Health state assessment method, device and equipment of intelligent excitation system, readable storage medium and program product

Abstract

The invention relates to a health state assessment method, device and equipment of an intelligent excitation system, a readable storage medium and a program product. The method comprises the following steps: acquiring health index data obtained by performing health index monitoring on the intelligent excitation system; inputting the health index data into a pre-trained health state evaluation model, and outputting a health state evaluation result of the intelligent excitation system under the excitation system health evaluation system; determining health state description information corresponding to the intelligent excitation system according to the health state evaluation result; querying an operation optimization suggestion matched with the health state description information and the system working condition information of the intelligent excitation system in a preset scheme database; and constructing a health state analysis report corresponding to the intelligent excitation system according to the health state description information and the operation optimization suggestion. By adopting the method, the reliability of health state evaluation of the intelligent excitation system can be improved.

Description

Technical Field

[0001] This application relates to the field of power technology, and in particular, to a method, device, computer device, computer-readable storage medium, and computer program product for evaluating the health status of an intelligent excitation system. Background Art

[0002] The excitation system is an important part of the generator, mainly responsible for controlling the magnetic field of the generator rotor by adjusting the excitation current, thereby stabilizing the output voltage and power of the generator. The health status of the excitation system is directly related to the safety and stability of the generator and the entire power system. With the increase in the complexity of modern power systems and load fluctuations, the excitation system needs to operate stably for a long time under high loads and complex environments. Therefore, accurate evaluation of its health status is particularly important.

[0003] Most of the work on evaluating the health status of the excitation system in the related art is carried out based on health indexes or score sheets, and relies on the experience and intuitive judgment of operation and maintenance personnel, which has certain subjectivity and uncertainty, and may lead to missed inspections or misjudgments of faults.

[0004] Therefore, there is a problem of low reliability in evaluating the health status of the intelligent excitation system in the related art. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide a method, device, computer device, computer-readable storage medium, and computer program product for evaluating the health status of an intelligent excitation system, which can improve the reliability of evaluating the health status of the intelligent excitation system.

[0006] In a first aspect, this application provides a method for evaluating the health status of an intelligent excitation system, including:

[0007] Obtaining health index data obtained by monitoring the health indexes of the intelligent excitation system; the health indexes are indexes used to evaluate the health status of the intelligent excitation system;

[0008] Inputting the health index data into a pre-trained health status evaluation model, and outputting a health status evaluation result of the intelligent excitation system under the excitation system health evaluation system; the health status evaluation result is determined by the pre-trained health status evaluation model according to the weight information corresponding to each health index and the health index data corresponding to each health index;

[0009] Determining health status description information corresponding to the intelligent excitation system according to the health status evaluation result;

[0010] Query the operation optimization suggestions that match the health status description information and the system working conditions information of the intelligent excitation system in the preset solution database;

[0011] Construct a health status analysis report corresponding to the intelligent excitation system according to the health status description information and the operation optimization suggestions.

[0012] In one embodiment, the obtaining the health index data obtained by monitoring the health index of the intelligent excitation system includes:

[0013] Obtain the operation status data of the intelligent excitation system;

[0014] Obtain the data operation rules corresponding to each health index, and perform arithmetic processing on the operation status data according to the data operation rules corresponding to each health index to obtain the health index data.

[0015] In one embodiment, the obtaining the operation status data of the intelligent excitation system includes:

[0016] Obtain the operation log data of the intelligent excitation system; the operation log data includes the operation status data to be processed;

[0017] Eliminate the operation status data that does not meet the preset conditions from the operation status data to be processed to obtain the operation status data after elimination.

[0018] In one embodiment, the method further includes:

[0019] Obtain the health indexes set for the intelligent excitation system;

[0020] According to each health index, combine the states of the intelligent excitation system from the whole to the part in the form of hierarchical components to construct the health assessment system of the excitation system; the health assessment system of the excitation system includes the health indexes with corresponding hierarchical structures.

[0021] In one embodiment, the method further includes:

[0022] Adopt the pairwise comparison method to make pairwise judgments on the importance of each health index for health status monitoring to obtain an index comparison matrix;

[0023] Determine the weight information corresponding to each health index in the index comparison matrix by the eigenvalue method or the arithmetic average method.

[0024] In one embodiment, inputting the health index data into a pre-trained health status evaluation model and outputting the health status evaluation result of the intelligent excitation system under the excitation system health evaluation system includes:

[0025] Converting the corresponding health index data into a score value according to the scoring rules corresponding to each health index;

[0026] Obtaining the product result between the weight information corresponding to each health index and the corresponding score value;

[0027] Obtaining the sum of each product result, and generating a health status score and / or a health status level according to the sum of each product result;

[0028] Outputting the health status evaluation result according to the health status score and / or the health status level.

