CN116990619A - Intelligent monitoring method of mine frequency conversion integrated machine - Google Patents
Intelligent monitoring method of mine frequency conversion integrated machine Download PDFInfo
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Abstract
The application discloses an intelligent monitoring method of a mine frequency conversion all-in-one machine, which belongs to the field of intelligent manufacturing and comprises the following steps: the method comprises the steps of (1) constructing a use scene of the interactive frequency conversion integrated machine and constructing an N-level working condition; configuring a calibration environment and collecting historical control data of the integrated machine; performing data matching on the historical control data to construct reference unit energy consumption under each level of working condition; performing N-level working condition environmental impact evaluation of the frequency conversion integrated machine, and constructing environmental impact correlation so as to determine a unit energy consumption abnormal tolerance threshold value with reference unit energy consumption; constructing a monitoring data set for the frequency conversion integrated mechanism; matching unit energy consumption abnormal tolerance threshold values, and comparing unit energy consumption to generate a comparison result; and performing intelligent monitoring management of the frequency conversion integrated machine according to the comparison result. The application solves the technical problem of high energy consumption of the integrated machine caused by the fact that the frequency conversion integrated machine is not fine in monitoring management in the prior art, and achieves the technical effect of finely monitoring and controlling the frequency conversion integrated machine so as to reduce the energy consumption of the integrated machine.
Description
Technical Field
The application relates to the field of intelligent manufacturing, in particular to an intelligent monitoring method of a mine frequency conversion integrated machine.
Background
Along with the development of society, the frequency conversion all-in-one machine is widely used in the industrial and civil fields as a high-efficiency energy-saving product. However, in the use process of the existing variable frequency integrated machine, the setting of the operation parameters of the existing variable frequency integrated machine is generally configured according to a preset curve when leaving a factory, and cannot be monitored and adjusted in real time according to actual working conditions, so that the operation parameters of the existing variable frequency integrated machine cannot be optimized in real time, and the integrated machine has high energy consumption.
Disclosure of Invention
The application provides an intelligent monitoring method of a mine frequency conversion integrated machine, and aims to solve the technical problem that in the prior art, the frequency conversion integrated machine is not fine in monitoring management, so that the integrated machine can be powered up.
In view of the problems, the application provides an intelligent monitoring method of a mine frequency conversion all-in-one machine.
The application discloses a first aspect, which provides an intelligent monitoring method of a mine frequency conversion all-in-one machine, comprising the following steps: the method comprises the steps that the use scene of the interactive frequency conversion integrated machine is used, working condition division is conducted based on the use scene, and an N-level working condition is constructed; configuring a calibration environment, and collecting historical control data of the variable frequency integrated machine under the calibration environment, wherein the historical control data comprises mapping data of coal conveying capacity and energy consumption; performing data matching on the historical control data according to the N-level working conditions, and constructing reference unit energy consumption under each-level working condition; performing N-level working condition environmental impact evaluation of the frequency conversion integrated machine through big data, constructing environmental impact association, and determining a unit energy consumption abnormal tolerance threshold through the environmental impact association and the reference unit energy consumption; continuously monitoring the frequency conversion integrated machine to construct a monitoring data set, wherein the monitoring data set comprises an energy consumption data set and mapped working conditions and environment identifiers; matching unit energy consumption abnormal tolerance threshold values, and comparing unit energy consumption of the monitoring data set to generate a comparison result; and performing intelligent monitoring management of the frequency conversion integrated machine according to the comparison result.
In another aspect of the disclosure, an intelligent monitoring system of a mine frequency conversion integrated machine is provided, the system comprises: the method comprises the steps that a working condition grading module is used for interacting a use scene of the frequency conversion integrated machine, and working condition division is carried out based on the use scene to construct an N-level working condition; the historical data acquisition module is used for configuring a calibration environment and acquiring historical control data of the frequency conversion integrated machine under the calibration environment, wherein the historical control data comprises mapping data of coal conveying amount and energy consumption; the working condition data matching module is used for carrying out data matching on the historical control data according to the N-level working conditions and constructing the reference unit energy consumption under each level of working conditions; the environment influence evaluation module is used for performing N-level working condition environment influence evaluation of the frequency conversion integrated machine through big data, constructing environment influence association, and determining a unit energy consumption abnormal tolerance threshold through the environment influence association and the reference unit energy consumption; the monitoring data construction module is used for continuously monitoring the frequency conversion integrated machine and constructing a monitoring data set, wherein the monitoring data set comprises an energy consumption data set and mapped working conditions and environment identifiers; the unit energy consumption comparison module is used for matching the unit energy consumption abnormal tolerance threshold value, and carrying out unit energy consumption comparison on the monitoring data set to generate a comparison result; and the intelligent monitoring management module is used for carrying out intelligent monitoring management of the frequency conversion integrated machine according to the comparison result.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
the frequency conversion integrated machine-based use scene is adopted to divide a plurality of working conditions; collecting historical operation data under a calibration environment, and constructing an energy consumption benchmark under each working condition; evaluating the influence of environmental factors under various working conditions by utilizing a big data technology, and establishing an environmental influence correlation model; determining an abnormal tolerance threshold value of unit energy consumption through an environmental impact correlation model and an energy consumption reference; monitoring operation data of the variable frequency integrated machine in real time, comparing the operation data with a reference and a threshold value, and detecting abnormal conditions of an operation state; according to the technical scheme of state monitoring and early warning management according to the detection result, the technical problem that the frequency conversion integrated machine is not fine in monitoring management in the prior art, so that the integrated machine can be powered up is solved, and the technical effect of finely monitoring, controlling and frequency conversion integrated machine is achieved, so that the integrated machine energy consumption is reduced.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
Fig. 1 is a schematic diagram of a possible flow chart of an intelligent monitoring method of a mine frequency conversion integrated machine according to an embodiment of the application;
fig. 2 is a schematic diagram of a possible flow chart of intelligent monitoring and management of a frequency conversion integrated machine in an intelligent monitoring method of the frequency conversion integrated machine in a mine according to an embodiment of the application;
fig. 3 is a schematic flow chart of a possible abnormal evaluation of energy consumption in an intelligent monitoring method of a mine frequency conversion integrated machine according to an embodiment of the present application;
fig. 4 is a schematic diagram of a possible structure of an intelligent monitoring system of a mine frequency conversion integrated machine according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a working condition grading module 11, a historical data acquisition module 12, a working condition data matching module 13, an environmental impact evaluation module 14, a monitoring data construction module 15, a unit energy consumption comparison module 16 and an intelligent monitoring management module 17.
