A sensor data acquisition and processing method based on big data

Abstract

The present application relates to big data processing technical field, specifically be a kind of sensor data acquisition, processing method based on big data.The present application first obtains the historical environmental characteristic data of detection target by big data analysis technology, then environmental adaptability analysis is carried out according to historical environmental characteristic data, and the operating state value of sensor is obtained by analyzing the sensor influence state to sensor operating data, further obtains the sensor influence state corresponding to detection target, and further comprehensively analyzes the operating state value of sensor and sensor influence state to obtain acquisition frequency setting information, and acquisition frequency setting information is obtained by sensor control information execution analysis, and according to acquisition frequency table setting information, corresponding acquisition frequency adjustment is executed, and the adaptability of different detection environments is increased by the adaptability adjustment of sensor acquisition frequency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of big data processing, in particular to a sensor data acquisition and processing method based on big data. BACKGROUND

[0002] A sensor is a detection device that can sense the information of the measured quantity and convert the sensed information into an electrical signal or other required form of information output according to a certain rule, to meet the requirements of information transmission, processing, storage, display, recording and control. The existence and development of sensors give objects such as touch, taste and smell, and make objects come to life. A sensor is an extension of the human senses. Sensors have the characteristics of miniaturization, digitization, intelligence, multifunctionality, systematization and networking, and are the primary step in achieving automatic detection and automatic control.

[0003] When a traditional sensor is running in a relatively closed environment such as battery health monitoring and production equipment, the sensor will generate a large amount of heat energy during frequent data acquisition and processing, which will affect the sensor and cause the acquisition accuracy to decrease. Therefore, the existing acquisition frequency is mostly fixed acquisition, which is difficult to automatically adjust the acquisition state according to the environmental state, and has low adaptability. SUMMARY

[0004] The present application provides a sensor data acquisition and processing method based on big data, which solves the above technical problems.

[0005] The present application provides a sensor data acquisition and processing method based on big data, which solves the above technical problems.

[0006] Step one: obtain the historical environmental characteristic data of the detection target through big data analysis technology, and obtain the sensor influence state through environmental adaptability analysis according to the historical environmental characteristic data.

[0007] As a further improvement of the present application, the environmental adaptability analysis according to the historical environmental characteristic data is as follows:

[0008] The historical environmental characteristic data includes environmental parameter data of the detection target corresponding to multiple detection periods and spatial structure information of the detection target; a corresponding three-dimensional model is obtained according to the spatial structure information of the detection target; the environmental parameter data includes environmental temperature and humidity data and electromagnetic data;

[0009] The detection time points corresponding to each detection period are acquired, and the environment temperature and humidity data corresponding to each detection time point are acquired. The environment temperature and humidity data are used to obtain the environment temperature and humidity. The environment temperature and humidity of each detection time point are matched with the three-dimensional model of the corresponding detection target to obtain the spatial temperature thermal distribution data and the spatial humidity thermal distribution data of the three-dimensional model corresponding to the detection target. The limit running temperature interval and the limit running humidity interval corresponding to the sensor are obtained. The spatial temperature thermal distribution data are matched with the limit running temperature interval to obtain the corresponding temperature feasible space information. Similarly, the spatial humidity thermal distribution data are matched with the limit running humidity interval to obtain the corresponding humidity feasible space information. The temperature feasible space information and the humidity feasible space information are matched to obtain the health running space corresponding to the detection target. The position information of each sensor is obtained, and the position information of each sensor is matched with the health running space of the detection target to obtain the corresponding temperature and humidity influence state. The temperature and humidity influence states corresponding to each detection time point are collected to obtain the temperature and humidity influence law corresponding to each detection period.

[0010] The temperature and humidity influence law is represented by the temperature and humidity change law of each detection time point corresponding to each detection period, and the change of the health running space caused by the change of the temperature and humidity change law. The temperature and humidity influence laws corresponding to each detection period are compared, and the temperature and humidity influence law with the highest repetition degree is marked as the normal temperature and humidity influence information corresponding to the detection target.