[0029] In a second aspect, the present application further provides a health status evaluation device for an intelligent excitation system, including:

[0030] An acquisition module, configured to acquire health index data obtained by monitoring health indexes of the intelligent excitation system; the health indexes are indexes used to evaluate the health status of the intelligent excitation system;

[0031] An evaluation module, configured to input the health index data into a pre-trained health status evaluation model and output the health status evaluation result of the intelligent excitation system under the excitation system health evaluation system; the health status evaluation result is determined by the pre-trained health status evaluation model according to the weight information corresponding to each health index and the health index data corresponding to each health index;

[0032] An information determination module, configured to determine the health status description information corresponding to the intelligent excitation system according to the health status evaluation result;

[0033] A query module, configured to query operation optimization suggestions matching the health status description information and the system working condition information of the intelligent excitation system in a preset solution database;

[0034] A construction module, configured to construct a health status analysis report corresponding to the intelligent excitation system according to the health status description information and the operation optimization suggestions.

[0035] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, and the memory stores a computer program, and when the computer program is executed by the processor, the steps of the above method are implemented.

[0036] Fourthly, the present application also provides a computer-readable storage medium. On the computer-readable storage medium, a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0037] Fifthly, the present application also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0038] For the above intelligent excitation system health status evaluation method, device, computer device, computer-readable storage medium and computer program product, health index data obtained by monitoring health indexes of the intelligent excitation system is acquired; the health index is an index used to evaluate the health status of the intelligent excitation system; the health index data is input into a pre-trained health status evaluation model to output a health status evaluation result of the intelligent excitation system under the excitation system health evaluation system; the health status evaluation result is determined by the pre-trained health status evaluation model according to the weight information corresponding to each health index and the health index data corresponding to each health index; according to the health status evaluation result, health status description information corresponding to the intelligent excitation system is determined; an operation optimization suggestion matching the health status description information and the system working condition information of the intelligent excitation system is queried in a preset scheme database; according to the health status description information and the operation optimization suggestion, a health status analysis report corresponding to the intelligent excitation system is constructed.

[0039] In this way, by acquiring the health index data obtained by monitoring the health indexes of the intelligent excitation system, and through the pre-trained health status evaluation model according to the weight information corresponding to each health index and the health index data corresponding to each health index, the health status evaluation result of the intelligent excitation system under the excitation system health evaluation system can be more accurately evaluated. Thus, according to the health status evaluation result, the health status description information and the operation optimization suggestion of the intelligent excitation system can be determined to construct a health status analysis report corresponding to the intelligent excitation system, so as to enable the user to accurately master the health status of the intelligent excitation system, effectively improving the reliability of the health status evaluation of the intelligent excitation system. Description of the Drawings

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0041] Figure 1Schematic flowchart of a method for evaluating the health status of an intelligent excitation system in an embodiment;

[0042] Figure 2 Schematic flowchart of the step of outputting the health status evaluation result in an embodiment;

[0043] Figure 3 Schematic flowchart of a method for evaluating the health status of an intelligent excitation system in another embodiment;

[0044] Figure 4 Block diagram of the structure of a device for evaluating the health status of an intelligent excitation system in an embodiment;

[0045] Figure 5 Internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0046] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0048] In one embodiment, as Figure 1 shown, a method for evaluating the health status of an intelligent excitation system is provided. In this embodiment, it is exemplified that the method is applied to a computer device. It can be understood that the computer device can be a terminal or a system including a terminal and a server. In this embodiment, the method includes the following steps:

[0049] Step S110, obtaining health index data obtained by monitoring health indexes of the intelligent excitation system.

[0050] Among them, the health index is an index used to evaluate the health status of the intelligent excitation system.

[0051] Among them, the health index data can be index data obtained by monitoring the intelligent excitation system using the health index.

[0052] Among them, the health indicators may include at least one of the conventional failure rate, the accelerated failure rate, the conventional mean time between failures, the accelerated mean time between failures, and other indicators.

[0053] Among them, the other indicators may include at least one of temperature, load, running time, etc.

[0054] Among them, the health indicators can be selected according to actual needs.

[0055] In a specific implementation, the computer device can obtain the health indicator data obtained by monitoring the real-time health indicators of the intelligent excitation system.

[0056] In some embodiments, the health indicator data may include the indicator data of the intelligent excitation system under at least one of the conventional failure rate, the accelerated failure rate, the conventional mean time between failures, the accelerated mean time between failures, and other indicators.