Detailed Description
The technical scheme provided by the application has the following overall thought:
the embodiment of the application provides an intelligent monitoring method of a mine frequency conversion all-in-one machine. Firstly, analyzing and researching the use scene of the frequency conversion integrated machine, dividing a plurality of working conditions of equipment, and establishing a basis of accurate reference. And collecting a large amount of historical operation data in a calibration environment, and constructing an energy consumption standard under each working condition, wherein the energy consumption standard is a judgment standard for detecting abnormal states. Meanwhile, the sensitivity degree of each working condition to environmental factors is evaluated by using a big data technology, and the establishment of an environmental impact correlation model is a key for eliminating external environmental impact. And then, determining the normal variation range and the abnormal tolerance threshold of the unit energy consumption through the environmental impact correlation model and the energy consumption standard. And then, monitoring the operation data of the frequency conversion integrated machine in real time, comparing the operation data with a reference and a threshold value, and accurately detecting the abnormal condition of the operation state. And finally, carrying out state monitoring and early warning management on the operation of the frequency conversion integrated machine according to the detection result, and furthest playing the stability and economy of the equipment.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Embodiment one:
as shown in fig. 1, an embodiment of the present application provides an intelligent monitoring method for a mine frequency conversion integrated machine, where the method includes:
step S1000: the method comprises the steps of performing operation scene division on the interactive frequency conversion integrated machine, and constructing N-level operation conditions based on the operation scene;
specifically, a use scene of the frequency conversion integrated machine, such as frequency conversion control in the coal mining process of the underground coal mining machine of the mechanical anchor stope, is obtained by dividing the use scene into different working conditions, and N-level working conditions are obtained, wherein N is an integer greater than or equal to 2.
Firstly, a user of the frequency conversion integrated machine is investigated by providing a questionnaire form, and information such as equipment model, function positioning of the frequency conversion integrated machine in equipment, working environment conditions, general control parameter range and the like is inquired. Then, based on the obtained usage scenario information, the working states of the equipment, such as a starting state, an overload state and a normal working state, are divided, wherein the starting state corresponds to an idle starting working condition, the overload state corresponds to an overload working condition, the normal working state corresponds to a normal working condition, and then the N-level working condition is obtained. Through the service scenario of the interactive frequency conversion all-in-one, equipment operation information is collected, the working condition of the frequency conversion all-in-one is reasonably divided, and a foundation is laid for follow-up intelligent monitoring and management.
Step S2000: configuring a calibration environment, and collecting historical control data of the variable frequency integrated machine in the calibration environment, wherein the historical control data comprises mapping data of coal conveying capacity and energy consumption;
specifically, firstly, historical environmental data of an actual working environment of the frequency conversion integrated machine, such as temperature, humidity, altitude and the like, are collected, and average environmental states are obtained by calculating the average value of all environmental parameters. Then, a floating value of each environmental parameter is determined from the historical environmental data, and a floating threshold value of each environmental parameter is determined by an expert. The average environmental state is then combined with the floating threshold as the calibrated environment.
The frequency conversion all-in-one machine is provided with a current sensor and a voltage sensor, current and voltage data are measured and recorded in real time, and the workload of the frequency conversion all-in-one machine is recorded. In a historical control database of the frequency conversion integrated machine, control data under a calibration environment is searched, control parameters of the frequency conversion integrated machine under the calibration environment, such as output frequency, voltage and the like, are obtained, and input power can be obtained through power calculation and used as energy consumption data; and simultaneously, working parameters such as the rotating speed, the feeding amount and the like of the corresponding coal feeder under the control parameters are obtained from a historical control database, and the coal conveying amount in unit time is obtained and used as the data of the working amount. And then, converting the collected control parameters and the working parameters of the coal feeder into standardized data formats, and establishing a corresponding relation between the coal conveying amount and the energy consumption as historical control data.
