CN116645010B - Chemical industry safety in production inspection system - Google Patents
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Abstract
The invention discloses a chemical safety production inspection system, in particular to the technical field of safety inspection, which obtains potential evaluation coefficients by collecting multiple parameters of an inspection robot and is used for analyzing potential problems and risks of operation of the inspection robot; simultaneously, a first threshold value of the potential evaluation coefficient and a second threshold value of the potential evaluation coefficient are set, the potential evaluation coefficient is compared with the first threshold value of the potential evaluation coefficient and the second threshold value of the potential evaluation coefficient respectively, classification and warning of the running state of the inspection robot are facilitated, emergency measures are conveniently and immediately adopted for classification judgment to avoid possible accidents or faults, and safety and continuity of chemical production are ensured; the method comprises the steps of analyzing a plurality of potential evaluation coefficients obtained in a period of time, calculating an average value and a discrete degree value according to the plurality of potential evaluation coefficients, analyzing whether the inspection robot in the current stage is in an accidental or normalized state, correcting initial maintenance frequency through the average value, and realizing personalized maintenance of the machine.
Description
Technical Field
The invention relates to the technical field of safety inspection, in particular to a chemical safety production inspection control system.
Background
The system combines sensor technology, data acquisition and processing, communication network, information management and other technologies, and aims to realize real-time monitoring, early warning and control of various potential safety hazards and risks in the chemical production process. The inspection robot is used for inspecting and safely inspecting two common chemical production sites, the embedded sensor is mainly concentrated in specific equipment or areas, the whole production site cannot be comprehensively inspected, blind areas possibly exist, and therefore the inspection robot is used as a supplement of the embedded sensor, and is widely distributed and used in the severe chemical production monitoring of the environment.
Because the chemical production place is great, consequently can dispose a considerable amount of inspection robots in advance and be used for the inspection, but current inspection robot inspection has following some problems:
as the time that inspection robots are frequently used in harsh environments increases, inspection robots more or less suffer from quality problems, and the lack of analysis of potential problems with inspection robots can make diagnosis when inspection robots fail difficult because of insufficient knowledge and analysis, it is difficult to quickly and accurately determine the cause of the failure, and take appropriate action to repair. This will lead to an extended recovery time of the fault, reducing the number of available inspection robots in the chemical plant, and when the inspection robots fail, certain inspection tasks may be delayed or not performed, resulting in a failure to discover potential safety risks and problems in time. This can pose a potential threat to the safety of chemical plants and personnel; lack of analysis of potential problems with inspection robots may result in failures that are too late, producing a series of adverse effects on chemical inspection, including reduced inspection efficiency, increased inspection costs, reduced inspection quality, and increased security risks. Therefore, it is important to comprehensively analyze and prevent potential problems of the inspection robot so as to ensure reliability, stability and durability of the inspection robot, thereby improving efficiency and safety of chemical inspection.
In order to solve the above problems, a technical solution is now provided.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, an embodiment of the present invention provides a chemical safety production inspection system to solve the above-mentioned problems in the prior art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the system comprises a parameter acquisition module, a comprehensive evaluation module, an analysis judgment module, a re-analysis module and a maintenance adjustment module, wherein the modules are connected through signals;
the parameter acquisition module acquires mechanical parameters and software parameters of the inspection robot and sends the acquired parameters to the comprehensive evaluation module;
the comprehensive evaluation module receives the acquired parameters, obtains potential evaluation coefficients according to the acquired parameters, and sends the acquired coefficient signals to the analysis judgment module;
the analysis judging module sets a potential evaluation coefficient first threshold value and a potential evaluation coefficient second threshold value, compares the potential evaluation coefficient with the potential evaluation coefficient first threshold value and the potential evaluation coefficient second threshold value respectively, generates a reliable signal, an uncertain signal and an unreliable signal according to the comparison result, and sends the judged signals to the analysis module;
the analysis module collects the average value and the discrete degree value of the uncertain signal inspection robot, compares the average value and the discrete degree value with corresponding threshold values respectively, generates a signal to be maintained and an unreliable signal according to a comparison result, and sends a re-judging signal to the monitoring and adjusting module;
and the maintenance adjustment module corrects the maintenance frequency of the inspection robot to be maintained according to the average value.
In a preferred embodiment, the parameter acquisition module operation includes the following:
counting the inspection robots participating in inspection, and collecting mechanical parameters and software parameters of the inspection robots;
the mechanical parameters comprise a maximum abnormality index of the motor and a battery stability index;
the software parameters include an average increase in instruction execution response time and a control software failure rate.