[0057] In one of the embodiments, obtaining the health indicator data obtained by monitoring the health indicators of the intelligent excitation system includes: obtaining the operation status data of the intelligent excitation system; obtaining the data operation rules corresponding to each health indicator, and performing arithmetic processing on the operation status data according to the data operation rules corresponding to each health indicator to obtain the health indicator data.

[0058] Among them, the computer device can obtain the operation log data of the intelligent excitation system, and the operation log data is used to record various events and operation records generated during the operation of the intelligent excitation system. The operation status data of the intelligent excitation system can be obtained through the operation log data.

[0059] Among them, the operation status data may refer to the data associated with the operation status of the intelligent excitation system. For example, the operation status data may include temperature data, voltage and current data, etc.

[0060] Among them, the operation status data recorded in the operation log data can be obtained through sensor monitoring. For example, industrial-grade sensors can be used to monitor the operation status of the intelligent excitation system and the generator in which it is located to obtain the operation status data.

[0061] In this way, the computer device can obtain the data operation rules corresponding to each health indicator, and perform arithmetic processing on the operation status data according to the data operation rules corresponding to each health indicator to obtain the health indicator data.

[0062] Further, the operation log data may include operation status data to be processed; the computer device may eliminate the operation status data that does not meet the preset conditions from the operation status data to be processed, obtaining the operation status data after elimination, and thus may perform arithmetic processing on the operation status data after elimination according to the data arithmetic rules corresponding to each health indicator, obtaining health indicator data.

[0063] Among them, the operation status data that does not meet the preset conditions may refer to at least one of the data with data acquisition errors, data format errors, and illogical data. For example, a data acquisition error may refer to the data collected not conforming to the actual situation due to reasons such as sensor failure, communication interruption, or data transmission error; a data format error may refer to the data format not conforming to the predefined rules; being illogical may refer to that from the perspective of the logic of system operation, some data may not conform to the logic of the normal operation of the intelligent excitation system. For example, under normal circumstances, the excitation current should appropriately increase as the generator load increases, but there is a data record in the operation log that when the generator load increases, the excitation current suddenly drops to zero, which is different from the logic of the normal operation of the system. This kind of illogical data is likely to be caused by data recording errors or system anomalies, and this kind of illogical data will be identified as incorrect data.

[0064] In this way, the health indicators set for the intelligent excitation system can be used to more accurately monitor the health status of the intelligent excitation system. Thus, by performing arithmetic processing on the operation status data of the intelligent excitation system according to the data arithmetic rules corresponding to the health indicators set for the intelligent excitation system, the health indicator data for identifying the health status of the intelligent excitation system can be obtained more accurately, effectively improving the accuracy and reliability of the health status assessment of the intelligent excitation system.

[0065] Step S120: Input the health indicator data into a pre-trained health status assessment model, and output the health status assessment result of the intelligent excitation system under the excitation system health assessment system; among them, the health status assessment result is determined by the pre-trained health status assessment model according to the weight information corresponding to each health indicator and the health indicator data corresponding to each health indicator.

[0066] Among them, the pre-trained health assessment model may be an artificial intelligence model.

[0067] Among them, the excitation system health assessment system may refer to an index system for health status assessment of the intelligent excitation system. This index system may include each hierarchical health indicator.

[0068] In a specific implementation, the computer device can input the health index data into a pre-trained health status assessment model. The pre-trained health status assessment model can evaluate the health status of the intelligent excitation system according to the weight information corresponding to each health index and the health index data corresponding to each health index, and output the health status assessment result of the intelligent excitation system under the excitation system health assessment system.

[0069] Furthermore, the computer device can convert the health index data corresponding to each health index into a score value according to the scoring rule corresponding to each health index; adjust the corresponding score value according to the weight information corresponding to each health index to obtain the health status assessment result.

[0070] In some embodiments, the health status assessment result may include the overall health status score of the intelligent excitation system.

[0071] In this way, the computer device can generate a health status analysis report corresponding to the intelligent excitation system according to the health status assessment result.

[0072] Among them, the health status analysis report includes health status description information and operation optimization suggestions for the intelligent excitation system.

[0073] Among them, the health status description information refers to the text information that describes the health status of the intelligent excitation system according to the corresponding health status assessment result.

[0074] Among them, the operation optimization suggestions refer to a series of targeted measures proposed based on the current health status assessment result and health status description information of the excitation system, combined with its actual operating conditions (such as real-time fluctuations in power generation load, grid voltage stability, ambient temperature and humidity, etc.), with the goals of further improving the operation efficiency, reliability, stability of the excitation system and extending the service life of the equipment.