By configuring a calibration environment, control data in the actual running process of the equipment is acquired, the running characteristics of the equipment are acquired to the maximum extent, and a reliable control data mapping model is established, so that the method is an important basis for subsequent monitoring and management.
Step S3000: performing data matching on the historical control data according to the N-level working conditions, and constructing reference unit energy consumption under each-level working condition;
specifically, according to the divided N-level working conditions, collected historical control data are matched to different levels of working conditions, and the standard unit energy consumption of the frequency conversion all-in-one machine under each working condition is obtained.
The historical control data contains operation data under a plurality of working conditions, and a data set corresponding to each working condition needs to be screened out from the historical control data so as to accurately detect different working conditions. Firstly, splicing historical control data according to an integrated machine to obtain a plurality of complete control data chains, wherein the complete control data chains comprise complete data from starting up to stopping up of the integrated machine; then, marking all complete control data chains according to the N-level working conditions, and dividing the control data chains according to the marking result to obtain data chain dividing sections; and then, carrying out cluster analysis on the obtained data chain segments, and dividing similar data into the same working conditions, so that each working condition has a corresponding control data set, and each data set comprises a plurality of actual frequency conversion all-in-one machine operation records under the working condition of the stage. And then, calculating the energy consumption values of all the operation records of each stage of working condition, sequencing the energy consumption values of the operation records, taking the middle median value as the reference unit energy consumption, comparing with the real-time monitoring data, and judging whether the energy consumption of the frequency conversion all-in-one machine is abnormal or not.
Step S4000: performing N-level working condition environmental impact evaluation of the frequency conversion integrated machine through big data, constructing environmental impact association, and determining a unit energy consumption abnormal tolerance threshold through the environmental impact association and the reference unit energy consumption;
specifically, environmental impact evaluation refers to analysis of the impact of environmental condition changes on energy consumption under different working conditions, such as factors of temperature, humidity, power supply voltage fluctuation and the like. Firstly, operation big data of the frequency conversion all-in-one machine are obtained through big data technology, wherein the operation big data comprise operation parameters, output conditions and corresponding environment parameters of the frequency conversion all-in-one machine, such as temperature, humidity, power supply voltage and the like. Then, preprocessing the data, removing abnormal points and normalizing. Then, each working condition is analyzed, and key environment parameters, such as temperature and humidity in normal working conditions, are determined as main influencing factors. Then, an environmental impact evaluation model under each working condition, namely a regression model of the environmental parameters and the output efficiency of the frequency conversion integrated machine is constructed through a random forest algorithm, for example, under normal working conditions, the energy consumption is increased by 2% when the temperature is increased by 1 ℃. And then, according to the reference unit energy consumption and the influence prediction result of the environment model, calculating the upper limit and the lower limit of the unit energy consumption abnormal threshold under each working condition, and realizing the environment influence correlation. Then, determining abnormal tolerance thresholds of unit energy consumption under each working condition according to the upper limit and the lower limit of the abnormal threshold of unit energy consumption in the environmental impact association and the standard unit energy consumption, for example, normal working conditions: at a temperature of 35 ℃, the standard unit energy consumption is 1.5 kilowatt-hours/ton, and according to an environmental impact model, when the temperature rises to 36 ℃, the corresponding unit energy consumption abnormal threshold upper limit is 1.53 kilowatt-hours/ton. And by establishing an environmental impact correlation model, the unit energy consumption abnormal threshold is dynamically determined, so that accurate energy consumption monitoring and early warning are realized.
Step S5000: continuously monitoring the frequency conversion integrated machine to construct a monitoring data set, wherein the monitoring data set comprises an energy consumption data set, mapped working conditions and environment identifiers;
specifically, the operation state of the frequency conversion integrated machine is continuously monitored and recorded, a large amount of data is obtained, and a monitoring data set is constructed according to the operation state, wherein the monitoring data set comprises an energy consumption data set, namely, the energy consumption of the frequency conversion integrated machine is recorded. Meanwhile, the method also comprises mapped working conditions and environment identifications. The working condition identifier is a mark for the working condition of the frequency conversion integrated machine; the environment identifier is used for identifying the environment condition, such as temperature, humidity and the like, of the frequency conversion all-in-one machine. The energy consumption data is associated with the working condition and the environment identifier, so that the running condition of the frequency conversion integrated machine can be more comprehensively known.
Firstly, sensors are installed in the frequency conversion all-in-one machine and used for monitoring key parameters such as current, voltage, frequency and the like in real time, and the sensors transmit collected data to a data processing system through a physical interface or a wireless communication mode. And then, the data processing system is connected with the sensor through a data interface, receives the operation data of the frequency conversion integrated machine in real time, and processes and analyzes the data to obtain the mapping relation between the energy consumption data and the working condition and environment identification. Then, the data processing system extracts information related to energy consumption from the data acquired by the sensor, such as power consumption and energy use condition, and sorts and stores the data to form an energy consumption data set. And then, according to the operation data of the frequency conversion integrated machine, the working condition and the environment identification are mapped with the actual operation condition by analyzing the change of the working condition and the environment parameter.