In a preferred embodiment, the logic for obtaining the motor maximum abnormality index is:
measuring the real-time rotating speed of a motor through a rotating speed sensor, and monitoring the rotating speed of the motor through a Hall sensor;
step two, obtaining the rotation speed change rate of the motor by recording the rotation speed change condition in a period of time;
step three, according to the collected parameter data, the following example formula is established to calculate the abnormal rotating speed index of the motor:
the rotation speed abnormality index formula:
abnormality index= (real-time rotation speed value-average rotation speed value)/standard deviation;
the real-time rotating speed value is obtained through a sensor or monitoring equipment; the average rotating speed value is obtained by calculating the average value of the real-time rotating speed value in a period of time; the standard deviation is obtained by calculating the standard deviation of the real-time rotating speed value;
counting the abnormal indexes of all motors of the inspection robot, recording the maximum abnormal index and marking the maximum abnormal index as the maximum abnormal index of the motor;
the acquisition logic of the battery stability index is as follows:
measuring a real-time temperature value of a battery through a temperature sensor, and placing the temperature sensor on the surface of the battery to obtain accurate temperature data;
step two, according to the collected temperature parameter data, the following example formula is established to calculate the temperature stability index of the battery:
battery stability index= (real-time temperature change rate-average temperature change rate)/standard deviation;
the real-time temperature change rate is obtained by recording the temperature value of the battery and calculating the temperature change difference between adjacent time points by dividing the time interval; the average temperature change rate is obtained by calculating the average value of the real-time temperature change rate; the standard deviation is obtained by calculating the standard deviation of the real-time temperature change rate.
In a preferred embodiment, the instruction execution response time average increment fetch logic is:
step one, selecting and calculating the difference between the response time of each instruction and the initial response time, and calculating the average value of the difference as an increment;
step two, recording initial instruction response time of the inspection robot in a normal working state, wherein the initial instruction response time is a reference value used as a base for comparison;
step three, sending instructions to the inspection robot, recording response time of each instruction, and measuring the sending and response time of the instructions by using a timer;
step four, calculating the response time increment of each instruction, namely the difference value between the response time of the instruction and the initial response time by using the recorded response time and the initial response time of each instruction; adding the response time increment of all instructions, dividing the response time increment by the number of the instructions to obtain the average increase of the response time of instruction execution;
the acquisition logic of the control software failure rate is as follows:
firstly, acquiring the running time, the fault occurrence times and the maintenance time of control software used by the inspection robot for a period of time, and acquiring the data according to a fault report, a log file and a maintenance record;
step two, calculating the failure rate of the inspection robot control software by using the data of the failure occurrence times and the running time, wherein the calculation formula is as follows:
failure rate= (failure occurrence number/running time) ×time unit.
In a preferred embodiment, the integrated assessment module operation includes the following:
the potential evaluation coefficient is obtained by normalizing the maximum abnormality index of the motor, the stability index of the battery, the average increment of the instruction execution response time and the failure rate of control software, and the calculation formula is as follows:
；
wherein MMEI, BSI, AMIRT, CSR respectively represents a maximum abnormality index of the motor, a battery stability index, an average increase in instruction execution response time, a failure rate of control software,respectively a maximum abnormality index of the motor, a battery stability index, an average increase of instruction execution response time and a preset proportionality coefficient of a control software failure rate,are all greater than 0.
In a preferred embodiment, the analysis and judgment module operates to include the following:
setting a potential evaluation coefficient first threshold value and a potential evaluation coefficient second threshold value, wherein the potential evaluation coefficient second threshold value is larger than the potential evaluation coefficient first threshold value, and comparing the potential evaluation coefficient with the potential evaluation coefficient first threshold value and the potential evaluation coefficient second threshold value respectively;
if the potential evaluation coefficient is smaller than the first threshold value of the potential evaluation coefficient, generating a reliable signal;
if the potential evaluation coefficient is greater than or equal to the first threshold value of the potential evaluation coefficient and less than or equal to the second threshold value of the potential evaluation coefficient, generating an uncertain signal;
if the potential evaluation coefficient is larger than the potential evaluation coefficient second threshold value, generating an unreliable signal and sending out an early warning prompt.