[0011] The electromagnetic data corresponding to each sensor of the detection target are identified to obtain the electromagnetic interference intensity corresponding to each sensor. The interference intensity threshold is obtained in advance. The part of the electromagnetic interference intensity exceeding the interference intensity threshold is recorded as an electromagnetic excess value. The electromagnetic excess value is divided into a plurality of electromagnetic excess value intervals. The electromagnetic excess value intervals are sequentially sorted according to the electromagnetic interference intensity from small to large to obtain the electromagnetic influence interval serial number. An electromagnetic influence coefficient is set for each electromagnetic influence interval serial number. The electromagnetic influence coefficient increases with the increase of the electromagnetic influence interval serial number. The electromagnetic excess value corresponding to each sensor is matched with each electromagnetic excess value interval to obtain the corresponding electromagnetic influence coefficient. The electromagnetic excess value of each sensor is added to the corresponding electromagnetic influence coefficient to obtain the electromagnetic influence value. The electromagnetic influence values of each detection time point corresponding to each detection period are obtained. When the electromagnetic influence value is greater than the preset threshold, the corresponding detection time point is recorded as an electromagnetic abnormal point. The electromagnetic abnormal point and the total number of detection time points are calculated to obtain the electromagnetic abnormality proportion. The normal temperature and humidity influence information corresponding to each detection period and the electromagnetic abnormality proportion are collected to obtain the sensor influence state.

[0012] Step two: dynamic acquisition frequency setting, obtaining sensor operation data corresponding to each sensor and analyzing the sensor operation data to obtain the running state value of the sensor, and obtaining the sensor influence state corresponding to the detection target, and comprehensively analyzing the running state value of the sensor and the sensor influence state to obtain the acquisition frequency setting information.

[0013] As a further improvement of the application, the sensor operation data is analyzed, and the specific analysis steps are as follows:

[0014] The sensor operation data includes acquisition frequency and power consumption data. According to the acquisition frequency, the acquisition frequency corresponding to each acquisition time point in the detection period of each sensor is obtained, and the acquisition reference frequency is obtained. When the acquisition frequency is greater than the acquisition reference frequency, the difference between the acquisition frequency and the acquisition reference frequency is calculated to obtain the frequency influence value. The acquisition frequency of each sensor is obtained, and the acquisition frequency is the number of acquisition time points in the detection period of each sensor. The acquisition frequency is divided into multiple acquisition frequency intervals, and each acquisition frequency interval corresponds to a frequency influence value. The acquisition frequency corresponding to the sensor is matched with each acquisition frequency interval to obtain the corresponding frequency influence value.

[0015] According to the power consumption data, the power consumption value of each sensor is obtained, and the pre-set power consumption reference value is obtained. When the power consumption value corresponding to the sensor is greater than the power consumption reference value, the power consumption value of each sensor is calculated by the difference between the power consumption value and the power consumption reference value to obtain the power consumption influence value.

[0016] The frequency influence value, the frequency influence value and the power consumption influence value are normalized and the values are taken, and the running state value yxt is calculated by the formula ; wherein PC, PL and ghz represent the frequency influence value, the frequency influence value and the power consumption influence value, respectively; k1, k2 and k3 are all preset weight factors, and the values are 2.254, 2.587 and 2.014, respectively.

[0017] As a further improvement of the application, the sensor operation data is analyzed, and the specific analysis steps are as follows:

[0018] According to the sensor influence state, normal temperature and humidity influence information and electromagnetic anomaly proportion value are obtained;According to the normal temperature and humidity influence information, the running temperature and humidity of the running environment corresponding to each sensor are obtained, the values of the running temperature and humidity and the electromagnetic anomaly proportion value are taken as two right-angle sides of a right-angled triangle, a right line perpendicular to the right-angled triangle is drawn from the right-angle vertex of the right-angled triangle as a starting point, the length of the right line is equal to the running state value, a three-prism is constructed by the right-angled triangle and the right line, the volume of the three-prism is calculated and the value of the volume is marked as a total collection shadow value;The pre-set total collection shadow value interval is obtained, when the total collection shadow value is greater than the pre-total collection shadow value interval, the corresponding collection frequency setting information is generated as a reduced collection frequency.

[0019] Step three: obtaining the collection frequency setting information, and performing corresponding collection frequency adjustment according to the collection frequency table setting information.

[0020] Step four: processing and analyzing the data collected by the sensor, specifically: obtaining uniform specification collection data by cleaning and converting the data collected by the sensor, and collecting and packaging the collection data according to the time information of the collection data to obtain the collection data package corresponding to each time point.

[0021] Step five: intelligently diagnosing the collection data package obtained after processing and analysis to obtain the state information of the detection target, and generating corresponding early warning display instructions according to the state information, then performing early warning display according to the early warning display instructions, and storing the collection data package.

[0022] The technical scheme provided by the application has the beneficial effects compared with the prior art:

[0023] The application analyzes the running data of the sensor to obtain the running state value of the sensor, obtains the sensor influence state of the corresponding detection target, and comprehensively analyzes the running state value of the sensor and the sensor influence state to obtain the collection frequency setting information, thereby increasing the adaptability to different detection environments through adaptive adjustment of the collection frequency of the sensor. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical scheme of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description, the following drawings are not deliberately drawn according to the actual size and proportion, and the emphasis is on showing the main idea of the application.

