CN116026389A - Intelligent sensor operation detection system based on data analysis - Google Patents
Intelligent sensor operation detection system based on data analysis Download PDFInfo
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- CN116026389A CN116026389A CN202310211514.0A CN202310211514A CN116026389A CN 116026389 A CN116026389 A CN 116026389A CN 202310211514 A CN202310211514 A CN 202310211514A CN 116026389 A CN116026389 A CN 116026389A
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Abstract
The invention belongs to the technical field of sensor detection, and particularly relates to an intelligent sensor operation detection system based on data analysis, which comprises a server, a data storage module, a maintenance suitability judging module, an environment damage causing backtracking module, an operation damage causing backtracking module and a sensor monitoring and early warning module; according to the invention, the sensor monitoring and early warning module is used for detecting and analyzing the corresponding sensor, and the environmental damage-causing evaluation information and the operation damage-causing evaluation information are combined to judge whether to generate the monitoring and early warning signal, so that the analysis is more comprehensive, the detection and analysis result is more accurate, the corresponding manager timely performs cause investigation and judgment after receiving the monitoring and early warning signal and performs corresponding treatment, the safe and stable operation of the corresponding sensor is ensured, the maintenance suitability judgment and analysis of the corresponding sensor is performed through the maintenance suitability judgment module, the maintenance and overhaul are more timely, and the subsequent normal and stable operation of the corresponding intelligent sensor is further ensured.
Description
Technical Field
The invention relates to the technical field of sensor detection, in particular to an intelligent sensor operation detection system based on data analysis.
Background
The sensor is a detection device, can sense the measured information, and can convert the sensed information into an electric signal or other information output in a required form according to a certain rule so as to meet the requirements of information transmission, processing, storage, display, recording, control and the like, and is a primary component for realizing automatic detection and automatic control, and is generally divided into a thermosensitive element, a photosensitive element, a gas-sensitive element, a force-sensitive element, a magnetic-sensitive element, a humidity-sensitive element, a sound-sensitive element, a radioactive ray-sensitive element, a color-sensitive element, a taste-sensitive element and the like according to the basic sensing function of the sensor;
at present, when the intelligent sensor operates, the operation condition of the intelligent sensor is difficult to effectively monitor and reasonably analyze, corresponding management staff cannot timely know the abnormality of the intelligent sensor and make countermeasures, the intelligent sensor is difficult to judge the maintenance suitability, the corresponding management staff cannot timely overhaul and maintain the corresponding sensor, and the stable operation of the sensor is not guaranteed;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to provide an intelligent sensor operation detection system based on data analysis, which solves the problems that the operation condition of an intelligent sensor is difficult to effectively monitor and reasonably analyze, the maintenance suitability of the intelligent sensor is difficult to judge, corresponding management staff cannot timely overhaul and maintain the corresponding sensor, and the stable operation of the sensor is not guaranteed in the prior art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an intelligent sensor operation detection system based on data analysis comprises a server, a data storage module, a maintenance suitability judging module, an environment damage causing backtracking module, an operation damage causing backtracking module and a sensor monitoring and early warning module; the maintenance suitability judging module is used for carrying out maintenance suitability judging and analyzing on the corresponding sensor o, determining whether to generate a maintenance early warning signal based on the maintenance suitability judging and analyzing, and sending the maintenance early warning signal to the sensor management and control terminal through the server when the maintenance early warning signal is generated;
the environment damage backtracking module is used for carrying out environment damage backtracking analysis on the corresponding sensor o based on the environment information of the corresponding sensor o in the history operation process, generating environment damage coefficients through the environment damage analysis, generating environment damage evaluation symbols H-1 or H-2 based on the environment damage coefficients through judgment analysis, and sending the environment damage evaluation symbols H-1 or H-2 to the server;
the operation damage-causing backtracking module is used for carrying out operation damage-causing backtracking analysis on the corresponding sensor o based on the operation information of the corresponding sensor o in the history operation process, generating an operation damage-causing coefficient through the operation damage-causing backtracking analysis, generating an operation damage-causing evaluation symbol Y-1 or Y-2 based on the operation damage-causing coefficient and through analysis and judgment, and sending the operation damage-causing evaluation symbol Y-1 or Y-2 to the server;
the server sends an environmental damage-causing evaluation symbol H-1 or H-2 and an operation damage-causing evaluation symbol Y-1 or Y-2 to a sensor monitoring and early warning module, wherein the sensor monitoring and early warning module is used for carrying out detection and analysis on a corresponding sensor o, combining the environmental damage-causing evaluation information and the operation damage-causing evaluation information to judge whether to generate a monitoring and early warning signal, and sending the monitoring and early warning signal to the server when the monitoring and early warning signal is generated; and after receiving the monitoring and early warning signals, the server sends the monitoring and early warning signals to the corresponding sensor management and control terminal.