In a preferred embodiment, the analysis module operation includes the following:
counting the inspection robots generating uncertain signals, collecting a plurality of potential evaluation coefficients of each inspection robot within a period of recent working time, calculating the average value and the discrete degree value of the potential evaluation coefficients, setting an average threshold value and a discrete degree threshold value, comparing the average value and the discrete degree value with the average threshold value and the discrete degree threshold value respectively, and if the average value is larger than the average value threshold value and the discrete degree value is larger than the discrete degree threshold value, indicating that the inspection robots are in an unstable running state, and generating unreliable signals;
if the average value is larger than the average threshold value and the discrete degree value is smaller than the discrete degree threshold value, or if the average value is smaller than the average threshold value and the discrete degree value is larger than the discrete degree threshold value, comparing the average value with a potential evaluation coefficient second threshold value, and if the average value is smaller than the potential evaluation coefficient second threshold value, generating a signal to be maintained; if the average value is smaller than or equal to the potential evaluation coefficient second threshold value, generating an unreliable signal;
if the average value is smaller than the average threshold value and the discrete degree value is smaller than the discrete degree threshold value, the running state of the inspection robot is stable, the average value is compared with the first threshold value of the potential evaluation coefficient, and if the average value is smaller than or equal to the first threshold value of the potential evaluation coefficient, a reliable signal is generated; and if the average value is larger than the first threshold value of the potential evaluation coefficient, generating a signal to be maintained.
In a preferred embodiment, the maintenance adjustment module operation includes the following:
counting the number of inspection robots generating signals to be maintained, sorting the inspection robots from small to large according to an average value, maintaining, correcting maintenance frequency, and the calculation formula is as follows:
；
in the formula, CMF is a correction maintenance frequency, MF is an initial maintenance frequency, AVG is an average value, and the correction maintenance frequency is used for replacing the initial maintenance frequency.
The invention relates to a technical effect and advantages of a chemical safety production inspection control system, which are as follows:
1. acquiring a maximum abnormal index of a motor, a battery stability index, an average increase of instruction execution response time and a control software fault rate of the inspection robot to obtain a potential evaluation coefficient for evaluating the running state of the inspection robot, analyzing potential problems and risks of running of the inspection robot, helping to discover and solve the problems early and improving the reliability and stability of the inspection robot; simultaneously, a first threshold value of the potential evaluation coefficient and a second threshold value of the potential evaluation coefficient are set, the potential evaluation coefficient is compared with the first threshold value of the potential evaluation coefficient and the second threshold value of the potential evaluation coefficient respectively, classification and warning of the running state of the inspection robot are facilitated, emergency measures are conveniently and immediately adopted for classification judgment to avoid possible accidents or faults, and safety and continuity of chemical production are ensured;
2. the method comprises the steps of analyzing a plurality of potential evaluation coefficients obtained in a period of time, calculating an average value and a discrete degree value according to the plurality of potential evaluation coefficients, analyzing whether the inspection robot in the current stage is accidental or in a normalized presentation state, further analyzing an uncertain signal according to an analysis result to be divided into unreliable signals and signals to be maintained, and correcting an initial maintenance frequency according to the signal to be maintained through the average value, so that personalized maintenance of the machine can be realized; machines with higher potential evaluation coefficients can increase maintenance frequency, prevent faults from occurring, and improve reliability and stability of the machines; and the machine with lower potential evaluation coefficient can reduce maintenance frequency, save maintenance resources and enable the machine to be better used for the maintenance of high-risk machines.
Drawings
FIG. 1 is a schematic diagram of a chemical safety production inspection system.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
The invention relates to a chemical safety production inspection control system.
FIG. 1 shows a chemical safety production inspection system, which comprises a parameter acquisition module, a comprehensive evaluation module, an analysis and judgment module, an analysis module and a maintenance adjustment module, wherein the modules are connected through signals;
the parameter acquisition module acquires mechanical parameters and software parameters of the inspection robot and sends the acquired parameters to the comprehensive evaluation module;
the comprehensive evaluation module receives the acquired parameters, obtains potential evaluation coefficients according to the acquired parameters, and sends the acquired coefficient signals to the analysis judgment module;
the analysis judging module sets a potential evaluation coefficient first threshold value and a potential evaluation coefficient second threshold value, compares the potential evaluation coefficient with the potential evaluation coefficient first threshold value and the potential evaluation coefficient second threshold value respectively, generates a reliable signal, an uncertain signal and an unreliable signal according to the comparison result, and sends the judged signals to the analysis module;
the analysis module collects the average value and the discrete degree value of the uncertain signal inspection robot, compares the average value and the discrete degree value with corresponding threshold values respectively, generates a signal to be maintained and an unreliable signal according to a comparison result, and sends a re-judging signal to the monitoring and adjusting module;
and the maintenance adjustment module corrects the maintenance frequency of the inspection robot to be maintained according to the average value.