[0025] Figure 1 The method flowchart of the application. DETAILED DESCRIPTION

[0026] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described, obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the protection scope of the present application.

[0027] For the convenience of understanding, the specific flow of the embodiments of the present application will be described below, please refer to Figure 1 In the embodiments of the present application, one embodiment of a sensor data acquisition and processing method based on big data includes the following steps:

[0028] Step one: environmental adaptability analysis, obtaining historical environmental characteristic data of the detection target through big data analysis technology, and obtaining the sensor influence state through environmental adaptability analysis according to the historical environmental characteristic data.

[0029] The specific analysis method according to the historical environmental characteristic data is as follows:

[0030] The historical environmental characteristic data includes environmental parameter data of the detection target corresponding to multiple detection periods and spatial structure information of the detection target; the corresponding three-dimensional model is obtained according to the spatial structure information of the detection target; the environmental parameter data includes environmental temperature and humidity data and electromagnetic data;

[0031] Obtaining the detection time points corresponding to each detection period, and obtaining the environmental temperature and humidity data corresponding to each detection time point, obtaining the environmental temperature and humidity according to the environmental temperature and humidity data, matching the environmental temperature and humidity of each detection time point with the three-dimensional model of the corresponding detection target to obtain the spatial temperature thermal distribution data and the spatial humidity thermal distribution data of the three-dimensional model of the detection target, obtaining the pre-set limit running temperature interval and the limit running humidity interval corresponding to the sensor, matching the spatial temperature thermal distribution data with the limit running temperature interval to obtain the corresponding temperature feasible space information; similarly, obtaining the corresponding humidity feasible space information according to the spatial humidity thermal distribution data and the limit running humidity interval, and overlapping and matching the temperature feasible space information and the humidity feasible space information to obtain the healthy running space corresponding to the detection target, obtaining the position information of each sensor, matching the position information of each sensor with the healthy running space of the detection target to obtain the corresponding temperature and humidity influence state, and collecting the temperature and humidity influence state corresponding to each detection time point to obtain the temperature and humidity influence law corresponding to each detection period.

[0032] The temperature and humidity influence law is represented as the temperature and humidity change law of each detection time point corresponding to each detection period, and the law of the change of the temperature and humidity influence state caused by the change of the health operation space caused by the temperature and humidity change law. The temperature and humidity influence laws corresponding to each detection period are compared, and the temperature and humidity influence law with the highest repetition degree is marked as the normal temperature and humidity influence information corresponding to the detection target.

[0033] The electromagnetic data corresponding to each sensor of the detection target is identified to obtain the electromagnetic interference intensity corresponding to each sensor. The interference intensity threshold value is obtained, and the part of the electromagnetic interference intensity exceeding the interference intensity threshold value is recorded as an electromagnetic exceeding value. The electromagnetic exceeding value is divided into a plurality of electromagnetic exceeding value intervals, and the electromagnetic exceeding value intervals are sequentially sorted according to the electromagnetic interference intensity from small to large to obtain an electromagnetic influence interval serial number. An electromagnetic influence coefficient is set for each electromagnetic influence interval serial number, and the electromagnetic influence coefficient increases with the increase of the electromagnetic influence interval serial number. The electromagnetic exceeding value corresponding to each sensor is matched with each electromagnetic exceeding value interval to obtain the corresponding electromagnetic influence coefficient. The electromagnetic exceeding value of each sensor is added to the corresponding electromagnetic influence coefficient to obtain an electromagnetic influence value. When the electromagnetic influence value is greater than the preset threshold value, the corresponding detection time point is recorded as an electromagnetic abnormal point. The electromagnetic abnormal point and the total number of detection time points are calculated to obtain an electromagnetic abnormality proportion value. The normal temperature and humidity influence information corresponding to each detection period and the electromagnetic abnormality proportion value are collected to obtain a sensor influence state.

[0034] Step two: dynamically collecting frequency setting, obtaining sensor operation data corresponding to each sensor and analyzing the sensor operation data to obtain the running state value of the sensor, and obtaining the sensor influence state of the corresponding detection target, and comprehensively analyzing the running state value of the sensor and the sensor influence state to obtain the collection frequency setting information.