Further, the specific operation process of the maintenance suitability judging module comprises the following steps:
acquiring the occurrence date of each fault of the corresponding sensor o in the historical operation process, and marking the time interval of two groups of faults of adjacent time as a fault time adjacent value; acquiring all fault time adjacent difference values, establishing a fault adjacent difference set according to all the fault time adjacent difference values, and performing variance calculation on the fault adjacent difference set to acquire a fault occurrence periodicity coefficient; the method comprises the steps of calling a preset fault occurrence periodic coefficient threshold value through a data storage module, comparing the fault occurrence periodic coefficient with the preset fault occurrence periodic coefficient threshold value in a numerical mode, and if the fault occurrence periodic coefficient is smaller than or equal to the preset fault occurrence periodic coefficient threshold value, summing a fault adjacent difference set and taking an average value to obtain a fault time average value;
obtaining a last fault occurrence date closest to a current date, performing difference value calculation on the current date and the last fault occurrence date to obtain a fault time actual measurement value, and performing ratio calculation on the fault time actual measurement value and a fault time difference mean value to obtain a maintenance initial judgment coefficient; and a data storage module is used for calling a preset initial maintenance judgment coefficient threshold value, the initial maintenance judgment coefficient is compared with the preset initial maintenance judgment coefficient threshold value in numerical value, if the initial maintenance judgment coefficient is larger than or equal to the preset initial maintenance judgment coefficient threshold value, a maintenance early warning signal is generated, and if the initial maintenance judgment coefficient is smaller than the preset initial maintenance judgment coefficient threshold value, a maintenance early warning signal is not generated.
Further, if the periodical coefficient of fault occurrence is greater than a preset periodical coefficient threshold of fault occurrence, sequencing all fault time adjacent difference values from big to small, establishing an end fault adjacent difference set by n groups of fault time adjacent difference values at the end, summing the end fault adjacent difference sets, taking an average value to obtain an accurate fault interval value, and calculating the ratio of the actually measured fault time value to the accurate fault interval value to obtain a maintenance final judgment coefficient; and a data storage module is used for calling a preset maintenance final judgment coefficient threshold value, the maintenance final judgment coefficient is compared with the preset maintenance final judgment coefficient threshold value in numerical value, if the maintenance final judgment coefficient is larger than or equal to the preset maintenance final judgment coefficient threshold value, a maintenance early warning signal is generated, and if the maintenance final judgment coefficient is smaller than the preset maintenance final judgment coefficient threshold value, the maintenance early warning signal is not generated.
Further, the specific operation process of the environment damage backtracking module includes:
acquiring a temperature curve and a humidity curve of an environment where a corresponding sensor o is located in a historical operation process, marking the temperature curve and the humidity curve as a historical temperature change curve and a historical humidity change curve, placing the historical temperature change curve and the historical humidity change curve into a temperature and humidity rectangular coordinate system, wherein an X axis of the temperature and humidity rectangular coordinate system represents time, and a Y axis of the temperature and humidity rectangular coordinate system represents temperature/humidity; the method comprises the steps of obtaining heavy damage duration data, medium damage duration data and light damage duration data of a corresponding sensor o through temperature-humidity curve analysis, and obtaining environment damage coefficients through numerical calculation of the heavy damage duration data, the medium damage duration data and the light damage duration data; and the data storage module is used for retrieving a preset environmental damage coefficient threshold value, comparing the environmental damage coefficient with the preset environmental damage coefficient threshold value in a numerical mode, generating an environmental damage evaluation symbol H-1 if the environmental damage coefficient is greater than or equal to the preset environmental damage coefficient threshold value, and generating an environmental damage evaluation symbol H-2 if the environmental damage coefficient is greater than or equal to the preset environmental damage coefficient threshold value.
Further, the specific analysis process of the temperature-humidity curve analysis is as follows:
setting an upper temperature ray and a lower temperature ray which are parallel to an X axis by taking (0, W1) and (0, W2) as endpoints in a temperature and humidity rectangular coordinate system, and setting an upper wet ray and a lower wet ray which are parallel to the X axis by taking (0, S1) and (0, S2) as endpoints; marking a time period of the history temperature change curve between the upper temperature ray and the lower temperature ray as a suitable temperature time period, marking a time period of the history humidity change curve between the upper humidity ray and the lower humidity ray as an abnormal temperature time period, marking a time period of the history humidity change curve between the upper humidity ray and the lower humidity ray as a suitable humidity time period, and marking a time period of the history humidity change curve not between the upper humidity ray and the lower humidity ray as an abnormal humidity time period;
the method comprises the steps of obtaining all different-temperature time periods and different-humidity time periods and all suitable-temperature time periods and suitable-humidity time periods, marking total time length values represented by X-axis coincident parts of the different-temperature time periods and the different-humidity time periods as heavy damage time length data, marking total time length values represented by X-axis coincident parts of the suitable-temperature time periods and the suitable-humidity time periods as light damage time length data, and performing difference value calculation on historical total operation time length of a corresponding sensor o, the heavy damage time length data and the light damage time length data to obtain medium damage time length data.
Further, the specific operation process of the operation damage-causing backtracking module includes:
obtaining the frequency of faults of the corresponding sensor o in the history operation process, the frequency of oversrange detection in the history operation process and the duration of each oversrange detection, and summing up and calculating the duration of all oversrange detection to obtain the total oversrange detection value; performing numerical calculation on the frequency of faults, the frequency of the oversrange detection and the total time value of the oversrange detection to obtain an operation damage coefficient;
and a data storage module is used for calling a preset operation damage coefficient threshold value, the operation damage coefficient is compared with the preset operation damage coefficient threshold value in numerical value, if the operation damage coefficient is greater than or equal to the preset operation damage coefficient threshold value, an operation damage evaluation symbol Y-1 is generated, and if the operation damage coefficient is smaller than the preset operation damage coefficient threshold value, an operation damage evaluation symbol Y-2 is generated.