The parameter acquisition module operation comprises the following contents:
the motor has an important function in the inspection robot, is a key component for driving the inspection robot to move and execute tasks, and drives the inspection robot to move, including forward, backward, steering and the like. The good motor state can provide stable power output, ensures that the inspection robot can accurately execute the inspection path and instructions. If the motor state is poor, the movement of the inspection robot is unstable, the speed is fluctuated or the inspection robot cannot normally move, and the accuracy and the efficiency of the inspection task are affected; the motor is crucial to the accurate positioning of the inspection task by controlling the motion trail and the position of the inspection robot. The good motor state can provide accurate motion control, so that the inspection robot can accurately reach the target position and follow a preset path, and the integrity and the accuracy of the inspection task are ensured; the energy consumption management of the motor is important to the working time and the cruising ability of the inspection robot. The inspection robot with good motor state can efficiently utilize energy, and prolong working time and endurance. And the inspection robot with poor motor state can consume excessive energy, so that the battery is rapidly exhausted, and the inspection time and range of the inspection robot are limited.
Counting the inspection robots participating in inspection, and collecting mechanical parameters and software parameters of the inspection robots;
the mechanical parameters comprise a maximum abnormality index of the motor and a battery stability index;
the software parameters comprise the average increment of instruction execution response time and control software failure rate;
the acquisition logic of the maximum abnormality index of the motor is as follows:
measuring the real-time rotating speed of a motor through a rotating speed sensor, and monitoring the rotating speed of the motor through a Hall sensor;
and step two, obtaining the rotating speed change rate of the motor by recording the rotating speed change condition in a period of time. This may help to detect abnormal fluctuations or abnormal patterns of variation in rotational speed;
step three, according to the collected parameter data, the following example formula is established to calculate the abnormal rotating speed index of the motor:
the rotation speed abnormality index formula:
abnormality index= (real-time rotation speed value-average rotation speed value)/standard deviation;
the real-time rotating speed value represents the rotating speed value of the inspection robot motor at the current moment and is obtained through a sensor or monitoring equipment; the average rotating speed value represents the average level of the rotating speed of the motor of the inspection robot in a period of time, and is obtained by calculating the average value of the real-time rotating speed value in a period of time; the standard deviation is used for measuring the discrete degree of the motor rotating speed value of the inspection robot, and is obtained by calculating the standard deviation of the real-time rotating speed value.
The motor abnormality index is used for reflecting the abnormality degree or abnormal state of the motor and aims to provide evaluation and monitoring on the running state of the motor;
the greater the motor abnormality index, the higher the degree of abnormality of the motor, meaning that the motor may be faulty or abnormal. This may be due to overload, overheating, mechanical failure, power problems, etc. of the motor. When the abnormality index exceeds a preset threshold, an alarm can be triggered or corresponding maintenance measures can be taken to avoid further damage or shutdown;
conversely, a smaller motor abnormality index indicates that the motor is in a normal or near normal state with no obvious signs of abnormality. This means that no obvious faults or anomalies are found in the operation of the motor, and a stable and normal operation state is maintained.
The abnormal condition of the motor can be estimated by comparing the deviation degree of the real-time rotating speed value and the historical average rotating speed value;
this formula is calculated based on real-time rotational speed values and historical statistics. The real-time rotational speed value can be obtained by a sensor, and the historical average rotational speed value and standard deviation can be obtained by carrying out statistical calculation on rotational speed data in a period of time.
And step four, counting the abnormality indexes of all motors of the inspection robot, recording the maximum abnormality index and marking the maximum abnormality index as the maximum abnormality index of the motor.
The acquisition logic of the battery stability index is as follows:
the importance of a battery to a patrol robot is self-evident, as it is a key component that provides energy to the patrol robot to work. The state of the battery directly influences the running state and performance of the inspection robot; good battery conditions can provide a stable electrical output, ensuring that the inspection robot obtains a continuous and stable energy supply. If the battery state is poor, the electric energy output is unstable, which may cause the work of the inspection robot to be disconnected or interrupted, and the completion of the inspection task is affected; the inspection robot with good battery state can exert the best performance such as running speed, load capacity and the like, and the inspection robot with poor battery state can show the problem of low power, low speed or other performance degradation, and the quality and effect of the inspection task are affected.