Through the operation data that continuously monitors frequency conversion all-in-one to with energy consumption data and operating mode and environment identification correlate, constitute complete monitoring dataset, provide the comprehensive understanding to frequency conversion all-in-one running state, help the accurate energy consumption monitoring that carries on, thereby improve frequency conversion all-in-one's efficiency and reliability.
Step S6000: matching unit energy consumption abnormal tolerance threshold values, and comparing unit energy consumption of the monitoring data set to generate a comparison result;
specifically, firstly, the monitoring data are divided according to different working conditions based on the working condition identification, and energy consumption data of the variable frequency integrated machine at different time points and corresponding working condition identifications are obtained, wherein the energy consumption data are energy consumption conditions of the variable frequency integrated machine at different time points, and the working condition identifications represent working conditions of the corresponding time points. And then, acquiring the standard unit energy consumption under each working condition, and determining the unit energy consumption abnormal tolerance threshold of the all-in-one machine under the working condition according to the environment identification corresponding to the monitoring data, wherein the tolerance degree of the system to the energy consumption abnormality is represented by the environment identification of the specific working condition.
And then, in each stage of working condition, comparing the collected energy consumption data with a matched unit energy consumption abnormal tolerance threshold, and calculating whether the actual energy consumption is smaller than the unit energy consumption abnormal tolerance threshold or not for each time point. And generating a comparison result according to the unit energy consumption comparison result, and displaying whether the energy consumption under each working condition is abnormal or not. Therefore, the energy consumption condition of the frequency conversion integrated machine is monitored and managed, the energy consumption abnormality is found and processed in time, and the control effect of the energy consumption is improved.
Step S7000: and performing intelligent monitoring management of the frequency conversion integrated machine according to the comparison result.
Specifically, firstly, the comparison result is analyzed, the running state and the energy consumption condition of the frequency conversion integrated machine are known, whether the integrated machine is abnormal or not is judged, and if so, whether the frequency conversion integrated machine has a problem or not is judged through the unit energy consumption, the energy consumption trend transition nodes and other information in the comparison result. And then, according to analysis of the comparison result, determining abnormal conditions of the frequency conversion integrated machine, such as overhigh energy consumption, large energy consumption fluctuation and the like. Once the abnormal condition of the frequency conversion all-in-one machine is determined, relevant personnel are notified in an alarm or reminding mode. For example, the alarm information is sent by means of mobile phone application, e-mail or short message, etc., so as to remind maintenance personnel or management personnel to process in time. Meanwhile, corresponding management measures are adopted according to the specific type of the abnormal condition. For example, if the energy consumption is too high, checking whether the equipment has faults or whether the voltage and the current are stable, and repairing or optimizing; and if the working interval deviates from the normal range, adjusting the control strategy of the frequency conversion integrated machine to enable the frequency conversion integrated machine to be restored to the normal working interval. After the abnormal condition is processed, the running state and the energy consumption condition of the frequency conversion integrated machine are continuously monitored, and monitoring data are recorded so as to facilitate subsequent analysis and evaluation of the performance and the effect of the frequency conversion integrated machine, and the normal running and the high-efficiency performance of the frequency conversion integrated machine are ensured, so that the frequency conversion integrated machine is finely monitored and controlled, and the energy consumption of the integrated machine is reduced.
Further, as shown in fig. 2, the embodiment of the present application further includes:
step S7100: reading control parameters of the frequency conversion integrated machine, carrying out control division of the frequency conversion integrated machine according to the control parameters, and configuring a starting protection node;
step S7200: acquiring starting load data according to the starting protection node, and constructing a starting load data set;
step S7300: performing starting load evaluation of the frequency conversion integrated machine according to the starting load data set, and generating starting abnormal data of equipment;
step S7400: and carrying out intelligent monitoring management on the frequency conversion integrated machine according to the starting abnormal data and the comparison result.
Specifically, first, a communication connection is established with the frequency conversion all-in-one machine, and a corresponding communication protocol is used to send a reading instruction to the frequency conversion all-in-one machine so as to acquire control parameters. And then, determining the working mode, the operating frequency range and the like of the frequency conversion all-in-one machine according to the parameter values. And then configuring a starting protection node to ensure that the frequency conversion integrated machine is properly protected in the starting process, for example, setting parameters such as current limit, rotation speed limit and the like in starting.
Then, according to the configured starting protection node, load data of the motor are collected in real time in the starting process, such as current, rotating speed and other data of the motor are recorded; and recording and storing the load data acquired in each starting process to construct a starting load data set. And then, based on the starting load data set, the starting performance and the load capacity of the frequency conversion integrated machine are evaluated, and whether abnormal conditions exist in the starting process is judged by analyzing parameters in the load data set, such as starting time, current waveform, rotating speed change and the like. If abnormal data which is not consistent with the normal starting condition is found, the abnormal data is recorded to generate starting abnormal data for subsequent fault diagnosis and analysis. And finally, judging the working condition of the frequency conversion integrated machine according to the starting abnormal data and the comparison result so as to provide more detailed information for fault detection of the integrated machine, thereby realizing the fine monitoring of the frequency conversion integrated machine.
