Power equipment data processing method based on Internet of Things

Abstract

The invention discloses a power equipment data processing method based on Internet of Things, which relates to the technical field of data analysis, and comprises the following steps: calculating a first average value, matching a manufacturer name, calculating quality indexes of components, counting damage interval time of the components, calculating component attenuation factors, and calculating a second average value, and constructing a state evaluation model of the to-be-tested equipment, performing grade division on the state of the to-be-tested equipment to obtain a state grade, and calculating a current state evaluation value to obtain a current state grade. According to the invention, through comprehensive analysis of fault records and environmental data, the downtime of the equipment is reduced, through establishment of a state evaluation model, the scientificity and rationality of equipment management are improved, and in combination with a component attenuation factor and a quality index, predictive maintenance of the equipment can be realized, and the risk of sudden faults is reduced; intelligent management of power equipment is realized, the management level is improved, and the labor cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a method for processing power equipment data based on the Internet of Things. Background Art

[0002] In recent years, smart power operation and maintenance has achieved unified management and remote control of equipment, saving manpower and time costs. It relies on key technologies such as the Internet of Things, big data analysis and cloud computing, and is applied to power equipment such as substations, transmission lines and distribution equipment to achieve full life cycle management.

[0003] Currently, a Chinese invention patent with publication number CN 116776258 A discloses a method and system for processing power equipment monitoring data. This method replaces abnormal data and missing data through the centroid mean, and performs data processing and analysis based on the monitoring data itself to ensure the device scenario adaptability of the data processing results. However, the related technology does not construct an evaluation model based on the natural characteristics of the area where the equipment to be tested is located, which is not conducive to the diversity and accuracy of the evaluation. It does not predict future faults based on current fault data, lacks foresight in the evaluation, and has certain limitations. Summary of the Invention

[0004] The technical problem solved by the present invention is that the related art does not construct an evaluation model based on the natural characteristics of the area where the equipment to be tested is located, which is not conducive to the diversity and accuracy of the evaluation, does not predict future failures based on current failure data, lacks foresight in the evaluation, and has certain limitations.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, a method for processing power equipment data based on the Internet of Things comprises the following steps:

[0006] Step S100, identifying the code of the device under test and obtaining relevant information of the device under test, wherein the relevant information of the device under test includes the geographical location, commissioning time, fault records and component models, wherein the fault records include the fault component model, fault type and fault occurrence time;

[0007] Step S200: Acquire environmental data based on geographic location, perform standardization on the environmental data, obtain component models, match corresponding fault records based on component models, classify fault occurrence times in the fault records into a first category based on fault type to obtain a first time point, classify the fault occurrence times into a second category based on quarterly time to obtain a second time point, obtain environmental data corresponding to the second time point, record the data as first data, calculate an average value of the first data, record the data as a first average value, match the manufacturer name based on the component model, calculate the quality index of the component based on the manufacturer name, calculate the damage interval of the component based on the commissioning time point and the fault records, and calculate the component attenuation factor based on the damage time;

[0008] Step S300, traversing component attenuation factors, calculating an average value of the attenuation factors of the components of the device under test, recording it as a second average value, and constructing a condition assessment model for the device under test, the condition assessment model including a first quarter condition assessment model, a second quarter condition assessment model, a third quarter condition assessment model, and a fourth quarter condition assessment model;

[0009] Step S400: Setting a state value threshold, comparing the state evaluation value with the state evaluation threshold, and classifying the state of the device under test according to the comparison result to obtain a state level;

[0010] Step S500, obtain the current quarter corresponding to the current time point, calculate the average value of the environmental data between the start time point of the current quarter to the current time point, record it as the third average value, call the status assessment model corresponding to the current quarter, calculate the current status assessment value, and obtain the current status level.