[0035] The sensor operation data is analyzed, and the specific analysis steps are as follows:

[0036] The sensor operation data includes collection frequency and power consumption data. According to the collection frequency, the collection frequency corresponding to each collection time point in each detection period of each sensor is obtained. The preset collection reference frequency is obtained, and when the collection frequency is greater than the collection reference frequency, the difference between the collection frequency and the collection reference frequency is calculated to obtain a frequency influence value. The collection frequency of each sensor is obtained, which is the number of collection time points in the detection period corresponding to each sensor. The collection frequency is divided into a plurality of collection frequency intervals, and each collection frequency interval corresponds to a frequency influence value. The collection frequency of the sensor is matched with each collection frequency interval to obtain the corresponding frequency influence value.

[0037] According to the power consumption data, the power consumption values of the sensors are obtained, the preset power consumption reference value is obtained, when the power consumption value corresponding to the sensor is greater than the power consumption reference value, the power consumption values of the sensors are calculated by difference with the power consumption reference value to obtain the power consumption influence value.

[0038] The frequency influence value, the frequency influence value and the power consumption influence value are normalized and the values are taken, and the running state value yxt is calculated by formula ; wherein, PC, PL and ghz represent the frequency influence value, the frequency influence value and the power consumption influence value respectively; k1, k2 and k3 are all preset weight factors, and the values are 2.254, 2.587 and 2.014 respectively.

[0039] The running state value of the sensor and the sensor influence state are comprehensively analyzed, and the specific analysis steps are as follows:

[0040] According to the sensor influence state, the normal temperature and humidity influence information and the electromagnetic anomaly proportion value are obtained; according to the normal temperature and humidity influence information, the running temperature and humidity of the running environment corresponding to each sensor are obtained, the values of the running temperature and humidity and the electromagnetic anomaly proportion value are taken as two right angles of a right triangle, a straight line perpendicular to the right triangle is drawn from the right angle vertex of the right triangle as the starting point, the length of the straight line is equal to the running state value, a three-prism is constructed with the right triangle and the straight line, the volume of the three-prism is calculated and the value of the volume is marked as the total collection shadow value; the preset total collection shadow value interval is obtained, when the total collection shadow value is greater than the preset total collection shadow value interval, the corresponding collection frequency setting information is generated as reducing the collection frequency.

[0041] Step three: sensor control information execution analysis, obtaining the collection frequency setting information, and adjusting the corresponding collection frequency according to the collection frequency table setting information.

[0042] Step four: data processing analysis, processing and analyzing the data collected by the sensor, which is specifically: unified specification collection data is obtained by cleaning and transforming the data collected by the sensor, and the collection data is packaged according to the time information of the collection data to obtain the collection data package corresponding to each time point.

[0043] Step five: intelligent diagnosis analysis and storage, obtaining the state information of the detection target by intelligent diagnosis of the collection data package obtained after processing and analysis, and generating the corresponding early warning display instruction according to the state information, then performing early warning display according to the early warning display instruction, and storing the collection data package.