Step one, measuring a real-time temperature value of the battery through a temperature sensor. Placing a temperature sensor on the surface of the battery to obtain accurate temperature data;
step two, according to the collected temperature parameter data, the following example formula is established to calculate the temperature stability index of the battery:
battery stability index= (real-time temperature change rate-average temperature change rate)/standard deviation
The real-time temperature change rate represents the change degree of the battery temperature in a period of time, and is obtained by recording the temperature value of the battery and calculating the temperature change difference between adjacent time points by dividing the time interval; the average temperature change rate represents the average rate of battery temperature change in a period of time, and is obtained by calculating the average value of the real-time temperature change rate; the standard deviation measures the discrete degree of the temperature change rate of the battery and is obtained by calculating the standard deviation of the real-time temperature change rate;
this formula evaluates the temperature stability of the battery by comparing the degree of deviation of the real-time temperature change rate from the historical average temperature change rate;
the battery stability index is used for the stability degree of the system battery and aims at evaluating the temperature change condition of the battery in the operation process, and specifically, the size of the battery temperature stability index can be used for representing the temperature stability of the battery and whether the battery is in a normal temperature range;
the smaller the battery temperature stability index, the higher the temperature stability of the battery, i.e., the smaller and stable the temperature change of the battery during operation. This means that the battery is in a normal operating temperature range, has no overheating or overcooling problems, and can operate normally;
conversely, a greater battery temperature stability index indicates a lower battery temperature stability, i.e., a greater, unstable temperature change during operation of the battery. This may be due to the fact that the battery is affected by overheating, overcharge, overdischarge, external environment temperature variations, etc. Too high or too low a temperature change may negatively affect the performance, life and safety of the battery, even causing malfunction or damage.
The instruction execution response time average increment acquisition logic is:
the instruction response time refers to a time interval when the inspection robot responds after receiving the instruction. The lower instruction response time means that the inspection robot can rapidly and timely execute instructions, and task execution efficiency is improved. In contrast, if the average increase of the command response time is larger, the response time of the inspection robot after receiving the command becomes longer, so that the task execution speed is reduced, and the timeliness and the efficiency of the inspection task are affected.
Step one, selecting and calculating the difference between the response time of each instruction and the initial response time, and calculating the average value of the difference as an increment;
step two, recording initial instruction response time of the inspection robot in a normal working state. This is a reference value for comparison as a base;
and thirdly, sending instructions to the inspection robot and recording the response time of each instruction. A timer or automated tool may be used to measure the sending and response times of the instructions;
step four, calculating the response time increment of each instruction, namely the difference value between the response time of the instruction and the initial response time by using the recorded response time and the initial response time of each instruction; adding the response time increment of all the instructions, dividing the response time increment by the instruction number to obtain an average increment;
the average increase in instruction response time is used to evaluate the efficiency and performance of the inspection robot in processing instructions. Specifically, the index reflects the change degree of response time of the inspection robot in a normal working state after receiving the instruction; when the average increment of the command response time is smaller, the change of the response time of the inspection robot when the command is processed is smaller, namely the response time of the inspection robot is more stable. The inspection robot can respond quickly after receiving the instruction, and has higher response speed and efficiency;
in contrast, when the average increment of the inspection robot instruction response time is large, it means that the change of the inspection robot response time is large when the inspection robot processes the instruction, that is, the inspection robot response time is not stable enough. This may mean that the inspection robot takes a long time to respond after receiving the instruction, and there may be a processing bottleneck or other performance problem;
therefore, a smaller average increment is generally considered to perform well, indicating that the inspection robot has efficient instruction processing capability and faster response speed. A larger average increment may require further analysis and improvement to improve the response performance and efficiency of the inspection robot.
The acquisition logic of the control software failure rate is as follows:
the control software fault rate is directly related to the functional stability of the inspection robot. If the control software has faults or errors, the inspection robot can not execute the instruction correctly, and the problems of error inspection, out-of-control or response stopping and the like occur, so that the inspection capability and the task completion quality of the inspection robot are seriously affected; accuracy and precision: the failure rate of the control software also affects the accuracy of the motion and perception of the inspection robot. If the control software fails or is wrong, deviation or errors can occur in the aspects of positioning, navigation, sensor data processing and the like of the inspection robot, so that the inspection robot is influenced to accurately sense the environment and control the position, and the inspection accuracy and precision are further influenced;
step one, collecting the running time, the fault occurrence times and the maintenance time of control software used by the inspection robot for a period of time, and acquiring the data according to a fault report, a log file or a maintenance record;
step two, calculating the failure rate of the inspection robot control software by using the data of the failure occurrence times and the running time, wherein the calculation formula is as follows:
failure rate= (failure occurrence number/operation time) ×time unit
The failure occurrence times refer to the failure occurrence times in a specific time period, the running time refers to the total running time of the inspection robot control software in the same time period, and the time unit can be selected according to actual requirements, such as hours, days, months and the like;
the failure rate of the control software is used to evaluate the reliability and stability of the software system. In particular, the failure rate reflects the frequency or probability of failure of the inspection robot control software within a particular period of time,
when the failure rate of the control software is low, the software system is relatively stable, and the probability of failure is low. The method means that the control software can work normally and execute instructions stably in the running process of the inspection robot, and the inspection robot has higher reliability and stability;
in contrast, when the failure rate of the control software is high, the software system has more failure problems, and the failure occurrence frequency is high. This may cause abnormal behavior, instruction errors or interruption of the inspection robot during operation, affecting the normal operation and performance of the inspection robot;
therefore, a lower failure rate is generally considered to be a good performance, which means that the control software of the inspection robot has higher reliability and stability and can support long-time stable operation of the inspection robot. Higher failure rates may require further failure analysis and repair to improve the reliability and performance of the software system.