[0075] In a specific implementation, the computer device can determine the health status description information and operation optimization suggestions of the intelligent excitation system according to the health status assessment result, and output the health status description information and operation optimization suggestions in the form of a report to obtain the health status analysis report. In steps S130~S150, the generation of the health status analysis report will be further described in detail.

[0076] Step S130, determine the health status description information corresponding to the intelligent excitation system according to the health status assessment result.

[0077] Among them, the health status assessment result may include a health status score and / or a health status level.

[0078] In a specific implementation, the computer device can determine the health status description information corresponding to the intelligent excitation system according to the health status evaluation result.

[0079] Among them, the preset health status levels have corresponding scoring ranges, and the health status level corresponding to the intelligent excitation system can be determined according to the scoring range where the health status score of the intelligent excitation system is located. For example, the preset health status levels can include excellent, good, medium, and poor, and the corresponding scoring ranges can be (85, 100), (70, 84), (60, 69), and less than 60 respectively.

[0080] For example, if the health status score is between 85 - 100 points, the health status description information can be "The current operating status of the excitation system is good, all parameters are stable, and the equipment reliability is high"; if the health status score is between (60, 69) points, the health status description information can be "The excitation system is in the normal operating range, but some indicators are close to the critical value, and the operation dynamics need to be closely monitored."

[0081] Step S140, query the operation optimization suggestions that match the health status description information and the system operating conditions information of the intelligent excitation system in the preset solution database.

[0082] Among them, the system operating conditions information refers to the information used to characterize the system operating conditions of the intelligent excitation system.

[0083] Among them, the preset solution database is a database storing various operation optimization suggestions.

[0084] Among them, the operation optimization suggestions can be classified and stored according to dimensions such as health status, system operating conditions (power generation load range, grid voltage stability, environmental conditions, etc.).

[0085] For example, for the health status of "normal but need to pay attention", and in the condition that the grid voltage fluctuates greatly during the peak power generation load period, the operation optimization suggestions stored in the database may include "Increase the monitoring frequency of the voltage regulation link, check the regulator parameters every 15 minutes; install a static var compensator between the grid side and the excitation system to buffer the voltage fluctuation."

[0086] When it is determined that the current health status is "normal but need to pay attention", and the known system operating conditions are that the power generation load is at 80% of the rated value, the grid voltage fluctuates frequently, and the environmental temperature is high, through the retrieval function of the database, the corresponding operation optimization suggestions are accurately matched. These suggestions should not only address the problems of the current operating status but also combine specific operating condition factors, such as "Considering the high-temperature environment, give priority to checking the cooling system to ensure normal heat dissipation; adjust the excitation current in a timely manner according to the load situation to ensure the stable operation of the generator; prepare emergency spare parts to deal with possible component failures caused by voltage fluctuations."

[0087] Step S150: Construct a health status analysis report corresponding to the intelligent excitation system based on the health status description information and operation optimization suggestions.

[0088] In specific implementation, the computer device can construct a health status analysis report according to the health status description information and operation optimization suggestions.

[0089] Furthermore, the computer device can input the health status description information and operation optimization suggestions into a large language model, and through the natural language understanding and text generation capabilities of the large language model, output a health status analysis report constructed based on the health status description information and operation optimization suggestions according to the instruction requirements.

[0090] Among them, the instructions to be input into the large language model mainly include the number of words, format, content requirements, etc.

[0091] In specific implementation, the computer device can generate target prompt word information according to the health status description information, operation optimization suggestions and a preset prompt word template, and input the target prompt word information into a pre-trained large model to instruct the pre-trained large model to generate a health status analysis report.

[0092] Among them, the preset prompt word template can be the Prompt template of the pre-trained large model.

[0093] Among them, a Prompt is an instruction, question or statement that can be used to guide or instruct a language model to generate a specific text output. A Prompt is the starting point for the user to interact with the language model. It tells the model the user's intention and expects the model to respond in a meaningful and relevant way.

[0094] In this way, by determining the health status description information corresponding to the intelligent excitation system according to the health status evaluation result; querying operation optimization suggestions that match the health status description information and the system working conditions information of the intelligent excitation system in a preset solution database; constructing a health status analysis report according to the health status description information and operation optimization suggestions. Thus, it is possible to match operation optimization suggestions from the solution database based on the health status description information and system working conditions, ensure that the given operation optimization suggestions closely meet the actual needs, and construct a health status analysis report according to the health status description information and operation optimization suggestions, standardize the information presentation format, and make information transmission clearer and more efficient.