[0044] The above-described embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalent replacements; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A big data-based sensor data collection and processing method, characterized in that, The method comprises the following steps: Step 1: obtaining historical environmental characteristic data of the detection target through big data analysis technology, and obtaining a sensor influence state through environmental adaptability analysis according to the historical environmental characteristic data, the specific analysis method being as follows: The historical environmental characteristic data comprises environmental parameter data of the detection target in multiple detection periods and spatial structure information of the detection target; a corresponding three-dimensional model is obtained according to the spatial structure information of the detection target; the environmental parameter data comprises environmental temperature and humidity data and electromagnetic data; corresponding detection time points are obtained, and corresponding environmental temperature and humidity data of the detection time points are obtained; the environmental temperature and humidity of each detection time point are matched with the three-dimensional model of the corresponding detection target to obtain spatial temperature thermal distribution data and spatial humidity thermal distribution data of the three-dimensional model of the detection target; a pre-set limit running temperature interval and a limit running humidity interval corresponding to the sensor are obtained, and the spatial temperature thermal distribution data are matched with the limit running temperature interval to obtain corresponding temperature feasible space information; similarly, the spatial humidity thermal distribution data are matched with the limit running humidity interval to obtain corresponding humidity feasible space information; the temperature feasible space information and the humidity feasible space information are matched to obtain a healthy running space corresponding to the detection target; position information of each sensor is obtained, and the position information of each sensor is matched with the healthy running space of the detection target to obtain a corresponding temperature and humidity influence state; the temperature and humidity influence states corresponding to each detection time point are collected to obtain temperature and humidity influence rules corresponding to each detection period; the temperature and humidity influence rules corresponding to each detection period are compared, and the rule with the highest repetition degree is marked as normal temperature and humidity influence information corresponding to the detection target; electromagnetic data corresponding to each sensor of the detection target are identified to obtain electromagnetic interference intensity corresponding to each sensor; a pre-set interference intensity threshold is obtained; a part of the electromagnetic interference intensity exceeding the interference intensity threshold is recorded as an electromagnetic exceeding value; the electromagnetic exceeding value is divided into multiple electromagnetic exceeding value intervals; the electromagnetic exceeding value intervals are sequentially sorted according to the electromagnetic interference intensity from small to large to obtain electromagnetic influence interval serial numbers; an electromagnetic influence coefficient is set for each electromagnetic influence interval serial number, and the electromagnetic influence coefficient increases with the increase of the electromagnetic influence interval serial number; the electromagnetic exceeding value corresponding to each sensor is matched with each electromagnetic exceeding value interval to obtain a corresponding electromagnetic influence coefficient; the electromagnetic exceeding value of each sensor is added to the corresponding electromagnetic influence coefficient to obtain an electromagnetic influence value; the electromagnetic influence values of each detection time point corresponding to each detection period are obtained; when the electromagnetic influence value is greater than a pre-set threshold, the corresponding detection time point is recorded as an electromagnetic abnormal point; the electromagnetic abnormal point is compared with the total number of detection time points to obtain an electromagnetic abnormality proportion value; the normal temperature and humidity influence information corresponding to each detection period and the electromagnetic abnormality proportion value are collected to obtain a sensor influence state. Step two: obtain sensor running data corresponding to each sensor and analyze the sensor running data to obtain the running state value of the sensor, obtain the sensor influence state corresponding to the detection target, and comprehensively analyze the running state value of the sensor and the sensor influence state to obtain the collection frequency setting information; The sensor running data is analyzed, and the specific analysis steps are as follows: The sensor running data includes collection frequency and power consumption data. According to the collection frequency, the collection frequency corresponding to each collection time point in the detection period of each sensor is obtained. The pre-set collection reference frequency is obtained. When the collection frequency is greater than the collection reference frequency, the difference between the collection frequency and the collection reference frequency is calculated to obtain the frequency influence value. The collection frequency of each sensor is obtained, and the collection frequency is divided into multiple collection frequency intervals. Each collection frequency interval corresponds to a frequency influence value. The collection frequency of the sensor is matched with each collection frequency interval to obtain the corresponding frequency influence value. According to the power consumption data, the power consumption value of each sensor is obtained. The pre-set power consumption reference value is obtained. When the power consumption value of the sensor is greater than the power consumption reference value, the difference between the power consumption value of each sensor and the power consumption reference value is calculated to obtain the power consumption influence value. The frequency influence value, the frequency influence value and the power consumption influence value are comprehensively calculated to obtain the running state value. The running state value of the sensor and the sensor influence state are comprehensively analyzed, and the specific analysis steps are as follows: According to the sensor influence state, the normal temperature and humidity influence information and the electromagnetic anomaly proportion value are obtained. According to the normal temperature and humidity influence information, the running temperature and humidity of the running environment corresponding to each sensor are obtained. The values of the running temperature and humidity and the electromagnetic anomaly proportion value are taken as the two right angles of a right triangle to construct a right triangle. A straight line perpendicular to the right triangle is drawn from the right angle vertex of the right triangle as the starting point. The length of the straight line is equal to the running state value. A three-prism is constructed with the right triangle and the straight line. The volume of the three-prism is calculated and the value of the volume is marked as the total collection shadow value. The pre-set total collection shadow value interval is obtained. When the total collection shadow value is greater than the pre-total collection shadow value interval, the corresponding collection frequency setting information is generated as the collection frequency is reduced. Step three: sensor control information execution analysis; Step four: processing and analysis of the data collected by the sensor; Step five: intelligent diagnosis analysis and storage. 2.The big data based sensor data collection and processing method according to claim 1, wherein, The sensor control information execution analysis is as follows: the collection frequency setting information is obtained, and the corresponding collection frequency adjustment is executed according to the collection frequency table setting information. 3.The big data based sensor data collection and processing method of claim 1, wherein, The processing and analysis of the data collected by the sensor is as follows: the collection data of uniform specification is obtained by cleaning and transforming the data collected by the sensor. According to the time information of the collection data, the collection data is collected and packaged to obtain the collection data package corresponding to each time point.

4. The big data based sensor data collection and processing method of claim 1, wherein, The intelligent diagnosis analysis and storage is as follows: the state information of the detection target is obtained by intelligent diagnosis of the collection data package obtained after processing and analysis, and the corresponding early warning display instruction is generated according to the state information. Then, the early warning display is performed according to the early warning display instruction, and the collection data package is stored.

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