The comprehensive evaluation module operation comprises the following contents:
the potential evaluation coefficient is obtained by normalizing the maximum abnormality index of the motor, the stability index of the battery, the average increment of the instruction execution response time and the failure rate of control software, and the calculation formula is as follows:
；
wherein MMEI, BSI, AMIRT, CSR respectively represents a maximum abnormality index of the motor, a battery stability index, an average increase in instruction execution response time, a failure rate of control software,respectively a maximum abnormality index of the motor, a battery stability index, an average increase of instruction execution response time and a preset proportionality coefficient of a control software failure rate,are all greater than 0;
the potential evaluation coefficient is used for comprehensively evaluating the overall performance and reliability of the inspection robot system, and the smaller the potential evaluation coefficient is, the better the overall performance of the inspection robot system is, and the higher the reliability, stability and execution efficiency are. This means that the inspection robot performs well in all aspects of the index, can efficiently complete tasks, and has a small risk of failure;
conversely, the larger the potential evaluation coefficient is, the worse the overall performance of the inspection robot system is, and more potential problems and risks of faults exist. This may result in an increased likelihood of abnormal behavior, performance degradation, or malfunction of the inspection robot during operation.
The analysis and judgment module operation comprises the following contents:
setting a potential evaluation coefficient first threshold value and a potential evaluation coefficient second threshold value, wherein the potential evaluation coefficient second threshold value is larger than the potential evaluation coefficient first threshold value, and comparing the potential evaluation coefficient with the potential evaluation coefficient first threshold value and the potential evaluation coefficient second threshold value respectively;
if the potential evaluation coefficient is smaller than the first threshold value of the potential evaluation coefficient, the inspection robot is higher in operation reliability, stability and execution efficiency, and a reliable signal is generated;
if the potential evaluation coefficient is greater than or equal to the first threshold value of the potential evaluation coefficient and less than or equal to the second threshold value of the potential evaluation coefficient, the running state of the inspection robot is between reliable and unreliable, and further analysis is needed to generate an uncertain signal;
if the potential evaluation coefficient is larger than a second threshold value of the potential evaluation coefficient, the potential evaluation coefficient indicates that abnormal behavior, performance degradation and possibility of fault occurrence are greatly increased in the running process of the inspection robot, an unreliable signal is generated, and an early warning prompt is sent to warn related personnel of emergency maintenance or scrapping arrangement;
according to the invention, the potential evaluation coefficient for evaluating the running state of the inspection robot is obtained by collecting the maximum abnormality index of the motor, the battery stability index, the average increment of the instruction execution response time and the control software fault rate of the inspection robot, and is used for analyzing the potential problems and risks of the operation of the inspection robot, so that the problems can be found and solved early, and the reliability and stability of the inspection robot are improved; and meanwhile, a first threshold value of the potential evaluation coefficient and a second threshold value of the potential evaluation coefficient are set, the potential evaluation coefficient is compared with the first threshold value of the potential evaluation coefficient and the second threshold value of the potential evaluation coefficient respectively, classification and warning of the running state of the inspection robot are facilitated, emergency measures are conveniently and immediately adopted for classification judgment to avoid possible accidents or faults, and safety and continuity of chemical production are ensured.
The re-analysis module operation includes the following:
counting the inspection robots generating uncertain signals, collecting a plurality of potential evaluation coefficients of each inspection robot within a period of recent working time, calculating the average value and the discrete degree value of the potential evaluation coefficients, setting an average threshold value and a discrete degree threshold value, comparing the average value and the discrete degree value with the average threshold value and the discrete degree threshold value respectively, and if the average value is larger than the average value threshold value and the discrete degree value is larger than the discrete degree threshold value, indicating that the inspection robots are in an unstable running state, and generating unreliable signals;
if the average value is larger than the average threshold value and the discrete degree value is smaller than the discrete degree threshold value, or the average value is smaller than the average threshold value, the discrete degree value is larger than the discrete degree threshold value, which indicates that the running state of the inspection robot is general, the average value is compared with a potential evaluation coefficient second threshold value, and if the average value is smaller than the potential evaluation coefficient second threshold value, a signal to be maintained is generated; if the average value is smaller than or equal to the potential evaluation coefficient second threshold value, generating an unreliable signal;
if the average value is smaller than the average threshold value and the discrete degree value is smaller than the discrete degree threshold value, the running state of the inspection robot is stable, the average value is compared with the first threshold value of the potential evaluation coefficient, and if the average value is smaller than or equal to the first threshold value of the potential evaluation coefficient, a reliable signal is generated; and if the average value is larger than the first threshold value of the potential evaluation coefficient, generating a signal to be maintained.