[0095] In the above-mentioned method for evaluating the health state of the intelligent excitation system, health index data obtained by monitoring the health indexes of the intelligent excitation system is acquired; the health index is an index used to evaluate the health state of the intelligent excitation system; the health index data is input into a pre-trained health state evaluation model, and the health state evaluation result of the intelligent excitation system under the excitation system health evaluation system is output; the health state evaluation result is determined by the pre-trained health state evaluation model according to the weight information corresponding to each health index and the health index data corresponding to each health index; according to the health state evaluation result, the health state description information corresponding to the intelligent excitation system is determined; an operation optimization suggestion that matches the health state description information and the system working condition information of the intelligent excitation system is queried in a preset scheme database; according to the health state description information and the operation optimization suggestion, a health state analysis report corresponding to the intelligent excitation system is constructed.

[0096] In this way, by acquiring the health index data obtained by monitoring the health indexes of the intelligent excitation system, and through the pre-trained health state evaluation model according to the weight information corresponding to each health index and the health index data corresponding to each health index, the health state evaluation result of the intelligent excitation system under the excitation system health evaluation system can be more accurately evaluated. Thus, according to the health state evaluation result, the health state description information and the operation optimization suggestion of the intelligent excitation system can be determined to construct a health state analysis report corresponding to the intelligent excitation system, so as to enable the user to accurately grasp the health state of the intelligent excitation system, effectively improving the reliability of the health state evaluation of the intelligent excitation system.

[0097] In one embodiment, the method further includes: acquiring the health indexes set for the intelligent excitation system; according to each health index, combining the states of the intelligent excitation system from the whole to the part in the form of hierarchical components to construct an excitation system health evaluation system; the excitation system health evaluation system includes health indexes with corresponding hierarchical structures.

[0098] In specific implementation, for the construction of the excitation system health evaluation system, the computer device can acquire the health indexes set for the intelligent excitation system, and according to each health index, combine the states of the intelligent excitation system from the whole to the part in the form of hierarchical components to construct the excitation system health evaluation system, so that the excitation system health evaluation system includes each health index with corresponding hierarchical structures.

[0099] In practical applications, in the process of constructing the excitation system health evaluation system, the states of the excitation system are organically combined from the whole to the part in the form of hierarchical components, and the Analytic Hierarchy Process (AHP) is used to evaluate the health state of the intelligent excitation system and specify the scoring rules.

[0100] Specifically include:

[0101] Step 1: Determine the evaluation objectives and criteria; Evaluation objective: Evaluate the health status of the intelligent excitation system. Evaluation criteria: Determine the main health indicators that affect the health status, and these health indicators may include at least one of the following: conventional failure rate, accelerated failure rate, conventional mean time between failures, accelerated mean time between failures, and other indicators (such as temperature, load, operating time, etc., depending on the specific excitation system).

[0102] Step 2: Construct a hierarchical structure; Construct a hierarchical structure from the overall to the local of the excitation system in the form of hierarchical components; Determine the hierarchical structure where each health indicator is located.

[0103] In some embodiments, the excitation system may include an excitation transformer and an excitation cabinet; an excitation transformer, a dry-type transformer; a wire between the excitation transformer and the excitation cabinet.

[0104] In other embodiments, the excitation system may include an excitation transformer (which includes a high-voltage coil, a low-voltage coil, an iron core, temperature measurement, a voltage transformer, and a current transformer of the dry-type transformer), an AC conductor (cable or cast busbar), an excitation disk cabinet (an AC incoming breaker inside the AC incoming cabinet (including an operating mechanism, contacts, a closing coil, a tripping coil, a storage motor, a heater)), three power cabinets (including a fan, thyristors, fast fuses, capacitors, resistors, control boards, contactors, temperature probes, pulse circuits, copper bars, heaters, a human-machine interface), a field discharge switch cabinet (including a DC field discharge switch, copper bars, a transmitter, a shunt, a Hall sensor, a heater, a human-machine interface, a DC output cable), a field discharge resistor cabinet (including a field discharge resistor, a jumper control board, a heater, a human-machine interface), and an adjustment cabinet (a core control board, a relay, a human-machine interface, an air switch, a contactor)).

[0105] Thus, in some embodiments, construct a hierarchical structure from the overall to the local of the excitation system in the form of hierarchical components; Determine the hierarchical structure where each health indicator is located, and the faulty components in the intelligent excitation system can be detected through the health indicator data obtained by monitoring the health indicators of the intelligent excitation system.

[0106] Step 3: Conduct pairwise comparisons: Use the pairwise comparison method to make pairwise judgments on the importance of each health indicator for health status monitoring to obtain an indicator comparison matrix.