[0011] As a preferred solution of the method for processing power equipment data based on the Internet of Things described in the present invention, step S100 includes the following sub-steps:

[0012] Step S101, obtaining a coded image of the device under test;

[0013] Step S102: extracting the brightness value of the coded image of the device under test, and setting a first value and a second value as brightness thresholds, wherein the first value and the second value represent the minimum brightness and the maximum brightness at which the machine vision can recognize the coded image, and the first value is smaller than the second value;

[0014] Step S103: Compare the brightness value with a brightness threshold to obtain a first comparison result, and compensate the encoded image according to the first comparison result, wherein the first comparison result includes the brightness value being distributed between the first value and the second value, the brightness value being less than the first value, and the brightness value being greater than the second value;

[0015] When the first comparison result is that the brightness value is distributed between the first value and the second value, no compensation is performed on the encoded image;

[0016] When the first comparison result shows that the brightness value is less than the first value, continuously enhancing the brightness compensation for the encoded image until the first comparison result shows that the brightness value is distributed between the first value and the second value, and then stopping the continuous enhancing the brightness compensation;

[0017] When the first comparison result shows that the brightness is greater than the second value, continuously reducing the brightness compensation for the encoded image until the first comparison result shows that the brightness value is distributed between the first value and the second value, and then stopping the continuous reduction of the brightness compensation;

[0018] Step S104, connecting to the Internet to scan and identify the coded image to obtain relevant information of the device under test;

[0019] The relevant information of the device under test includes the geographical location, commissioning time, fault record and component model. The fault record includes the fault component model, fault type and fault occurrence time. The geographical location is represented by longitude and latitude.

[0020] As a preferred solution of the method for processing power equipment data based on the Internet of Things described in the present invention, step S200 includes the following sub-steps:

[0021] Step S201: obtaining a geographic location, accessing an environmental database, inputting the geographic location into the environmental database, matching environmental data corresponding to the geographic location, and normalizing the environmental data so that the environmental data is distributed between 0 and 1. The environmental data includes temperature, humidity, and light intensity.

[0022] Step S202: Obtain a component model, obtain a fault record corresponding to the component model, obtain a fault type and a corresponding fault occurrence time point corresponding to the fault record, wherein the fault type includes oil leakage, crack, corrosion, heat release fault, and abnormal vibration. The fault time points are first classified according to the fault type to obtain a first time point, wherein the first time point includes the time point of oil leakage, the time point of crack, the time point of corrosion, the time point of heat release fault, and the time point of abnormal vibration. The first time points are first numbered, and the first number is represented by X. i , where i is a natural number and i∈[1,5];

[0023] Step S203: obtain any first time point, classify the first time point into a second category according to the quarterly time, obtain a second time point, and assign a second number to the second time point, the second number being represented by X. ij, where j is a natural number and j∈[1,4], and the second time point includes the first quarter time point, the second quarter time point, the third quarter time point, and the fourth quarter time point;

[0024] Step S204: Obtain environmental data corresponding to the second time point, record it as first data, calculate the average value of the first data, take the reciprocal of the average value of the first data, and normalize the reciprocal of the average value of the first data so that it is distributed between 0 and 1, record it as the first average value, where the first average value includes a first average value of temperature, a first average value of humidity, and a first average value of light intensity;

[0025] Step S205, obtaining the component model, calling the parts database, inputting the component model into the parts database, and matching the manufacturer name corresponding to the component model;

[0026] Step S206: retrieve the manufacturer's after-sales database, input the component model into the after-sales database, and match the total number of sales and the total number of repairs corresponding to the production component model;

[0027] Step S207 , calculating the quality index of the component according to the manufacturer name, counting the damage interval of the component according to the commissioning time point and the fault record, and calculating the component attenuation factor according to the damage time.

[0028] As a preferred solution of the method for processing power equipment data based on the Internet of Things described in the present invention, the calculation logic of the quality index of the component includes:

[0029] Obtain the total number of units sold and the total number of units repaired, calculate the difference between the total number of units sold and the total number of units repaired, record it as the first quantity, calculate the ratio of the first quantity to the total number of units sold, record it as the first ratio, set the first ratio as the quality index of the component, traverse the quality index of each component, calculate the average value of the quality index, and set it as the quality average value.