Further, the embodiment of the application further comprises:
step S7410: screening a working interval monitoring data set in the monitoring data set through the starting protection node;
step S7420: performing time sequence comparison on the working interval monitoring data sets one by one according to the corresponding unit energy consumption abnormal tolerance threshold value to generate a unit energy consumption comparison result;
step S7430: calculating the average difference of the unit energy consumption comparison results, and generating a discrete mark according to the calculation results;
step S7440: and carrying out abnormal evaluation on the energy consumption according to the average value and the discrete identification, and carrying out intelligent monitoring management on the frequency conversion integrated machine based on the abnormal evaluation result of the energy consumption.
Specifically, the starting protection node is a key time point in the starting process of the frequency conversion integrated machine, and the data which are relevant to the working interval of the frequency conversion integrated machine in the monitoring data set can be screened out by identifying the time points. Firstly, analyzing data in a monitoring data set, and determining a working interval monitoring data set, namely data related to a working interval of the frequency conversion integrated machine, near a starting protection node. And then, comparing the unit energy consumption and the unit energy consumption abnormal tolerance threshold value at different time points in the monitoring data set of the working interval one by one, judging whether the unit energy consumption exceeds the abnormal tolerance threshold value, and generating a unit energy consumption comparison result which comprises the deviation value of the unit energy consumption and the abnormal tolerance threshold value at each time point.
And then, calculating the average difference of the unit energy consumption comparison results to obtain the average deviation degree of the unit energy consumption, and judging the fluctuation condition of the unit energy consumption so as to generate a discrete mark for representing the discrete degree of the unit energy consumption. And then, carrying out abnormal evaluation on the unit energy consumption according to the average value and the discrete identification of the unit energy consumption, and judging whether the unit energy consumption exceeds the normal range. And finally, according to the abnormal energy consumption evaluation result, performing intelligent monitoring management on the frequency conversion integrated machine, such as giving an alarm, taking control measures and the like, so as to ensure the normal operation of the frequency conversion integrated machine.
Further, as shown in fig. 3, the embodiment of the present application further includes:
step S7441: carrying out energy consumption trend analysis according to the unit energy consumption comparison result, and determining trend transition nodes;
step S7442: based on the starting abnormal data, carrying out working influence analysis of the frequency conversion all-in-one machine, and determining an influence association interval and an association factor;
step S7443: matching the trend transition nodes according to the influence association interval and the association factor, and removing data according to a matching result;
step S7444: and carrying out abnormal evaluation on the energy consumption according to the data eliminating result.
Specifically, firstly, unit energy consumption comparison results of each working interval are recorded, and electronic forms are used for arrangement; extracting the energy consumption data of each working interval, and calculating statistical indexes such as an average value, a maximum value, a minimum value and the like; analyzing the energy consumption data by using trend analysis methods such as linear regression, moving average, exponential smoothing and the like, drawing an energy consumption trend graph, and observing the change trend of energy consumption; and according to the result of trend analysis, observing inflection points or abrupt changes in the energy consumption trend graph, and determining nodes for converting energy consumption from one trend to another trend to obtain trend conversion nodes.
Then, according to the acquired starting load data set, starting abnormal data of the frequency conversion all-in-one machine are collected; analyzing the collected starting abnormal data, and observing the characteristics and the performances of the abnormality; and determining the influence of starting abnormality on the work of the frequency conversion all-in-one machine according to the analysis result of the abnormal data. For example, whether an abnormal start-up causes a start-up time delay of the variable frequency integrated machine; whether the abnormal starting causes power fluctuation or unstable voltage of the frequency conversion integrated machine or not; whether abnormal starting causes the energy consumption of the frequency conversion integrated machine to be increased; whether abnormal starting causes unstable operation or faults of the frequency conversion integrated machine or not. Then, according to the time stamp and duration in the abnormal data, determining the starting time and the ending time of the influence as an influence association interval; wherein, the starting time delay, the power fluctuation degree, the energy consumption increment and the like are the correlation factors.
Then, according to the determined influence association interval, the trend transition nodes are screened, and only the trend transition nodes overlapped with the influence association interval are reserved; and according to the determined association factors, matching the screened trend transition nodes, and observing whether each trend transition node is matched with the association factors, namely judging whether the node is affected by the association factors in the influence association interval. And eliminating the data related to the trend transition nodes which are not successfully matched, so as to avoid the data which are not affected during subsequent analysis and processing. And finally, taking the data eliminating result as one of the energy consumption abnormality evaluation dimensions, thereby realizing the fine monitoring and frequency conversion integrated machine and reducing the energy consumption of the integrated machine.
Further, the embodiment of the application further comprises:
step S8100: monitoring the equipment temperature of the frequency conversion integrated machine to generate an equipment temperature change curve;
step S8200: performing cooling control response evaluation of the equipment according to the environment identifier and the equipment temperature change curve, and generating a cooling control response evaluation result;
step S8300: and performing intelligent monitoring management of the frequency conversion integrated machine according to the cooling control response evaluation result.