[0107] Step 4: Calculate the weight vector: Determine the weight information corresponding to each health indicator in the indicator comparison matrix through the eigenvalue method or the arithmetic mean method.

[0108] In some embodiments, such as Figure 2As shown in the figure, in step S120, the health index data is input into a pre-trained health status evaluation model, and the health status evaluation result of the intelligent excitation system under the excitation system health evaluation system is output, including:

[0109] In step S1202, according to the scoring rules corresponding to each health index, the corresponding health index data is converted into a score value.

[0110] Among them, the scoring rules can be conversions based on thresholds, ranges, or mathematical formulas.

[0111] In step S1204, the product results between the weight information corresponding to each health index and the corresponding score value are obtained.

[0112] In step S1206, the sum of each product result is obtained, and based on the sum of each product result, a health status score and / or a health status level are generated.

[0113] In step S1208, the health status evaluation result is output according to the health status score and / or the health status level.

[0114] In a specific implementation, when the computer device inputs the health index data into the pre-trained health status evaluation model and outputs the health status evaluation result of the intelligent excitation system under the excitation system health evaluation system, the pre-trained health status evaluation model can convert the corresponding health index data into a score value according to the scoring rules corresponding to each health index; obtain the product results between the weight information corresponding to each health index and the corresponding score value, add the product results to get the sum of each product result, and generate a health status score and / or a health status level based on the sum of each product result; output the health status evaluation result according to the health status score and / or the health status level.

[0115] In practical applications, a health status score can be generated based on the sum of each product result, and then the corresponding health status level can be determined according to the health status score.

[0116] The technical solution of this embodiment is to convert the corresponding health index data into a score value according to the scoring rules corresponding to each health index; obtain the product results between the weight information corresponding to each health index and the corresponding score value; obtain the sum of each product result, and generate a health status score and / or a health status level based on the sum of each product result; output the health status evaluation result according to the health status score and / or the health status level.

[0117] In this way, by introducing the weight information corresponding to each health indicator, the relative importance of different indicators to the overall health status of the excitation system can be fully reflected. The sum of the product of the score values obtained by converting the health indicator data corresponding to each health indicator and the corresponding weights is used to generate the health status score and level, converting the complex system operation status into intuitive and easy-to-understand numerical values and categories, enabling the operation and maintenance personnel to quickly and accurately grasp the overall health degree of the excitation system, and effectively improving the intelligence of the health assessment of the excitation system.

[0118] In some embodiments, for the construction of the pre-trained health status assessment model, based on the existing excitation fault diagnosis data and expert database data, a data-driven health status assessment model for the intelligent excitation system can be established. This health status assessment model is an artificial intelligence model. In practical applications, the health status assessment model can be trained using training sample data, and by comparing the health status assessment results of the health status evaluation model with the actual experimental data of the intelligent excitation system, the model parameters of the health status assessment model can be dynamically updated to optimize the health status assessment model and obtain the pre-trained health status assessment model.

[0119] In this way, relying on the existing excitation fault diagnosis data, which carry key information such as a large number of past fault cases, fault characteristics, and corresponding solutions that occurred in actual operation, provides highly authentic and targeted learning materials for the model; moreover, introducing expert database data further strengthens the professionalism of the model. Continuously training the model using training sample data enables the model to flexibly adjust with the dynamic changes in the operating environment, working conditions, and the state of the equipment itself of the excitation system. Comparing the health status assessment results with the actual experimental data provides a direct basis for the dynamic update of the model. This real-time feedback and optimization mechanism ensures that the model does not deviate from the actual situation and continuously improves the reliability of the prediction.

[0120] In another embodiment, as Figure 3 shown, a method for assessing the health status of an intelligent excitation system is provided. Taking the application of this method to a computer device as an example, it includes the following steps S01 to step S150:

[0121] Step S01, obtain the health indicators set for the intelligent excitation system.

[0122] Step S011, according to each health indicator, combine the status of the intelligent excitation system from the whole to the local in the form of hierarchical components to construct a health assessment system for the excitation system.

[0123] Step S012, using the pairwise comparison method, make pairwise judgments on the importance of each health indicator for health status monitoring to obtain an indicator comparison matrix.

[0124] Step S014, determine the weight information corresponding to each health indicator in the indicator comparison matrix by the eigenvalue method or the arithmetic mean method.

[0125] Step S110, obtain the health indicator data obtained by monitoring the health indicators of the intelligent excitation system.

[0126] Among them, step S110, obtaining the health indicator data obtained by monitoring the health indicators of the intelligent excitation system, includes steps S1102 to S1104:

[0127] Step S1102, obtain the operation status data of the intelligent excitation system.