[0030] As a preferred solution of the method for processing power equipment data based on the Internet of Things described in the present invention, the calculation logic of the component attenuation factor includes:

[0031] Obtain the commissioning time point and fault record of the component, obtain a second time point according to the fault record, set the commissioning time point as the initial time point, calculate the difference between the second time point and the initial time point, record it as the first time interval, calculate the difference between adjacent second time points, record it as the second time interval, where "adjacent" means two second time points in chronological order, label the first time interval and the second time interval, the label is a natural number, and the label becomes larger in chronological order, and the labels of the first time interval and the second time interval are represented by k1, k2, ...k n , where n is a natural number;

[0032] A curve in which the first time interval and the second time interval coexist is fitted by the least square method, recorded as an attenuation curve, coefficients of the attenuation curve are obtained, the coefficients are traversed, an average value of the coefficients is calculated, and the average value is set as the attenuation factor.

[0033] As a preferred solution of the method for processing power equipment data based on the Internet of Things described in the present invention, step S300 includes the following sub-steps:

[0034] Step S301, traversing the attenuation factors of each component of the device under test, calculating the average value of the attenuation factors of the component under test, taking the reciprocal of the average value of the component attenuation factors, and normalizing the reciprocal of the average value of the component attenuation factors so that it is distributed between 0 and 1, and recording it as a second average value;

[0035] Step S302: Construct a condition assessment model for the device under test based on the first average value, the second average value, and the quality average value. The condition assessment model includes a first quarter condition assessment model, a second quarter condition assessment model, a third quarter condition assessment model, and a fourth quarter condition assessment model. The output of the condition assessment model is a condition assessment value. The calculation expression of the condition assessment model is:

[0036]

[0037] Among them, y j is the status evaluation value of the jth quarter, P tj is the tth independent variable in the jth quarter, that is, the first mean of the jth quarter, the second mean of the jth quarter, or the mass mean of the jth quarter.

[0038] As a preferred solution of the method for processing power equipment data based on the Internet of Things described in the present invention, step S400 includes the following sub-steps:

[0039] Step S401: setting a third value and a fourth value as a set state value threshold, wherein the third value and the fourth value are obtained through big data analysis, and the third value is smaller than the fourth value;

[0040] Step S402: comparing the state evaluation value with the state evaluation threshold, and classifying the state of the device under test according to the comparison result to obtain a state level;

[0041] The comparison result includes the state evaluation value being less than or equal to the third value, the state evaluation value being greater than the third value and less than or equal to the fourth value, and the state evaluation value being greater than the fourth value, wherein a larger value indicates a better state of the device under test;

[0042] When the comparison result is that the status evaluation value is less than or equal to the third value, the status level is set to the first level; when the status evaluation value is greater than the third value and less than or equal to the fourth value, the status level is set to the second level; when the status evaluation value is greater than the fourth value, the status level is set to the third level.

[0043] As a preferred solution of the method for processing power equipment data based on the Internet of Things described in the present invention, step S500 includes the following sub-steps:

[0044] Step 501: Get the current time point and the quarter corresponding to the current time point, which is recorded as the current quarter;

[0045] Step S502, calculating the average value of the environmental data between the start time point of the current quarter and the current time point, and recording it as a third average value;

[0046] Step S503: Retrieve a status assessment model corresponding to the current quarter, input the third average value, the second average value, and the quality average value into the status assessment model, and obtain a current status assessment value;

[0047] Step S504: Compare the current state evaluation value with a state evaluation threshold to obtain a current state level, where the current state level includes a current first level, a current second level, and a current third level.

[0048] In a second aspect, the present invention provides an electronic device comprising a memory, a processor and computer-readable instructions stored in the memory. When the computer-readable instructions are executed by the processor, the steps of the cloud computing-based TCM internal medicine case data processing method as described above are executed.

[0049] In a third aspect, the present invention provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the power equipment data processing method based on the Internet of Things as described in any one of the above items are executed.

[0050] The beneficial effects of the present invention are as follows: through comprehensive analysis of fault records and environmental data, potential faults can be discovered and handled more promptly, reducing equipment downtime; by establishing a status assessment model, the scientificity and rationality of equipment management can be improved; combined with component attenuation factors and quality indexes, predictive maintenance of equipment can be achieved, reducing the risk of sudden failures; through the application of Internet of Things technology, intelligent management of power equipment can be achieved, management level can be improved, and labor costs can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic diagram of the basic flow of a method for processing power equipment data based on the Internet of Things provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0053] Example, see Figure 1 , as an embodiment of the present invention, provides a method for processing power equipment data based on the Internet of Things, comprising the following steps:

[0054] Step S100, identifying the code of the device under test and obtaining relevant information of the device under test, wherein the relevant information of the device under test includes the geographical location, commissioning time, fault records and component models, wherein the fault records include the fault component model, fault type and fault occurrence time;

[0055] Step S200: Acquire environmental data based on geographic location, perform standardization on the environmental data, obtain component models, match corresponding fault records based on component models, classify fault occurrence times in the fault records into a first category based on fault type to obtain a first time point, classify the fault occurrence times into a second category based on quarterly time to obtain a second time point, obtain environmental data corresponding to the second time point, record the data as first data, calculate an average value of the first data, record the data as a first average value, match the manufacturer name based on the component model, calculate the quality index of the component based on the manufacturer name, calculate the damage interval of the component based on the commissioning time point and the fault records, and calculate the component attenuation factor based on the damage time;

[0056] Step S300, traversing component attenuation factors, calculating an average value of the attenuation factors of the components of the device under test, recording it as a second average value, and constructing a condition assessment model for the device under test, the condition assessment model including a first quarter condition assessment model, a second quarter condition assessment model, a third quarter condition assessment model, and a fourth quarter condition assessment model;

[0057] Step S400: Setting a state value threshold, comparing the state evaluation value with the state evaluation threshold, and classifying the state of the device under test according to the comparison result to obtain a state level;

[0058] Step S500, obtain the current quarter corresponding to the current time point, calculate the average value of the environmental data between the start time point of the current quarter to the current time point, record it as the third average value, call the status assessment model corresponding to the current quarter, calculate the current status assessment value, and obtain the current status level.

[0059] Through comprehensive analysis of fault records and environmental data, the present invention can discover and handle potential faults more promptly, reduce equipment downtime, and improve the scientificity and rationality of equipment management by establishing a status assessment model. Combined with component attenuation factors and quality indexes, it can achieve predictive maintenance of equipment and reduce the risk of sudden failures. Through the application of Internet of Things technology, it can realize intelligent management of power equipment, improve management level, and reduce labor costs.

[0060] The step S100 includes the following sub-steps:

[0061] Step S101, obtaining a coded image of the device under test;

[0062] Step S102: extracting the brightness value of the coded image of the device under test, and setting a first value and a second value as brightness thresholds, wherein the first value and the second value represent the minimum brightness and the maximum brightness at which the machine vision can recognize the coded image, and the first value is smaller than the second value;

[0063] Step S103: Compare the brightness value with a brightness threshold to obtain a first comparison result, and compensate the encoded image according to the first comparison result, wherein the first comparison result includes the brightness value being distributed between the first value and the second value, the brightness value being less than the first value, and the brightness value being greater than the second value;

[0064] When the first comparison result is that the brightness value is distributed between the first value and the second value, no compensation is performed on the encoded image;

[0065] When the first comparison result shows that the brightness value is less than the first value, continuously enhancing the brightness compensation for the encoded image until the first comparison result shows that the brightness value is distributed between the first value and the second value, and then stopping the continuous enhancing the brightness compensation;

[0066] When the first comparison result shows that the brightness is greater than the second value, continuously reducing the brightness compensation for the encoded image until the first comparison result shows that the brightness value is distributed between the first value and the second value, and then stopping the continuous reduction of the brightness compensation;

[0067] Step S104, connecting to the Internet to scan and identify the coded image to obtain relevant information of the device under test;

[0068] The relevant information of the device under test includes the geographical location, commissioning time, fault record and component model. The fault record includes the fault component model, fault type and fault occurrence time. The geographical location is represented by longitude and latitude.

[0069] In specific implementation, by performing brightness compensation on the coded image, it is ensured that the image is within the brightness range that can be accurately recognized by the machine vision system, thereby improving the recognition accuracy and being able to adapt to different ambient light conditions. By automatically adjusting the brightness compensation, the system can work stably in different lighting environments. Through network scanning and recognition, detailed information of the equipment under test can be obtained, including geographical location, commissioning time and fault records, etc. Through automated image processing and recognition, the workload of manual inspection and data recording is reduced, and the efficiency of power equipment operation and maintenance is improved.