Specifically, the temperature of the frequency conversion all-in-one machine is monitored, namely, the temperature of the frequency conversion all-in-one machine is measured in real time, a series of temperature data points are obtained through continuous temperature measurement, the temperature data points are drawn, a temperature change curve of the equipment is obtained, the temperature change condition of the frequency conversion all-in-one machine at different time points is displayed, and the temperature change trend of the equipment is analyzed and evaluated.
Then, whether the current environment has an influence on the temperature of the device is evaluated according to the environment identifier, and if the environment identifier displays that the temperature is higher, the temperature of the device is increased. Next, the current cooling control response condition is evaluated by combining the environment identifier and the equipment temperature change curve, and specific evaluation indexes comprise cooling effect, cooling speed, control stability and the like. If the temperature reduction control measures are effective and the equipment temperature has reached the expected reduction degree, the evaluation result is good; if the cooling control measures are not effective enough or the equipment temperature change is still large, the evaluation result is that improvement and adjustment are needed. And finally, judging whether the cooling effect of the equipment reaches the expected value or not and the cooling capacity of the equipment in different environments according to the cooling control response evaluation result so as to take corresponding measures, such as adjusting the working parameters of the equipment, changing the cooling strategy and the like, thereby realizing intelligent monitoring and management of the equipment.
Further, the embodiment of the application further comprises:
step S8210: determining a device temperature maximum value according to the device temperature change curve, and performing device temperature control evaluation according to the device temperature maximum value and the environment identifier to generate a maximum value control evaluation result;
step S8220: and determining a cooling response node based on the equipment temperature change curve, calculating a cooling rate, and generating a cooling control response evaluation result based on a cooling rate calculation result and the maximum value control evaluation result.
Specifically, first, a maximum value of the device temperature, that is, the highest point on the temperature change curve is determined from the device temperature change curve. And comparing the maximum value with an environment identifier, judging whether the equipment temperature exceeds a preset threshold value or not and whether the environment has influence on the equipment temperature or not, and using the maximum value as a maximum value control evaluation result to guide subsequent control measures. And then determining a cooling response node according to the equipment temperature change curve, namely determining a starting point of temperature reduction after cooling control on the curve. Then, a cooling rate, i.e., a rate at which the temperature of the device decreases, is calculated from the start point. And generating a cooling control response evaluation result according to the calculation result of the cooling rate and the maximum value control evaluation result.
Further, the embodiment of the application further comprises:
step S9100: based on the comparison result, carrying out abnormal accumulation to generate an accumulated abnormal value;
step S9200: when the accumulated abnormal value meets a preset threshold value, a maintenance instruction is generated;
step S9300: and controlling the frequency conversion all-in-one machine to carry out maintenance management according to the maintenance instruction.
Specifically, firstly, comparing the result obtained by comparison with an expected result, and if a difference or an abnormal condition exists, accumulating the abnormal times to obtain the latest accumulated abnormal value which is used for measuring the running stability of the equipment and whether maintenance is needed. Then, whether the accumulated abnormal value reaches a preset threshold value is judged, if the accumulated abnormal value exceeds the preset threshold value, the abnormal condition of the equipment is more, and maintenance is needed, and in this case, a maintenance instruction is generated for instructing maintenance personnel to perform corresponding maintenance and management work. And then, a serviceman controls the frequency conversion all-in-one machine to carry out maintenance management according to the generated maintenance instruction. The maintenance instructions instruct maintenance personnel to carry out maintenance and management operations on the equipment, including maintenance, repair, replacement of parts and the like on the equipment. By executing the maintenance instruction, the abnormal condition of the equipment is solved in time, so that the normal operation and performance of the equipment are ensured.
In summary, the intelligent monitoring method of the mine frequency conversion all-in-one machine provided by the embodiment of the application has the following technical effects:
the use scene of the interactive frequency conversion integrated machine is divided into N levels of working conditions based on the use scene, and a foundation is laid for building a precise energy consumption reference; configuring a calibration environment, and collecting historical control data of the variable frequency integrated machine under the calibration environment, wherein the historical control data comprises mapping data of coal conveying capacity and energy consumption; performing data matching on the historical control data according to N-level working conditions, constructing reference unit energy consumption under each-level working condition, and establishing energy consumption references under the normal working state of the frequency conversion integrated machine for detecting abnormal conditions of the running state; the method comprises the steps of performing N-level working condition environmental impact evaluation of the frequency conversion integrated machine through big data, constructing environmental impact association, determining a unit energy consumption abnormal tolerance threshold through the environmental impact association and reference unit energy consumption, eliminating the influence of external environment change on the frequency conversion integrated machine energy consumption, enabling the reference to more accurately reflect the working state of the equipment, establishing a judgment standard for detecting the abnormal running state of the equipment, and realizing accurate identification of abnormal conditions; continuously monitoring the frequency conversion integrated machine, constructing a monitoring data set, wherein the monitoring data set comprises an energy consumption data set, mapped working conditions and environment identifications, matching unit energy consumption abnormal tolerance threshold values, comparing unit energy consumption of the monitoring data set, generating a comparison result, and detecting abnormal conditions through comparison with a reference based on real-time monitoring of the running state of the frequency conversion integrated machine; and the intelligent monitoring management of the frequency conversion integrated machine is carried out according to the comparison result, the operation state of the frequency conversion integrated machine is managed according to the monitoring result, and an early warning is sent when an abnormal condition is detected, so that the technical effect of finely monitoring and controlling the frequency conversion integrated machine is achieved, and the energy consumption of the integrated machine is reduced.