Further, the specific operation process of the sensor monitoring and early warning module comprises the following steps:
acquiring an environmental damage evaluation symbol H-1 or H-2 and an operation damage evaluation symbol Y-1 or Y-2 of a corresponding sensor o, and distributing a monitoring influence factor of a corresponding numerical value based on the environmental damage evaluation symbol H-1 or H-2 and the operation damage evaluation symbol Y-1 or Y-2; the method comprises the steps of obtaining a pressure flow wave amplitude value, a temperature and humidity wave amplitude value and a vibration frequency amplitude value of a corresponding sensor o through analysis, carrying out numerical calculation on the pressure flow wave amplitude value, the temperature and humidity wave amplitude value and the vibration frequency amplitude value to obtain a sensor live value, multiplying the sensor live value by a corresponding monitoring influence factor, and marking the product of the sensor live value and the corresponding monitoring influence factor as a sensor early warning value; and the data storage module is used for retrieving a preset sensor early-warning threshold value, carrying out numerical comparison on the sensor early-warning value and the preset sensor early-warning threshold value, generating a monitoring early-warning signal if the sensor early-warning value is greater than or equal to the preset sensor early-warning threshold value, and not generating the monitoring early-warning signal if the sensor early-warning value is smaller than the preset sensor early-warning threshold value.
Further, if H-1 n Y-1 is acquired, a monitoring influence factor JZ1 is allocated, if H-2 n Y-1 is acquired, a monitoring influence factor JZ2 is allocated, if H-1 n Y-2 is acquired, a monitoring influence factor JZ3 is allocated, and if H-2 n Y-2 is acquired, a monitoring influence factor JZ4 is allocated; wherein, the values of JZ1, JZ2, JZ3 and JZ4 are all larger than zero, and JZ1 is larger than JZ2 and JZ3 is larger than JZ4.
Further, the analysis and acquisition method of the pressure flow wave amplitude and the temperature and humidity wave amplitude is as follows:
obtaining an average voltage value and an average current value of a corresponding sensor o, carrying out difference calculation on the average voltage value and a corresponding preset standard voltage value, taking an absolute value to obtain an actual pressure-off value, carrying out difference calculation on the average current value and the corresponding preset standard voltage value, taking the absolute value to obtain an actual pressure-off value, and multiplying the actual pressure-off value and the actual pressure-off value to obtain a pressure-flow wave amplitude;
the method comprises the steps of obtaining real-time temperature and real-time humidity of an environment where a corresponding sensor o is located, calculating a difference value between the real-time temperature and a preset standard temperature value, obtaining an actual temperature-to-humidity value by taking an absolute value, calculating a difference value between the real-time temperature and the preset standard humidity value, obtaining an actual humidity-to-humidity value by taking the absolute value, and obtaining a temperature-to-humidity wave amplitude value by adding the actual temperature-to-humidity value and the actual humidity-to-humidity value.
Further, the method for analyzing and acquiring the vibration frequency amplitude comprises the following steps:
the average vibration frequency and the average vibration amplitude of the corresponding sensor o are obtained, the average vibration frequency and the average vibration amplitude are multiplied by the corresponding preset weight coefficients respectively, and the vibration frequency amplitude is obtained by adding the two groups of product values.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, the damage tracing to the external environment in the history operation process of the sensor o is realized through the environment damage tracing module, and the operation damage tracing module realizes the damage tracing to the history operation of the sensor o, so that the accuracy of the detection and analysis results of the subsequent sensor is improved; the sensor monitoring and early warning module detects and analyzes the corresponding sensor o, combines the environmental damage-causing evaluation information and the operation damage-causing evaluation information to judge whether to generate a monitoring and early warning signal, has more comprehensive analysis and more accurate detection and analysis results, and ensures the safe and stable operation of the corresponding sensor by timely performing cause investigation and judgment and corresponding processing after receiving the monitoring and early warning signal by corresponding management personnel;
2. in the invention, the maintenance suitability judging module judges and analyzes the maintenance suitability of the corresponding sensor o, and sends the maintenance early warning signal to the sensor management and control terminal through the server when the maintenance early warning signal is generated, so as to play a role in early warning and reminding, and when corresponding management personnel of the sensor management and control terminal receive the maintenance early warning signal, the corresponding intelligent sensor should be maintained and overhauled according to the need, so that the maintenance and overhaul are more timely, and the subsequent normal and stable operation of the corresponding intelligent sensor is guaranteed.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is an overall system block diagram of the present invention;
FIG. 2 is a system block diagram of a second embodiment of the present invention;
FIG. 3 is a block diagram of the communication between a server and a sensor management and control terminal according to the present invention.
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.