[0128] Step S1104, obtain the data operation rules corresponding to each health indicator, and perform arithmetic processing on the operation status data according to the data operation rules corresponding to each health indicator to obtain the health indicator data.

[0129] Among them, step S1102, obtaining the operation status data of the intelligent excitation system, includes steps S11022 to S11024:

[0130] Step S11022, obtain the operation log data of the intelligent excitation system; the operation log data includes the operation status data to be processed.

[0131] Step S11024, eliminate the operation status data that does not meet the preset conditions from the operation status data to be processed to obtain the operation status data after elimination.

[0132] Step S120, input the health indicator data into the pre-trained health status evaluation model, and output the health status evaluation result of the intelligent excitation system under the excitation system health evaluation system; the health status evaluation result is determined by the pre-trained health status evaluation model according to the weight information corresponding to each health indicator and the health indicator data corresponding to each health indicator.

[0133] Among them, step S120, inputting the health indicator data into the pre-trained health status evaluation model, and outputting the health status evaluation result of the intelligent excitation system under the excitation system health evaluation system, includes steps S1202 to S1208:

[0134] Step S1202, convert the corresponding health indicator data into a score value according to the scoring rules corresponding to each health indicator.

[0135] Step S1204, obtain the product result between the weight information corresponding to each health indicator and the corresponding score value.

[0136] Step S1206: Obtain the sum of each product result, and generate a health status score and / or a health status level based on the sum of each product result.

[0137] Step S1208: Output a health status assessment result based on the health status score and / or the health status level.

[0138] Step S130: Determine the health status description information corresponding to the intelligent excitation system according to the health status assessment result.

[0139] Step S140: Query in a preset solution database for operation optimization suggestions that match the health status description information and the system operating conditions information of the intelligent excitation system.

[0140] Step S150: Construct a health status analysis report corresponding to the intelligent excitation system according to the health status description information and the operation optimization suggestions.

[0141] It should be noted that the specific limitations of the above steps can refer to the specific limitations of a method for evaluating the health status of an intelligent excitation system described above.

[0142] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limitation, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0143] Based on the same inventive concept, an embodiment of the present application also provides a health status assessment device for an intelligent excitation system for implementing the method for evaluating the health status of the intelligent excitation system involved above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the health status assessment device for an intelligent excitation system provided below can refer to the limitations of the method for evaluating the health status of an intelligent excitation system in the above text, and will not be repeated here.

[0144] In an exemplary embodiment, as Figure 4 shown, a health status assessment device for an intelligent excitation system is provided, including: an acquisition module 410, an evaluation module 420, an information determination module 430, a query module 440, and a construction module 450, where:

[0145] An acquisition module 410, configured to acquire health index data obtained by monitoring the health indexes of the intelligent excitation system; the health indexes are indexes used to evaluate the health status of the intelligent excitation system.

[0146] An evaluation module 420, configured to input the health index data into a pre-trained health status evaluation model, and output a health status evaluation result of the intelligent excitation system under the excitation system health evaluation system; the health status evaluation result is determined by the pre-trained health status evaluation model according to the weight information corresponding to each health index and the health index data corresponding to each health index.

[0147] An information determination module 430, configured to determine health status description information corresponding to the intelligent excitation system according to the health status evaluation result.

[0148] A query module 440, configured to query operation optimization suggestions that match the health status description information and the system working condition information of the intelligent excitation system in a preset solution database.

[0149] A construction module 450, configured to construct a health status analysis report corresponding to the intelligent excitation system according to the health status description information and the operation optimization suggestions.

[0150] In one embodiment, the acquisition module 410 is specifically configured to acquire the operation status data of the intelligent excitation system; acquire the data operation rules corresponding to each health index, and perform operation processing on the operation status data according to the data operation rules corresponding to each health index to obtain the health index data.

[0151] In one embodiment, the acquisition module 410 is specifically configured to acquire the operation log data of the intelligent excitation system; the operation log data includes operation status data to be processed; the operation status data that does not meet the preset conditions is removed from the operation status data to be processed to obtain the removed operation status data.

[0152] In one embodiment, the device further includes: a system construction module, configured to acquire the health indexes set for the intelligent excitation system; according to each health index, combine the states of the intelligent excitation system from the whole to the part in the form of hierarchical components to construct the excitation system health evaluation system; the excitation system health evaluation system includes the health indexes with corresponding hierarchical structures.

[0153] In one embodiment, the system construction module is further configured to use the pairwise comparison method to make pairwise judgments on the importance of each of the health indicators for health status monitoring, so as to obtain an index comparison matrix; and determine the weight information corresponding to each of the health indicators in the index comparison matrix by the eigenvalue method or the arithmetic mean method.