Embodiment two:
based on the same inventive concept as the intelligent monitoring method of the mine frequency conversion integrated machine in the foregoing embodiment, as shown in fig. 4, an embodiment of the present application provides an intelligent monitoring system of the mine frequency conversion integrated machine, where the system includes:
the method comprises the steps that a working condition grading module 11 is used for interactively converting the use scene of the integrated machine, and working condition division is carried out based on the use scene to construct N-level working conditions;
the historical data acquisition module 12 is used for configuring a calibration environment and acquiring historical control data of the frequency conversion integrated machine under the calibration environment, wherein the historical control data comprises mapping data of coal conveying capacity and energy consumption;
the working condition data matching module 13 is used for carrying out data matching on the historical control data according to the N-level working conditions and constructing reference unit energy consumption under each level of working conditions;
the environmental impact evaluation module 14 is configured to perform N-level working condition environmental impact evaluation of the frequency conversion integrated machine through big data, construct an environmental impact association, and determine a unit energy consumption abnormal tolerance threshold through the environmental impact association and the reference unit energy consumption;
the monitoring data construction module 15 is used for continuously monitoring the frequency conversion integrated machine and constructing a monitoring data set, wherein the monitoring data set comprises an energy consumption data set and mapped working conditions and environment identifiers;
the unit energy consumption comparison module 16 is used for matching unit energy consumption abnormal tolerance threshold values, and performing unit energy consumption comparison on the monitoring data set to generate comparison results;
and the intelligent monitoring management module 17 is used for performing intelligent monitoring management of the frequency conversion integrated machine according to the comparison result.
Further, the intelligent monitoring management module 17 includes the following steps:
reading control parameters of the frequency conversion integrated machine, carrying out control division of the frequency conversion integrated machine according to the control parameters, and configuring a starting protection node;
acquiring starting load data according to the starting protection node, and constructing a starting load data set;
performing starting load evaluation of the frequency conversion integrated machine according to the starting load data set, and generating starting abnormal data of equipment;
and carrying out intelligent monitoring management on the frequency conversion integrated machine according to the starting abnormal data and the comparison result.
Further, the intelligent monitoring management module 17 further includes the following steps:
screening a working interval monitoring data set in the monitoring data set through the starting protection node;
performing time sequence comparison on the working interval monitoring data sets one by one according to the corresponding unit energy consumption abnormal tolerance threshold value to generate a unit energy consumption comparison result;
calculating the average difference of the unit energy consumption comparison results, and generating a discrete mark according to the calculation results;
and carrying out abnormal evaluation on the energy consumption according to the average value and the discrete identification, and carrying out intelligent monitoring management on the frequency conversion integrated machine based on the abnormal evaluation result of the energy consumption.
Further, the intelligent monitoring management module 17 further includes the following steps:
carrying out energy consumption trend analysis according to the unit energy consumption comparison result, and determining trend transition nodes;
based on the starting abnormal data, carrying out working influence analysis of the frequency conversion all-in-one machine, and determining an influence association interval and an association factor;
matching the trend transition nodes according to the influence association interval and the association factor, and removing data according to a matching result;
and carrying out abnormal evaluation on the energy consumption according to the data eliminating result.
Further, the embodiment of the application also comprises a temperature monitoring management module, which comprises the following execution steps:
monitoring the equipment temperature of the frequency conversion integrated machine to generate an equipment temperature change curve;
performing cooling control response evaluation of the equipment according to the environment identifier and the equipment temperature change curve, and generating a cooling control response evaluation result;
and performing intelligent monitoring management of the frequency conversion integrated machine according to the cooling control response evaluation result.
Further, the temperature monitoring management module further comprises the following execution steps:
determining a device temperature maximum value according to the device temperature change curve, and performing device temperature control evaluation according to the device temperature maximum value and the environment identifier to generate a maximum value control evaluation result;
and determining a cooling response node based on the equipment temperature change curve, calculating a cooling rate, and generating a cooling control response evaluation result based on a cooling rate calculation result and the maximum value control evaluation result.
Further, the embodiment of the application also comprises an all-in-one machine maintenance management module, which comprises the following execution steps:
based on the comparison result, carrying out abnormal accumulation to generate an accumulated abnormal value;
when the accumulated abnormal value meets a preset threshold value, a maintenance instruction is generated;
and controlling the frequency conversion all-in-one machine to carry out maintenance management according to the maintenance instruction.
Any of the steps of the methods described above may be stored as computer instructions or programs in a non-limiting computer memory and may be called by a non-limiting computer processor to identify any method for implementing an embodiment of the present application, without unnecessary limitations.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, the present application is intended to include such modifications and alterations insofar as they come within the scope of the application or the equivalents thereof.