Embodiment one:
as shown in fig. 1 and fig. 3, the intelligent sensor operation detection system based on data analysis provided by the invention comprises a server, a data storage module, an environment damage-causing backtracking module, an operation damage-causing backtracking module and a sensor monitoring and early warning module, wherein the server is in communication connection with the data storage module, the environment damage-causing backtracking module, the operation damage-causing backtracking module and the sensor monitoring and early warning module; the server acquires intelligent sensors to be monitored, marks the corresponding sensors as o, o=1, 2, …, m, m represents the number of the sensors to be monitored and m is a positive integer greater than 1, and is beneficial to monitoring a plurality of groups of intelligent sensors;
the environment damage backtracking module carries out environment damage backtracking analysis on the corresponding sensor o based on the environment information of the corresponding sensor o in the history operation process, generates an environment damage coefficient through the environment damage analysis, generates an environment damage evaluation symbol H-1 or H-2 based on the environment damage coefficient through judgment analysis, and sends the environment damage evaluation symbol H-1 or H-2 to the server, so that damage to the environment in the history operation process of the sensor o is tracked, and accuracy of detection analysis results of subsequent sensors is improved; the specific operation process of the environment damage retrospective module is as follows:
acquiring a temperature curve and a humidity curve of an environment where a corresponding sensor o is located in a historical operation process, marking the temperature curve and the humidity curve as a historical temperature change curve and a historical humidity change curve, placing the historical temperature change curve and the historical humidity change curve into a temperature and humidity rectangular coordinate system, wherein an X axis of the temperature and humidity rectangular coordinate system represents time, and a Y axis of the temperature and humidity rectangular coordinate system represents temperature/humidity; setting an upper temperature ray and a lower temperature ray which are parallel to an X axis by taking (0, W1) and (0, W2) as endpoints in a temperature and humidity rectangular coordinate system, and setting an upper wet ray and a lower wet ray which are parallel to the X axis by taking (0, S1) and (0, S2) as endpoints; wherein W1 and W2 are the maximum value and the minimum value of a preset suitable environment temperature range, and S1 and S2 are the maximum value and the minimum value of a preset suitable environment humidity range;
marking a time period of the history temperature change curve between the upper temperature ray and the lower temperature ray as a suitable temperature time period, marking a time period of the history humidity change curve between the upper humidity ray and the lower humidity ray as an abnormal temperature time period, marking a time period of the history humidity change curve between the upper humidity ray and the lower humidity ray as a suitable humidity time period, and marking a time period of the history humidity change curve not between the upper humidity ray and the lower humidity ray as an abnormal humidity time period;
acquiring all different temperature time periods and different humidity time periods and all suitable temperature time periods and suitable humidity time periods, marking total duration values represented by X-axis overlapping parts of the different temperature time periods and the different humidity time periods as severe damage duration data ZSo, marking total duration values represented by X-axis overlapping parts of the suitable temperature time periods and the suitable humidity time periods as mild damage duration data QSo, and performing difference calculation on historical total operation duration of a corresponding sensor o and the severe damage duration data and the mild damage duration data to acquire moderate damage duration data HSo;
the environmental damage coefficient JSo is obtained by numerical calculation through the formula JSo =at1, ZSo +at2, HSo +at3, QSo and substitution of the severe damage duration data ZSo, the moderate damage duration data HSo and the mild damage duration data QSo; wherein, at1, at2 and at3 are preset proportionality coefficients, the values of at1, at2 and at3 are all larger than zero, and at1 is more than at2 and more than at3; it should be noted that, the larger the value of the environmental damage coefficient JSo, the larger the damage to the corresponding sensor o caused by the external environment during the historical operation;
the method comprises the steps of retrieving a preset environmental damage coefficient threshold value through a data storage module, comparing the environmental damage coefficient JSo with the preset environmental damage coefficient threshold value in a numerical mode, generating an environmental damage evaluation symbol H-1 if the environmental damage coefficient JSo is larger than or equal to the preset environmental damage coefficient threshold value, and generating an environmental damage evaluation symbol H-2 if the environmental damage coefficient JSo is smaller than the preset environmental damage coefficient threshold value.
The operation damage backtracking module performs operation damage backtracking analysis on the corresponding sensor o based on the operation information of the corresponding sensor o in the history operation process, generates an operation damage coefficient through the operation damage backtracking analysis, generates an operation damage evaluation symbol Y-1 or Y-2 based on the operation damage coefficient and through analysis and judgment, and sends the operation damage evaluation symbol Y-1 or Y-2 to a server, so that damage backtracking on the history operation of the sensor o is realized, and the accuracy of the detection analysis result of the subsequent sensor is improved; the specific operation process of the operation damage-causing backtracking module is as follows:
acquiring the frequency GPo of faults of the corresponding sensor o in the history operation process and the frequency CPo of overrange detection in the history operation process, wherein the overrange detection indicates that the detected actual value exceeds the range of the corresponding sensor, for example, the maximum weighing of the weighing sensor is 10 kg, the weight of an object weighed at the time is 15 kg, and the overrange detection can cause damage to the sensor; and summing the duration of all the oversrange detections to obtain a total oversrange detection value CSo; numerical calculation is carried out by using a formula JSo =tp1, GPo +tp2, CPo+tp3, CSo and substituting the frequency GPo of faults, the frequency CPo of overscan detection and the total overscan detection value CSo, and the operation damage coefficient YSo of the corresponding sensor o is obtained after the numerical calculation; wherein tp1, tp2 and tp3 are preset proportionality coefficients, the values of tp1, tp2 and tp3 are all larger than zero, and tp1 is larger than tp2 and tp3;
it should be noted that, the magnitude of the operation damage coefficient YSo is in a proportional relationship with the frequency GPo of failure, the frequency CPo of over-range detection and the total value CSo of over-range detection, and the larger the magnitude of the operation damage coefficient YSo is, the larger the damage caused to the corresponding sensor o in the historical operation process is indicated; the method comprises the steps of calling a preset operation damage coefficient threshold value through a data storage module, comparing the operation damage coefficient YSo with the preset operation damage coefficient threshold value in a numerical mode, generating an operation damage evaluation symbol Y-1 if the operation damage coefficient YSo is larger than or equal to the preset operation damage coefficient threshold value, and generating an operation damage evaluation symbol Y-2 if the operation damage coefficient YSo is smaller than the preset operation damage coefficient threshold value.