[0154] In one embodiment, the evaluation module 420 is specifically configured to convert the corresponding health indicator data into a score value according to the scoring rules corresponding to each of the health indicators; obtain the product results between the weight information corresponding to each of the health indicators and the corresponding score values; obtain the sum of each of the product results, and generate a health status score and / or a health status level according to the sum of each of the product results; and output the health status evaluation result according to the health status score and / or the health status level.

[0155] Each module in the above-mentioned health status evaluation device of the intelligent excitation system can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory in the computer device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to the above-mentioned modules.

[0156] In an exemplary embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 5 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the model parameter data of the pre-trained health status evaluation model. The input / output interface of the computer device is used for the processor to exchange information with external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. The computer program, when executed by the processor, implements a health status evaluation method for an intelligent excitation system.

[0157] Those skilled in the art can understand, Figure 5The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0158] In one embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0159] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0160] In one embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0161] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0162] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0163] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as within the scope recorded in the present application.

[0164] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for evaluating the health status of an intelligent excitation system, characterized in that: The method comprises: Acquire health indicator data obtained by monitoring the health indicators of the intelligent excitation system; the health indicator is an indicator used to evaluate the health status of the intelligent excitation system; The health indicator data is input into a pre-trained health status assessment model, and a health status assessment result of the intelligent excitation system under the excitation system health assessment system is output; the health status assessment result is determined by the pre-trained health status assessment model according to the weight information corresponding to each health indicator and the health indicator data corresponding to each health indicator; Determine health status description information corresponding to the intelligent excitation system according to the health status assessment result; Querying a preset solution database for operation optimization suggestions that match the health status description information and the system operating condition information of the intelligent excitation system; A health status analysis report corresponding to the intelligent excitation system is constructed based on the health status description information and the operation optimization suggestions.

2. The method according to claim 1, characterized in that The obtaining of health indicator data obtained by monitoring the health indicators of the intelligent excitation system includes: Acquiring operating status data of the intelligent excitation system; The data operation rules corresponding to each of the health indicators are obtained, and the operation status data is processed according to the data operation rules corresponding to each of the health indicators to obtain the health indicator data.

3. The method according to claim 2, characterized in that The obtaining of the operating status data of the intelligent excitation system comprises: Acquire operation log data of the intelligent excitation system; the operation log data includes operation status data to be processed; The running status data that does not meet the preset conditions are eliminated from the running status data to be processed to obtain the eliminated running status data.

4. The method according to claim 1, characterized in that: The method further comprises: Obtaining health indicators set for the intelligent excitation system; According to each of the health indicators, the states of the intelligent excitation system are combined from the whole to the parts of the intelligent excitation system in the form of hierarchical components to construct the excitation system health assessment system; the excitation system health assessment system includes the health indicators with corresponding hierarchical structures.

5. The method according to claim 1, characterized in that The method further comprises: Using the paired comparison method, the importance of each health indicator for health status monitoring is judged pairwise to obtain an indicator comparison matrix; The weight information corresponding to each health indicator in the indicator comparison matrix is ​​determined by the eigenvalue method or the arithmetic mean method.

6. The method according to claim 1, characterized in that The step of inputting the health indicator data into a pre-trained health status assessment model and outputting a health status assessment result of the intelligent excitation system under the excitation system health assessment system includes: According to the scoring rules corresponding to each of the health indicators, the corresponding health indicator data is converted into a scoring value; Obtaining the product result between the weight information corresponding to each of the health indicators and the corresponding score value; Obtaining the sum of the multiplication results, and generating a health status score and / or health status grade according to the sum of the multiplication results; The health status assessment result is output according to the health status score and / or the health status grade.

7. A health status assessment device for an intelligent excitation system, characterized in that: The device comprises: An acquisition module, used to acquire health indicator data obtained by monitoring the health indicators of the intelligent excitation system; the health indicator is an indicator used to evaluate the health status of the intelligent excitation system; An evaluation module is used to input the health indicator data into a pre-trained health status evaluation model, and output a health status evaluation result of the intelligent excitation system under the excitation system health evaluation system; the health status evaluation result is determined by the pre-trained health status evaluation model according to the weight information corresponding to each of the health indicators and the health indicator data corresponding to each of the health indicators; An information determination module, used to determine the health status description information corresponding to the intelligent excitation system according to the health status assessment result; A query module, used to query a preset solution database for operation optimization suggestions that match the health status description information and the system operating condition information of the intelligent excitation system; A construction module is used to construct a health status analysis report corresponding to the intelligent excitation system according to the health status description information and the operation optimization suggestions.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images