The maintenance adjustment module operation includes the following:
counting the number of inspection robots generating signals to be maintained, sorting the inspection robots from small to large according to an average value, maintaining, correcting maintenance frequency, and the calculation formula is as follows:
；
wherein CMF is a correction maintenance frequency, MF is an initial maintenance frequency, AVG is an average value, and the correction maintenance frequency is used for replacing the initial maintenance frequency;
for example, if the initial maintenance frequency is 15 days, the average value is 0.5, the corrected maintenance frequency isThe original maintenance is changed from 15 days to 6 days.
According to the invention, a plurality of potential evaluation coefficients obtained in a period of time are analyzed, an average value and a discrete degree value are calculated according to the plurality of potential evaluation coefficients, whether the inspection robot in the current stage is accidental or in a normalized presentation state is poor is analyzed, an uncertain signal is further analyzed according to an analysis result to be divided into unreliable signals and signals to be maintained, and the initial maintenance frequency is corrected by the average value aiming at the signals to be maintained, so that personalized maintenance of the machine can be realized; machines with higher potential evaluation coefficients can increase maintenance frequency, prevent faults from occurring, and improve reliability and stability of the machines; and the machine with lower potential evaluation coefficient can reduce maintenance frequency, save maintenance resources and enable the machine to be better used for the maintenance of high-risk machines.
The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
Claims (3)
1. The chemical safety production inspection control system is characterized by comprising the following steps:
the system comprises a parameter acquisition module, a comprehensive evaluation module, an analysis judgment module, a re-analysis module and a maintenance adjustment module, wherein the modules are connected through signals;
the parameter acquisition module acquires mechanical parameters and software parameters of the inspection robot and sends the acquired parameters to the comprehensive evaluation module;
the comprehensive evaluation module receives the acquired parameters, obtains potential evaluation coefficients according to the acquired parameters, and sends the acquired coefficient signals to the analysis judgment module;
the analysis judging module sets a potential evaluation coefficient first threshold value and a potential evaluation coefficient second threshold value, compares the potential evaluation coefficient with the potential evaluation coefficient first threshold value and the potential evaluation coefficient second threshold value respectively, generates a reliable signal, an uncertain signal and an unreliable signal according to the comparison result, and sends the judged signals to the analysis module;
the analysis module collects the average value and the discrete degree value of the uncertain signal inspection robot, compares the average value and the discrete degree value with corresponding threshold values respectively, generates a signal to be maintained and an unreliable signal according to a comparison result, and sends a re-judging signal to the monitoring and adjusting module;
the maintenance adjustment module corrects the maintenance frequency of the inspection robot to be maintained according to the average value;
counting the inspection robots participating in inspection, and collecting mechanical parameters and software parameters of the inspection robots;
the mechanical parameters comprise a maximum abnormality index of the motor and a battery stability index;
the software parameters comprise the average increment of instruction execution response time and control software failure rate;
the potential evaluation coefficient is obtained by normalizing the maximum abnormality index of the motor, the stability index of the battery, the average increment of the instruction execution response time and the failure rate of control software, and the calculation formula is as follows:
；
wherein MMEI, BSI, AMIRT, CSR respectively represents a maximum abnormality index of the motor, a battery stability index, an average increase in instruction execution response time, a failure rate of control software,respectively a maximum abnormality index of the motor, a battery stability index, an average increase of instruction execution response time and a preset proportionality coefficient of a control software failure rate,are all greater than 0;
the analysis and judgment module operation comprises the following contents:
setting a potential evaluation coefficient first threshold value and a potential evaluation coefficient second threshold value, wherein the potential evaluation coefficient second threshold value is larger than the potential evaluation coefficient first threshold value, and comparing the potential evaluation coefficient with the potential evaluation coefficient first threshold value and the potential evaluation coefficient second threshold value respectively;
if the potential evaluation coefficient is smaller than the first threshold value of the potential evaluation coefficient, generating a reliable signal;
if the potential evaluation coefficient is greater than or equal to the first threshold value of the potential evaluation coefficient and less than or equal to the second threshold value of the potential evaluation coefficient, generating an uncertain signal;
if the potential evaluation coefficient is larger than the potential evaluation coefficient second threshold, generating an unreliable signal and sending out an early warning prompt;
the re-analysis module operation includes the following:
counting the inspection robots generating uncertain signals, collecting a plurality of potential evaluation coefficients of each inspection robot within a period of recent working time, calculating the average value and the discrete degree value of the potential evaluation coefficients, setting an average threshold value and a discrete degree threshold value, comparing the average value and the discrete degree value with the average threshold value and the discrete degree threshold value respectively, and if the average value is larger than the average value threshold value and the discrete degree value is larger than the discrete degree threshold value, indicating that the inspection robots are in an unstable running state, and generating unreliable signals;
if the average value is larger than the average threshold value and the discrete degree value is smaller than the discrete degree threshold value, or if the average value is smaller than the average threshold value and the discrete degree value is larger than the discrete degree threshold value, comparing the average value with a potential evaluation coefficient second threshold value, and if the average value is smaller than the potential evaluation coefficient second threshold value, generating a signal to be maintained; if the average value is smaller than or equal to the potential evaluation coefficient second threshold value, generating an unreliable signal;
if the average value is smaller than the average threshold value and the discrete degree value is smaller than the discrete degree threshold value, the running state of the inspection robot is stable, the average value is compared with the first threshold value of the potential evaluation coefficient, and if the average value is smaller than or equal to the first threshold value of the potential evaluation coefficient, a reliable signal is generated; if the average value is larger than the first threshold value of the potential evaluation coefficient, generating a signal to be maintained;
the maintenance adjustment module operation includes the following:
counting the number of inspection robots generating signals to be maintained, sorting the inspection robots from small to large according to an average value, maintaining, correcting maintenance frequency, and the calculation formula is as follows:
；
in the formula, CMF is a correction maintenance frequency, MF is an initial maintenance frequency, AVG is an average value, and the correction maintenance frequency is used for replacing the initial maintenance frequency.
2. The chemical safety production inspection control system according to claim 1, wherein:
the acquisition logic of the maximum abnormality index of the motor is as follows:
measuring the real-time rotating speed of a motor through a rotating speed sensor, and monitoring the rotating speed of the motor through a Hall sensor;
step two, obtaining the rotation speed change rate of the motor by recording the rotation speed change condition in a period of time;
step three, according to the collected parameter data, the following example formula is established to calculate the abnormal rotating speed index of the motor:
the rotation speed abnormality index formula:
abnormality index= (real-time rotation speed value-average rotation speed value)/standard deviation;
the real-time rotating speed value is obtained through a sensor or monitoring equipment; the average rotating speed value is obtained by calculating the average value of the real-time rotating speed value in a period of time; the standard deviation is obtained by calculating the standard deviation of the real-time rotating speed value;
counting the abnormal indexes of all motors of the inspection robot, recording the maximum abnormal index and marking the maximum abnormal index as the maximum abnormal index of the motor;
the acquisition logic of the battery stability index is as follows:
measuring a real-time temperature value of a battery through a temperature sensor, and placing the temperature sensor on the surface of the battery to obtain accurate temperature data;
step two, according to the collected temperature parameter data, the following example formula is established to calculate the temperature stability index of the battery:
battery stability index= (real-time temperature change rate-average temperature change rate)/standard deviation;
the real-time temperature change rate is obtained by recording the temperature value of the battery and calculating the temperature change difference between adjacent time points by dividing the time interval; the average temperature change rate is obtained by calculating the average value of the real-time temperature change rate; the standard deviation is obtained by calculating the standard deviation of the real-time temperature change rate.
3. The chemical safety production inspection control system according to claim 2, wherein:
the instruction execution response time average increment acquisition logic is:
step one, selecting and calculating the difference between the response time of each instruction and the initial response time, and calculating the average value of the difference as an increment;
step two, recording initial instruction response time of the inspection robot in a normal working state, wherein the initial instruction response time is a reference value used as a base for comparison;
step three, sending instructions to the inspection robot, recording response time of each instruction, and measuring the sending and response time of the instructions by using a timer;
step four, calculating the response time increment of each instruction, namely the difference value between the response time of the instruction and the initial response time by using the recorded response time and the initial response time of each instruction; adding the response time increment of all instructions, dividing the response time increment by the number of the instructions to obtain the average increase of the response time of instruction execution;
the acquisition logic of the control software failure rate is as follows:
firstly, acquiring the running time, the fault occurrence times and the maintenance time of control software used by the inspection robot for a period of time, and acquiring the data according to a fault report, a log file and a maintenance record;
step two, calculating the failure rate of the inspection robot control software by using the data of the failure occurrence times and the running time, wherein the calculation formula is as follows:
failure rate= (failure occurrence number/running time) ×time unit.
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