fault information of the tracking bracket is generated based on the identification of the target tracking bracket, the target fault diagnosis result and the operation data of the fault period corresponding to the target fault diagnosis result.

7. The method of claim 1, wherein, After obtaining the operation fault information of the tracking bracket based on the operation data and the fault diagnosis algorithm, the method further includes: Generate a fault work order based on the operation fault information; the fault work order is used to prompt the operation fault of the tracking bracket; Generate a closed-loop work order based on the fault work order and fault repair information corresponding to the fault work order; the fault repair information includes at least one of work order type, fault handling process and fault repair duration.

8. A monitoring device for tracking the operation of a support, characterized in that The device comprises an acquisition module, a diagnosis module and an input module; The acquisition module is configured to acquire operation data of the tracking support; The diagnosis module is configured to obtain operation fault information of the tracking support based on the operation data and a fault diagnosis algorithm; the operation fault information at least includes operation data of a fault period and a fault diagnosis result; The input module is configured to input the operation fault information and fault repair information corresponding to the operation fault information into a large language model; the large language model is configured to output target fault information under a fault scene indicated by an inquiry instruction and fault repair information corresponding to the target fault information.

9. A monitoring device for tracking the operation of a support, characterized in that The device comprises a memory and a processor; The memory is configured to store program code and transmit the program code to the processor; The processor is configured to execute the steps of the tracking support operation monitoring method according to the program code.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and when the computer program runs on the tracking support operation monitoring device, the tracking support operation monitoring device executes the steps of the tracking support operation monitoring method as claimed in any one of claims 1-7.

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