The server sends an environmental damage-causing evaluation symbol H-1 or H-2 and an operation damage-causing evaluation symbol Y-1 or Y-2 to a sensor monitoring and early warning module, and the sensor monitoring and early warning module is used for carrying out detection analysis on a corresponding sensor o and combining environmental damage-causing evaluation information and operation damage-causing evaluation information to judge whether a monitoring and early warning signal is generated or not; the specific operation process of the sensor monitoring and early warning module is as follows:
acquiring an environmental damage evaluation symbol H-1 or H-2 and an operation damage evaluation symbol Y-1 or Y-2 of a corresponding sensor o; if H-1U Y-1 is acquired, a monitoring influence factor JZ1 is allocated, if H-2U Y-1 is acquired, a monitoring influence factor JZ2 is allocated, if H-1U Y-2 is acquired, a monitoring influence factor JZ3 is allocated, and if H-2U Y-2 is acquired, a monitoring influence factor JZ4 is allocated. Wherein, the values of JZ1, JZ2, JZ3 and JZ4 are all larger than zero, and JZ1 is larger than JZ2 and JZ3 is larger than JZ4;
acquiring an average voltage value and an average current value of a corresponding sensor o, calling a preset standard voltage value and a preset standard current value through a data storage module, performing difference calculation on the average voltage value and the corresponding preset standard voltage value, acquiring an actual pressure-off value by taking an absolute value, performing difference calculation on the average current value and the corresponding preset standard voltage value, acquiring an actual pressure-off value by taking the absolute value, and multiplying the actual pressure-off value and the actual pressure-off value to acquire a pressure-current wave amplitude YLo;
acquiring real-time temperature and real-time humidity of an environment where a corresponding sensor o is located, calling a preset standard temperature value and a preset standard humidity value through a data storage module, performing difference calculation on the real-time temperature and the preset standard temperature value, acquiring an actual temperature-off value by taking an absolute value, performing difference calculation on the real-time temperature and the preset standard humidity value, acquiring an actual humidity-off value by taking the absolute value, and adding the actual temperature-off value and the actual humidity-off value to acquire a temperature-humidity wave amplitude WSo;
acquiring average vibration frequency and average vibration amplitude of a corresponding sensor o, calling preset weight coefficients corresponding to the average vibration frequency and the average vibration amplitude respectively through a data storage module, multiplying the average vibration frequency and the average vibration amplitude by the corresponding preset weight coefficients respectively, and adding the two groups of product values to acquire a vibration frequency amplitude PFo;
by the formula
Substituting the pressure flow wave amplitude YLo, the temperature and humidity wave amplitude WSo and the vibration frequency amplitude PFo to perform numerical calculation, and obtaining a sensor live value SKo after the numerical calculation, wherein bp1, bp2 and bp3 are preset proportional coefficients, and the values of bp1, bp2 and bp3 are all larger than zero; it should be noted that, the magnitude of the value of the sensor live value SKo is in a proportional relation with the pressure flow wave amplitude YLo, the temperature and humidity wave amplitude WSo and the vibration frequency amplitude PFo, the greater the value of the sensor live value SKo, the worse the current running condition of the corresponding sensor o;
multiplying the sensor live value SKo by a corresponding monitoring influence factor and marking the product of the two as a sensor early warning value YJo; the method comprises the steps of calling a preset sensor early-warning threshold value through a data storage module, comparing a sensor early-warning value YJo with the preset sensor early-warning threshold value in a numerical mode, generating a monitoring early-warning signal if the sensor early-warning value YJo is larger than or equal to the preset sensor early-warning threshold value, and not generating the monitoring early-warning signal if the sensor early-warning value YJo is smaller than the preset sensor early-warning threshold value.