[0070] The step S200 includes the following sub-steps:

[0071] Step S201: obtaining a geographic location, accessing an environmental database, inputting the geographic location into the environmental database, matching environmental data corresponding to the geographic location, and normalizing the environmental data so that the environmental data is distributed between 0 and 1. The environmental data includes temperature, humidity, and light intensity.

[0072] Step S202: Obtain a component model, obtain a fault record corresponding to the component model, obtain a fault type and a corresponding fault occurrence time point corresponding to the fault record, wherein the fault type includes oil leakage, crack, corrosion, heat release fault, and abnormal vibration. The fault time points are first classified according to the fault type to obtain a first time point, wherein the first time point includes the time point of oil leakage, the time point of crack, the time point of corrosion, the time point of heat release fault, and the time point of abnormal vibration. The first time points are first numbered, and the first number is represented by X. i , where i is a natural number and i∈[1,5];

[0073] Step S203: obtain any first time point, classify the first time point into a second category according to the quarterly time, obtain a second time point, and assign a second number to the second time point, the second number being represented by X. ij, where j is a natural number and j∈[1,4], and the second time point includes the first quarter time point, the second quarter time point, the third quarter time point, and the fourth quarter time point;

[0074] Step S204: Obtain environmental data corresponding to the second time point, record it as first data, calculate the average value of the first data, take the reciprocal of the average value of the first data, and normalize the reciprocal of the average value of the first data so that it is distributed between 0 and 1, record it as the first average value, where the first average value includes a first average value of temperature, a first average value of humidity, and a first average value of light intensity;

[0075] Step S205, obtaining the component model, calling the parts database, inputting the component model into the parts database, and matching the manufacturer name corresponding to the component model;

[0076] Step S206: retrieve the manufacturer's after-sales database, input the component model into the after-sales database, and match the total number of sales and the total number of repairs corresponding to the production component model;

[0077] Step S207 , calculating the quality index of the component according to the manufacturer name, counting the damage interval of the component according to the commissioning time point and the fault record, and calculating the component attenuation factor according to the damage time.

[0078] The calculation logic of the quality index of the component includes:

[0079] Obtain the total number of units sold and the total number of units repaired, calculate the difference between the total number of units sold and the total number of units repaired, record it as the first quantity, calculate the ratio of the first quantity to the total number of units sold, record it as the first ratio, set the first ratio as the quality index of the component, traverse the quality index of each component, calculate the average value of the quality index, and set it as the quality average value.

[0080] In specific implementation, through automated data analysis, potential problems and failure trends of equipment can be quickly identified, so that maintenance can be carried out in advance and unexpected downtime can be reduced. By analyzing fault records and environmental data, maintenance resources can be allocated more reasonably, and priority can be given to equipment or components that are more likely to fail. By monitoring environmental data and fault records, the operating status of the equipment can be better understood, so that corresponding measures can be taken to improve equipment reliability.

[0081] The calculation logic of the component attenuation factor includes:

[0082] Obtain the commissioning time point and fault record of the component, obtain a second time point according to the fault record, set the commissioning time point as the initial time point, calculate the difference between the second time point and the initial time point, record it as the first time interval, calculate the difference between adjacent second time points, record it as the second time interval, where "adjacent" means two second time points in chronological order, label the first time interval and the second time interval, the label is a natural number, and the label becomes larger in chronological order, and the labels of the first time interval and the second time interval are represented by k1, k2, ...k n , where n is a natural number;

[0083] A curve in which the first time interval and the second time interval coexist is fitted by the least square method, recorded as an attenuation curve, coefficients of the attenuation curve are obtained, the coefficients are traversed, an average value of the coefficients is calculated, and the average value is set as the attenuation factor.

[0084] In specific implementation, by calculating the attenuation factor, the failure trend and remaining service life of components are predicted, thereby achieving predictive maintenance and reducing unexpected downtime and repair costs. The attenuation factor provides a quantitative risk assessment tool, which makes the aging of equipment more concrete. By understanding the aging rate of components, the best economic benefits can be achieved. By identifying and maintaining high-risk components, the reliability and stability of the entire power system can be improved.