Claims (8)
1. An intelligent monitoring method of a mine frequency conversion all-in-one machine is characterized by comprising the following steps:
the method comprises the steps of performing operation scene division on the interactive frequency conversion integrated machine, and constructing N-level operation conditions based on the operation scene;
configuring a calibration environment, and collecting historical control data of the variable frequency integrated machine in the calibration environment, wherein the historical control data comprises mapping data of coal conveying capacity and energy consumption;
performing data matching on the historical control data according to the N-level working conditions, and constructing reference unit energy consumption under each-level working condition;
performing N-level working condition environmental impact evaluation of the frequency conversion integrated machine through big data, constructing environmental impact association, and determining a unit energy consumption abnormal tolerance threshold through the environmental impact association and the reference unit energy consumption;
continuously monitoring the frequency conversion integrated machine to construct a monitoring data set, wherein the monitoring data set comprises an energy consumption data set, mapped working conditions and environment identifiers;
matching unit energy consumption abnormal tolerance threshold values, and comparing unit energy consumption of the monitoring data set to generate a comparison result;
and performing intelligent monitoring management of the frequency conversion integrated machine according to the comparison result.
2. The method of claim 1, wherein the method further comprises:
reading control parameters of the frequency conversion integrated machine, carrying out control division of the frequency conversion integrated machine according to the control parameters, and configuring a starting protection node;
acquiring starting load data according to the starting protection node, and constructing a starting load data set;
performing starting load evaluation of the frequency conversion integrated machine according to the starting load data set, and generating starting abnormal data of equipment;
and carrying out intelligent monitoring management on the frequency conversion integrated machine according to the starting abnormal data and the comparison result.
3. The method of claim 2, wherein the method further comprises:
screening a working interval monitoring data set in the monitoring data set through the starting protection node;
performing time sequence comparison on the working interval monitoring data sets one by one according to the corresponding unit energy consumption abnormal tolerance threshold value to generate a unit energy consumption comparison result;
calculating the average difference of the unit energy consumption comparison results, and generating a discrete mark according to the calculation results;
and carrying out abnormal evaluation on the energy consumption according to the average value and the discrete identification, and carrying out intelligent monitoring management on the frequency conversion integrated machine based on the abnormal evaluation result of the energy consumption.
4. A method as claimed in claim 3, wherein the method further comprises:
carrying out energy consumption trend analysis according to the unit energy consumption comparison result, and determining trend transition nodes;
based on the starting abnormal data, carrying out working influence analysis of the frequency conversion all-in-one machine, and determining an influence association interval and an association factor;
matching the trend transition nodes according to the influence association interval and the association factor, and removing data according to a matching result;
and carrying out abnormal evaluation on the energy consumption according to the data eliminating result.
5. The method of claim 1, wherein the method further comprises:
monitoring the equipment temperature of the frequency conversion integrated machine to generate an equipment temperature change curve;
performing cooling control response evaluation of the equipment according to the environment identifier and the equipment temperature change curve, and generating a cooling control response evaluation result;
and performing intelligent monitoring management of the frequency conversion integrated machine according to the cooling control response evaluation result.
6. The method of claim 5, wherein the method further comprises:
determining a device temperature maximum value according to the device temperature change curve, and performing device temperature control evaluation according to the device temperature maximum value and the environment identifier to generate a maximum value control evaluation result;
and determining a cooling response node based on the equipment temperature change curve, calculating a cooling rate, and generating a cooling control response evaluation result based on a cooling rate calculation result and the maximum value control evaluation result.
7. The method of claim 1, wherein the method further comprises:
based on the comparison result, carrying out abnormal accumulation to generate an accumulated abnormal value;
when the accumulated abnormal value meets a preset threshold value, a maintenance instruction is generated;
and controlling the frequency conversion all-in-one machine to carry out maintenance management according to the maintenance instruction.
8. An intelligent monitoring system for a mine frequency conversion all-in-one machine, for implementing the intelligent monitoring method for a mine frequency conversion all-in-one machine according to any one of claims 1 to 7, the system comprising:
the method comprises the steps that a working condition grading module is used for interactively changing the use scene of the frequency all-in-one machine, and working condition division is carried out based on the use scene to construct an N-level working condition;
the historical data acquisition module is used for configuring a calibration environment and acquiring historical control data of the frequency conversion integrated machine under the calibration environment, wherein the historical control data comprises mapping data of coal conveying capacity and energy consumption;
the working condition data matching module is used for carrying out data matching on the historical control data according to the N-level working conditions and constructing reference unit energy consumption under each-level working condition;
the environment influence evaluation module is used for performing N-level working condition environment influence evaluation of the frequency conversion integrated machine through big data, constructing environment influence association, and determining a unit energy consumption abnormal tolerance threshold through the environment influence association and the reference unit energy consumption;
the monitoring data construction module is used for continuously monitoring the frequency conversion integrated machine and constructing a monitoring data set, wherein the monitoring data set comprises an energy consumption data set and mapped working conditions and environment identifications;
the unit energy consumption comparison module is used for matching unit energy consumption abnormal tolerance threshold values, and performing unit energy consumption comparison on the monitoring data set to generate comparison results;
the intelligent monitoring management module is used for carrying out intelligent monitoring management of the frequency conversion integrated machine according to the comparison result.
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