The sensor monitoring and early warning module sends the monitoring and early warning signal to the server when generating the monitoring and early warning signal, the server sends the monitoring and early warning signal to the corresponding sensor management and control terminal after receiving the monitoring and early warning signal, corresponding management personnel of the sensor management and control terminal receive the monitoring and early warning signal and should timely carry out cause investigation and judgment and make corresponding processing, and check the sensor as required, and adjust the environment where the corresponding sensor is located, so that safe and stable operation of the corresponding sensor is ensured.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
Embodiment two:
as shown in fig. 2-3, the difference between the present embodiment and embodiment 1 is that the server is in communication connection with the maintenance suitability determination module, the server generates a maintenance suitability analysis signal and sends the maintenance suitability analysis signal to the maintenance suitability determination module, and the maintenance suitability determination module performs maintenance suitability determination analysis on the corresponding sensor o after receiving the maintenance suitability analysis signal; the specific operation process of the maintenance suitability judging module is as follows:
acquiring the occurrence date of each fault of the corresponding sensor o in the historical operation process, and marking the time interval of two groups of faults of adjacent time as a fault time adjacent value; acquiring all fault time adjacent difference values, marking the number of the fault time adjacent difference values as g, establishing a fault adjacent difference set of all the fault time adjacent difference values, and performing variance calculation on the fault adjacent difference set to acquire a fault occurrence periodicity coefficient; the method comprises the steps of calling a preset fault occurrence periodic coefficient threshold value through a data storage module, carrying out numerical comparison on a fault occurrence periodic coefficient and the preset fault occurrence periodic coefficient threshold value, and if the fault occurrence periodic coefficient is smaller than or equal to the preset fault occurrence periodic coefficient threshold value, indicating that the fault occurrence interval duration has periodic stability, summing a fault adjacent difference set, and taking an average value to obtain a fault time average difference value;
obtaining a last fault occurrence date closest to a current date, performing difference value calculation on the current date and the last fault occurrence date to obtain a fault time actual measurement value, and performing ratio calculation on the fault time actual measurement value and a fault time difference mean value to obtain a maintenance initial judgment coefficient WCo; the data storage module is used for calling a preset initial maintenance judgment coefficient threshold value, the initial maintenance judgment coefficient WCo is compared with the preset initial maintenance judgment coefficient threshold value in a numerical mode, if the initial maintenance judgment coefficient WCo is larger than or equal to the preset initial maintenance judgment coefficient threshold value, a maintenance early warning signal is generated, and if the initial maintenance judgment coefficient WCo is smaller than the preset initial maintenance judgment coefficient threshold value, the maintenance early warning signal is not generated.
The maintenance suitability judging module judges, analyzes and determines whether to generate a maintenance early warning signal based on the maintenance suitability, and sends the maintenance early warning signal to the sensor management and control terminal through the server when the maintenance early warning signal is generated, so that an early warning reminding effect is achieved, when corresponding management personnel of the sensor management and control terminal receive the maintenance early warning signal, the corresponding intelligent sensor is maintained and overhauled according to the requirement, the maintenance and overhauling is more timely, and the follow-up normal and stable operation of the corresponding intelligent sensor is guaranteed.
Embodiment III:
the difference between the embodiment and the embodiment 1 and the embodiment 2 is that if the fault occurrence periodicity coefficient is greater than the preset fault occurrence periodicity coefficient threshold, which indicates that the fault occurrence interval duration has no periodicity stability, all the fault time adjacency values are ordered from large to small, and n groups of fault time adjacency values at the tail end are established to form a tail end fault adjacency difference set, preferably, n/g=1/3; summing the terminal fault adjacent difference sets, taking an average value to obtain a precise fault interval value, and calculating the ratio of the fault time actual measurement value to the precise fault interval value to obtain a maintenance final judgment coefficient WZo; and a preset maintenance final judgment coefficient threshold value is called through the data storage module, the maintenance final judgment coefficient WZo is compared with the preset maintenance final judgment coefficient threshold value in numerical value, if the maintenance final judgment coefficient WZo is larger than or equal to the preset maintenance final judgment coefficient threshold value, a maintenance early warning signal is generated, and if the maintenance final judgment coefficient WZo is smaller than the preset maintenance final judgment coefficient threshold value, the maintenance early warning signal is not generated.
The working principle of the invention is as follows: when the sensor is used, the environment damage backtracking module performs environment damage backtracking analysis on the corresponding sensor o based on the environment information of the corresponding sensor o in the history operation process, so that damage backtracking on the corresponding sensor o caused by the external environment in the history operation process of the sensor o is realized, the operation damage backtracking module performs operation damage backtracking analysis on the corresponding sensor o based on the operation information of the corresponding sensor o in the history operation process, damage backtracking on the history operation of the sensor o is realized, and the accuracy of the detection analysis result of the subsequent sensor is improved; the sensor monitoring and early warning module detects and analyzes the corresponding sensor o, combines the environmental damage-causing evaluation information and the operation damage-causing evaluation information to judge whether to generate a monitoring and early warning signal, and timely performs cause investigation and judgment and corresponding processing after receiving the monitoring and early warning signal by the corresponding manager to ensure the safe and stable operation of the corresponding sensor.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (9)
1. The intelligent sensor operation detection system based on data analysis is characterized by comprising a server, a data storage module, a maintenance suitability judging module, an environment damage causing backtracking module, an operation damage causing backtracking module and a sensor monitoring and early warning module; the maintenance suitability judging module is used for carrying out maintenance suitability judging and analyzing on the corresponding sensor o, determining whether to generate a maintenance early warning signal based on the maintenance suitability judging and analyzing, and sending the maintenance early warning signal to the sensor management and control terminal through the server when the maintenance early warning signal is generated;
the environment damage backtracking module is used for carrying out environment damage backtracking analysis on the corresponding sensor o based on the environment information of the corresponding sensor o in the history operation process, generating environment damage coefficients through the environment damage analysis, generating environment damage evaluation symbols H-1 or H-2 based on the environment damage coefficients through judgment analysis, and sending the environment damage evaluation symbols H-1 or H-2 to the server;
the operation damage-causing backtracking module is used for carrying out operation damage-causing backtracking analysis on the corresponding sensor o based on the operation information of the corresponding sensor o in the history operation process, generating an operation damage-causing coefficient through the operation damage-causing backtracking analysis, generating an operation damage-causing evaluation symbol Y-1 or Y-2 based on the operation damage-causing coefficient and through analysis and judgment, and sending the operation damage-causing evaluation symbol Y-1 or Y-2 to the server;
the server sends an environmental damage-causing evaluation symbol H-1 or H-2 and an operation damage-causing evaluation symbol Y-1 or Y-2 to a sensor monitoring and early warning module, wherein the sensor monitoring and early warning module is used for carrying out detection and analysis on a corresponding sensor o, combining the environmental damage-causing evaluation information and the operation damage-causing evaluation information to judge whether to generate a monitoring and early warning signal, and sending the monitoring and early warning signal to the server when the monitoring and early warning signal is generated; and after receiving the monitoring and early warning signals, the server sends the monitoring and early warning signals to the corresponding sensor management and control terminal.