[0085] The step S300 includes the following sub-steps:

[0086] Step S301, traversing the attenuation factors of each component of the device under test, calculating the average value of the attenuation factors of the component under test, taking the reciprocal of the average value of the component attenuation factors, and normalizing the reciprocal of the average value of the component attenuation factors so that it is distributed between 0 and 1, and recording it as a second average value;

[0087] Step S302: Construct a condition assessment model for the device under test based on the first average value, the second average value, and the quality average value. The condition assessment model includes a first quarter condition assessment model, a second quarter condition assessment model, a third quarter condition assessment model, and a fourth quarter condition assessment model. The output of the condition assessment model is a condition assessment value. The calculation expression of the condition assessment model is:

[0088]

[0089] Among them, y j is the status evaluation value of the jth quarter, P tj is the tth independent variable in the jth quarter, that is, the first mean of the jth quarter, the second mean of the jth quarter, or the mass mean of the jth quarter.

[0090] In specific implementation, by combining environmental data, component attenuation factors and quality indexes, a comprehensive equipment status assessment model is provided to more accurately reflect the actual operating status of the equipment. The assessment model is divided into four quarters, and more targeted status assessments can be carried out according to the environmental changes and equipment operation characteristics of different seasons.

[0091] The step S400 includes the following sub-steps:

[0092] Step S401: setting a third value and a fourth value as a set state value threshold, wherein the third value and the fourth value are obtained through big data analysis, and the third value is smaller than the fourth value;

[0093] Step S402: comparing the state evaluation value with the state evaluation threshold, and classifying the state of the device under test according to the comparison result to obtain a state level;

[0094] The comparison result includes the state evaluation value being less than or equal to the third value, the state evaluation value being greater than the third value and less than or equal to the fourth value, and the state evaluation value being greater than the fourth value, wherein a larger value indicates a better state of the device under test;

[0095] When the comparison result is that the status evaluation value is less than or equal to the third value, the status level is set to the first level; when the status evaluation value is greater than the third value and less than or equal to the fourth value, the status level is set to the second level; when the status evaluation value is greater than the fourth value, the status level is set to the third level.

[0096] In specific implementation, by setting specific thresholds and level divisions, the status of the equipment can be clearly evaluated and expressed, which is easy to understand and communicate. The status level provides clear guidance for the maintenance team, and different maintenance strategies are adopted according to the level of the equipment. For example, for the first level equipment, emergency repairs may be required, while for the third level equipment, only routine inspections may be required. Through the status level division, maintenance resources can be allocated more effectively, and equipment in poor condition can be given priority, thereby improving the efficiency and effectiveness of maintenance work.

[0097] The step S500 includes the following sub-steps:

[0098] Step 501: Get the current time point and the quarter corresponding to the current time point, which is recorded as the current quarter;

[0099] Step S502, calculating the average value of the environmental data between the start time point of the current quarter and the current time point, and recording it as a third average value;

[0100] Step S503: Retrieve a status assessment model corresponding to the current quarter, input the third average value, the second average value, and the quality average value into the status assessment model, and obtain a current status assessment value;

[0101] Step S504: Compare the current state evaluation value with a state evaluation threshold to obtain a current state level, where the current state level includes a current first level, a current second level, and a current third level.

[0102] Through comprehensive analysis of fault records and environmental data, the present invention can discover and handle potential faults more promptly, reduce equipment downtime, and improve the scientificity and rationality of equipment management by establishing a status assessment model. Combined with component attenuation factors and quality indexes, it can achieve predictive maintenance of equipment and reduce the risk of sudden failures. Through the application of Internet of Things technology, it can realize intelligent management of power equipment, improve management level, and reduce labor costs.