2. The intelligent sensor operation detection system based on data analysis according to claim 1, wherein the specific operation process of the maintenance suitability determination module comprises:
acquiring the occurrence date of each fault of the corresponding sensor o in the historical operation process, and marking the time interval of two groups of faults of adjacent time as a fault time adjacent value; acquiring all fault time adjacent difference values, establishing a fault adjacent difference set according to all the fault time adjacent difference values, and performing variance calculation on the fault adjacent difference set to acquire a fault occurrence periodicity coefficient; the method comprises the steps of calling a preset fault occurrence periodic coefficient threshold value through a data storage module, comparing the fault occurrence periodic coefficient with the preset fault occurrence periodic coefficient threshold value in a numerical mode, and if the fault occurrence periodic coefficient is smaller than or equal to the preset fault occurrence periodic coefficient threshold value, summing a fault adjacent difference set and taking an average value to obtain a fault time average value;
obtaining a last fault occurrence date closest to a current date, performing difference value calculation on the current date and the last fault occurrence date to obtain a fault time actual measurement value, and performing ratio calculation on the fault time actual measurement value and a fault time difference mean value to obtain a maintenance initial judgment coefficient; and a data storage module is used for calling a preset initial maintenance judgment coefficient threshold value, the initial maintenance judgment coefficient is compared with the preset initial maintenance judgment coefficient threshold value in numerical value, if the initial maintenance judgment coefficient is larger than or equal to the preset initial maintenance judgment coefficient threshold value, a maintenance early warning signal is generated, and if the initial maintenance judgment coefficient is smaller than the preset initial maintenance judgment coefficient threshold value, a maintenance early warning signal is not generated.
3. The intelligent sensor operation detection system based on data analysis according to claim 2, wherein if the periodical coefficient of fault occurrence is greater than a preset periodical coefficient threshold of fault occurrence, all fault time adjacency values are ordered from big to small, n groups of fault time adjacency values at the tail end are established to form a tail end fault adjacency difference set, the tail end fault adjacency difference set is summed and averaged to obtain a precise fault interval value, and the ratio of the actually measured fault time value to the precise fault interval value is calculated to obtain a maintenance final judgment coefficient; and a data storage module is used for calling a preset maintenance final judgment coefficient threshold value, the maintenance final judgment coefficient is compared with the preset maintenance final judgment coefficient threshold value in numerical value, if the maintenance final judgment coefficient is larger than or equal to the preset maintenance final judgment coefficient threshold value, a maintenance early warning signal is generated, and if the maintenance final judgment coefficient is smaller than the preset maintenance final judgment coefficient threshold value, the maintenance early warning signal is not generated.
4. The intelligent sensor operation detection system based on data analysis according to claim 1, wherein the specific operation process of the environment damage backtracking module comprises:
acquiring a temperature curve and a humidity curve of an environment where a corresponding sensor o is located in a historical operation process, marking the temperature curve and the humidity curve as a historical temperature change curve and a historical humidity change curve, placing the historical temperature change curve and the historical humidity change curve into a temperature and humidity rectangular coordinate system, wherein an X axis of the temperature and humidity rectangular coordinate system represents time, and a Y axis of the temperature and humidity rectangular coordinate system represents temperature/humidity; the method comprises the steps of obtaining heavy damage duration data, medium damage duration data and light damage duration data of a corresponding sensor o through temperature-humidity curve analysis, and obtaining environment damage coefficients through numerical calculation of the heavy damage duration data, the medium damage duration data and the light damage duration data; and the data storage module is used for retrieving a preset environmental damage coefficient threshold value, comparing the environmental damage coefficient with the preset environmental damage coefficient threshold value in a numerical mode, generating an environmental damage evaluation symbol H-1 if the environmental damage coefficient is greater than or equal to the preset environmental damage coefficient threshold value, and generating an environmental damage evaluation symbol H-2 if the environmental damage coefficient is greater than or equal to the preset environmental damage coefficient threshold value.
5. The intelligent sensor operation detection system based on data analysis according to claim 1, wherein the specific analysis process of the temperature-humidity curve analysis is as follows:
setting an upper temperature ray and a lower temperature ray which are parallel to an X axis by taking (0, W1) and (0, W2) as endpoints in a temperature and humidity rectangular coordinate system, and setting an upper wet ray and a lower wet ray which are parallel to the X axis by taking (0, S1) and (0, S2) as endpoints; marking a time period of the history temperature change curve between the upper temperature ray and the lower temperature ray as a suitable temperature time period, marking a time period of the history humidity change curve between the upper humidity ray and the lower humidity ray as an abnormal temperature time period, marking a time period of the history humidity change curve between the upper humidity ray and the lower humidity ray as a suitable humidity time period, and marking a time period of the history humidity change curve not between the upper humidity ray and the lower humidity ray as an abnormal humidity time period;
the method comprises the steps of obtaining all different-temperature time periods and different-humidity time periods and all suitable-temperature time periods and suitable-humidity time periods, marking total time length values represented by X-axis coincident parts of the different-temperature time periods and the different-humidity time periods as heavy damage time length data, marking total time length values represented by X-axis coincident parts of the suitable-temperature time periods and the suitable-humidity time periods as light damage time length data, and performing difference value calculation on historical total operation time length of a corresponding sensor o, the heavy damage time length data and the light damage time length data to obtain medium damage time length data.
6. The intelligent sensor operation detection system based on data analysis according to claim 5, wherein the specific operation process of the damage-causing backtracking module comprises:
obtaining the frequency of faults of the corresponding sensor o in the history operation process, the frequency of oversrange detection in the history operation process and the duration of each oversrange detection, and summing up and calculating the duration of all oversrange detection to obtain the total oversrange detection value; performing numerical calculation on the frequency of faults, the frequency of the oversrange detection and the total time value of the oversrange detection to obtain an operation damage coefficient;
and a data storage module is used for calling a preset operation damage coefficient threshold value, the operation damage coefficient is compared with the preset operation damage coefficient threshold value in numerical value, if the operation damage coefficient is greater than or equal to the preset operation damage coefficient threshold value, an operation damage evaluation symbol Y-1 is generated, and if the operation damage coefficient is smaller than the preset operation damage coefficient threshold value, an operation damage evaluation symbol Y-2 is generated.
7. The intelligent sensor operation detection system based on data analysis according to claim 1, wherein the specific operation process of the sensor monitoring and early warning module comprises:
acquiring an environmental damage evaluation symbol H-1 or H-2 and an operation damage evaluation symbol Y-1 or Y-2 of a corresponding sensor o, and distributing a monitoring influence factor of a corresponding numerical value based on the environmental damage evaluation symbol H-1 or H-2 and the operation damage evaluation symbol Y-1 or Y-2; the method comprises the steps of obtaining a pressure flow wave amplitude value, a temperature and humidity wave amplitude value and a vibration frequency amplitude value of a corresponding sensor o through analysis, carrying out numerical calculation on the pressure flow wave amplitude value, the temperature and humidity wave amplitude value and the vibration frequency amplitude value to obtain a sensor live value, multiplying the sensor live value by a corresponding monitoring influence factor, and marking the product of the sensor live value and the corresponding monitoring influence factor as a sensor early warning value; and the data storage module is used for retrieving a preset sensor early-warning threshold value, carrying out numerical comparison on the sensor early-warning value and the preset sensor early-warning threshold value, generating a monitoring early-warning signal if the sensor early-warning value is greater than or equal to the preset sensor early-warning threshold value, and not generating the monitoring early-warning signal if the sensor early-warning value is smaller than the preset sensor early-warning threshold value.
8. The intelligent sensor operation detection system based on data analysis according to claim 7, wherein if H-1 n Y-1 is acquired, a monitoring influence factor JZ1 is assigned, if H-2 n Y-1 is acquired, a monitoring influence factor JZ2 is assigned, if H-1 n Y-2 is acquired, a monitoring influence factor JZ3 is assigned, and if H-2 n Y-2 is acquired, a monitoring influence factor JZ4 is assigned; wherein, the values of JZ1, JZ2, JZ3 and JZ4 are all larger than zero, and JZ1 is larger than JZ2 and JZ3 is larger than JZ4.
9. The intelligent sensor operation detection system based on data analysis according to claim 7, wherein the analysis and acquisition method of the pressure wave amplitude and the temperature and humidity wave amplitude is as follows:
obtaining an average voltage value and an average current value of a corresponding sensor o, carrying out difference calculation on the average voltage value and a corresponding preset standard voltage value, taking an absolute value to obtain an actual pressure-off value, carrying out difference calculation on the average current value and the corresponding preset standard voltage value, taking the absolute value to obtain an actual pressure-off value, and multiplying the actual pressure-off value and the actual pressure-off value to obtain a pressure-flow wave amplitude;
acquiring real-time temperature and real-time humidity of an environment where a corresponding sensor o is located, performing difference calculation on the real-time temperature and a preset standard temperature value, taking an absolute value to acquire an actual temperature-off value, performing difference calculation on the real-time temperature and the preset standard humidity value, taking the absolute value to acquire an actual humidity-off value, and adding the actual temperature-off value and the actual humidity-off value to acquire a temperature-humidity wave amplitude;
the method for analyzing and acquiring the vibration frequency amplitude comprises the following steps:
the average vibration frequency and the average vibration amplitude of the corresponding sensor o are obtained, the average vibration frequency and the average vibration amplitude are multiplied by the corresponding preset weight coefficients respectively, and the vibration frequency amplitude is obtained by adding the two groups of product values.
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