[0103] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium may be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0104] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for processing power equipment data based on the Internet of Things, characterized in that: The following steps are involved: Step S100, identifying the code of the device under test and obtaining relevant information of the device under test, wherein the relevant information of the device under test includes the geographical location, commissioning time, fault records and component models, wherein the fault records include the fault component model, fault type and fault occurrence time; Step S200: Acquire environmental data based on geographic location, perform standardization on the environmental data, obtain component models, match corresponding fault records based on component models, classify fault occurrence times in the fault records into a first category based on fault type to obtain a first time point, classify the fault occurrence times into a second category based on quarterly time to obtain a second time point, obtain environmental data corresponding to the second time point, record the data as first data, calculate an average value of the first data, record the data as a first average value, match the manufacturer name based on the component model, calculate the quality index of the component based on the manufacturer name, calculate the damage interval of the component based on the commissioning time point and the fault records, and calculate the component attenuation factor based on the damage time; Step S300, traversing component attenuation factors, calculating an average value of the attenuation factors of the components of the device under test, recording it as a second average value, and constructing a condition assessment model for the device under test, the condition assessment model including a first quarter condition assessment model, a second quarter condition assessment model, a third quarter condition assessment model, and a fourth quarter condition assessment model; Step S400: Setting a state value threshold, comparing the state evaluation value with the state evaluation threshold, and classifying the state of the device under test according to the comparison result to obtain a state level; Step S500, obtain the current quarter corresponding to the current time point, calculate the average value of the environmental data between the start time point of the current quarter to the current time point, record it as the third average value, call the status assessment model corresponding to the current quarter, calculate the current status assessment value, and obtain the current status level.

2. The method for processing power equipment data based on the Internet of Things according to claim 1, wherein: The step S100 includes the following sub-steps: Step S101, obtaining a coded image of the device under test; Step S102: extracting the brightness value of the coded image of the device under test, and setting a first value and a second value as brightness thresholds, wherein the first value and the second value represent the minimum brightness and the maximum brightness at which the machine vision can recognize the coded image, and the first value is smaller than the second value; Step S103: Compare the brightness value with a brightness threshold to obtain a first comparison result, and compensate the encoded image according to the first comparison result, wherein the first comparison result includes the brightness value being distributed between the first value and the second value, the brightness value being less than the first value, and the brightness value being greater than the second value; When the first comparison result is that the brightness value is distributed between the first value and the second value, no compensation is performed on the encoded image; When the first comparison result shows that the brightness value is less than the first value, continuously enhancing the brightness compensation for the encoded image until the first comparison result shows that the brightness value is distributed between the first value and the second value, and then stopping the continuous enhancing the brightness compensation; When the first comparison result shows that the brightness is greater than the second value, continuously reducing the brightness compensation for the encoded image until the first comparison result shows that the brightness value is distributed between the first value and the second value, and then stopping the continuous reduction of the brightness compensation; Step S104, connecting to the Internet to scan and identify the coded image to obtain relevant information of the device under test; The relevant information of the device under test includes the geographical location, commissioning time, fault record and component model. The fault record includes the fault component model, fault type and fault occurrence time. The geographical location is represented by longitude and latitude.

3. The method for processing power equipment data based on the Internet of Things according to claim 1, wherein: The step S200 includes the following sub-steps: Step S201: obtaining a geographic location, accessing an environmental database, inputting the geographic location into the environmental database, matching environmental data corresponding to the geographic location, and normalizing the environmental data so that the environmental data is distributed between 0 and 1. The environmental data includes temperature, humidity, and light intensity. Step S202: Obtain a component model, obtain a fault record corresponding to the component model, obtain a fault type and a corresponding fault occurrence time point corresponding to the fault record, wherein the fault type includes oil leakage, crack, corrosion, heat release fault, and abnormal vibration. The fault time points are first classified according to the fault type to obtain a first time point, wherein the first time point includes the time point of oil leakage, the time point of crack, the time point of corrosion, the time point of heat release fault, and the time point of abnormal vibration. The first time points are first numbered, and the first number is represented by X. i , where i is a natural number and i∈[1,5]; Step S203: obtain any first time point, classify the first time point into a second category according to the quarterly time, obtain a second time point, and assign a second number to the second time point, the second number being represented by X. ij , where j is a natural number and j∈[1,4], and the second time point includes the first quarter time point, the second quarter time point, the third quarter time point, and the fourth quarter time point; Step S204: Obtain environmental data corresponding to the second time point, record it as first data, calculate the average value of the first data, take the reciprocal of the average value of the first data, and normalize the reciprocal of the average value of the first data so that it is distributed between 0 and 1, record it as the first average value, where the first average value includes a first average value of temperature, a first average value of humidity, and a first average value of light intensity; Step S205, obtaining the component model, calling the parts database, inputting the component model into the parts database, and matching the manufacturer name corresponding to the component model; Step S206: retrieve the manufacturer's after-sales database, input the component model into the after-sales database, and match the total number of sales and the total number of repairs corresponding to the production component model; Step S207 , calculating the quality index of the component according to the manufacturer name, counting the damage interval of the component according to the commissioning time point and the fault record, and calculating the component attenuation factor according to the damage time.

4. The method for processing power equipment data based on the Internet of Things according to claim 3, wherein: The calculation logic of the quality index of the component includes: Obtain the total number of units sold and the total number of units repaired, calculate the difference between the total number of units sold and the total number of units repaired, record it as the first quantity, calculate the ratio of the first quantity to the total number of units sold, record it as the first ratio, set the first ratio as the quality index of the component, traverse the quality index of each component, calculate the average value of the quality index, and set it as the quality average value.

5. The method for processing power equipment data based on the Internet of Things according to claim 4, characterized in that: The calculation logic of the component attenuation factor includes: Obtain the commissioning time point and fault record of the component, obtain a second time point according to the fault record, set the commissioning time point as the initial time point, calculate the difference between the second time point and the initial time point, record it as the first time interval, calculate the difference between adjacent second time points, record it as the second time interval, where "adjacent" means two second time points in chronological order, label the first time interval and the second time interval, the label is a natural number, and the label becomes larger in chronological order, and the labels of the first time interval and the second time interval are represented by k1, k2, ...k n , where n is a natural number; A curve in which the first time interval and the second time interval coexist is fitted by the least square method, recorded as an attenuation curve, coefficients of the attenuation curve are obtained, the coefficients are traversed, an average value of the coefficients is calculated, and the average value is set as the attenuation factor.

6. The method for processing power equipment data based on the Internet of Things according to claim 1, wherein: The step S300 includes the following sub-steps: Step S301, traversing the attenuation factors of each component of the device under test, calculating the average value of the attenuation factors of the component under test, taking the reciprocal of the average value of the component attenuation factors, and normalizing the reciprocal of the average value of the component attenuation factors so that it is distributed between 0 and 1, and recording it as a second average value; Step S302: Construct a condition assessment model for the device under test based on the first average value, the second average value, and the quality average value. The condition assessment model includes a first quarter condition assessment model, a second quarter condition assessment model, a third quarter condition assessment model, and a fourth quarter condition assessment model. The output of the condition assessment model is a condition assessment value. The calculation expression of the condition assessment model is: Among them, y j is the status evaluation value of the jth quarter, P tj is the tth independent variable in the jth quarter, that is, the first mean of the jth quarter, the second mean of the jth quarter, or the mass mean of the jth quarter.

7. The method for processing power equipment data based on the Internet of Things according to claim 1, wherein: The step S400 includes the following sub-steps: Step S401: setting a third value and a fourth value as a set state value threshold, wherein the third value and the fourth value are obtained through big data analysis, and the third value is smaller than the fourth value; Step S402: comparing the state evaluation value with the state evaluation threshold, and classifying the state of the device under test according to the comparison result to obtain a state level; The comparison result includes the state evaluation value being less than or equal to the third value, the state evaluation value being greater than the third value and less than or equal to the fourth value, and the state evaluation value being greater than the fourth value, wherein a larger value indicates a better state of the device under test; When the comparison result is that the status evaluation value is less than or equal to the third value, the status level is set to the first level; when the status evaluation value is greater than the third value and less than or equal to the fourth value, the status level is set to the second level; when the status evaluation value is greater than the fourth value, the status level is set to the third level.

8. The method for processing power equipment data based on the Internet of Things according to claim 1, wherein: The step S500 includes the following sub-steps: Step 501: Get the current time point and the quarter corresponding to the current time point, which is recorded as the current quarter; Step S502, calculating the average value of the environmental data between the start time point of the current quarter and the current time point, and recording it as a third average value; Step S503: Retrieve a status assessment model corresponding to the current quarter, input the third average value, the second average value, and the quality average value into the status assessment model, and obtain a current status assessment value; Step S504: Compare the current state evaluation value with a state evaluation threshold to obtain a current state level, where the current state level includes a current first level, a current second level, and a current third level.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for processing power equipment data based on the Internet of Things as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the power equipment data processing method based on the Internet of Things as described in any one of claims 1 to 8.

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