Sensor data acquisition and processing method based on big data

Abstract

The invention relates to the technical field of big data processing, in particular to a sensor data collecting and processing method based on big data. According to the method, the detection demand influence information is obtained by comprehensively analyzing the detection demand information, the sensor influence state corresponding to the detection target is obtained, and the sensor layout optimization information is obtained by comprehensively analyzing the detection demand influence information and the sensor influence state; targeted layout optimization can be carried out on each detection target according to optimization of sensor layout, and normal operation of the sensors is guaranteed; the operation state value of the sensor is obtained by analyzing the operation data of the sensor, the sensor influence state corresponding to the detection target is obtained, the operation state value and the sensor influence state of the sensor are comprehensively analyzed to obtain the collection frequency setting information, and the collection frequency of the sensor is adaptively adjusted, so that the detection accuracy is improved. And the adaptability to different detection environments is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of big data processing, and in particular to a sensor data collection and processing method based on big data. Background Art

[0002] A sensor is a detection device that can sense the information being measured and convert the sensed information into electrical signals or other required forms of information output according to certain rules to meet the requirements of information transmission, processing, storage, display, recording and control. The existence and development of sensors have given objects senses such as touch, taste and smell, making objects come alive; sensors are an extension of the five senses of human beings. Sensors have the characteristics of miniaturization, digitization, intelligence, multi-function, systematization and networking. They are the primary link in realizing automatic detection and automatic control.

[0003] When traditional sensors are operating in relatively closed environments such as battery health monitoring and inside production equipment, they will generate a lot of heat energy during frequent data collection and processing, making the sensors easily affected, resulting in reduced collection accuracy. Moreover, even for the same type of collection environment, the corresponding sensor layout is different. Therefore, a fixed sensor layout plan will lead to the omission of key data. Summary of the invention

[0004] The present invention provides a sensor data collection and processing method based on big data, which is used to solve the above-mentioned technical problems.

[0005] The first aspect of the present invention provides a sensor data collection and processing method based on big data, comprising:

[0006] Step 1: Obtain historical environmental feature data of the detection target through big data analysis technology, and perform environmental adaptability analysis based on the historical environmental feature data to obtain the sensor impact status.

[0007] As a further improvement of the present invention, environmental usability analysis is performed based on historical environmental characteristic data, and the specific analysis method is as follows:

[0008] The historical environmental characteristic data includes environmental parameter data corresponding to multiple detection cycles of the detection target and the 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;

[0009] Obtain the detection time point corresponding to each detection cycle, and obtain the environmental temperature and humidity data corresponding to each detection time point, obtain the environmental temperature and environmental humidity according to the environmental temperature and humidity data, match the environmental temperature and environmental humidity at each detection time point with the three-dimensional model of the corresponding detection target to obtain the spatial temperature thermal distribution data and spatial humidity thermal distribution data of the three-dimensional model corresponding to the detection target, obtain the preset extreme operating temperature range and extreme operating humidity range corresponding to the sensor, match the spatial temperature thermal distribution data with the extreme operating temperature range to obtain the corresponding temperature feasible space information; similarly, obtain the corresponding humidity feasible space information according to the spatial humidity thermal distribution data and the extreme operating humidity range, overlap and match the temperature feasible space information with the humidity feasible space information to obtain the healthy operation space corresponding to the detection target, obtain the corresponding location information of each sensor, match the corresponding location information of each sensor with the healthy operation space of the detection target to obtain the corresponding temperature and humidity influence state, and aggregate the temperature and humidity influence states corresponding to each detection time point to obtain the temperature and humidity influence law corresponding to each detection cycle;

[0010] The temperature and humidity influence law is expressed as the temperature and humidity change law at each detection time point corresponding to each detection cycle, and the law of temperature and humidity influence state change caused by the change of healthy operating space caused by the temperature and humidity change law; the temperature and humidity influence laws corresponding to each detection cycle are compared, and the temperature and humidity influence law with the highest repetition rate is marked as the normal temperature and humidity influence information corresponding to the detected target.

[0011] The electromagnetic data corresponding to each sensor of the detection target is identified to obtain the electromagnetic interference intensity corresponding to each sensor, and a preset interference intensity threshold is obtained. The part of the electromagnetic interference intensity that exceeds the interference intensity threshold is recorded as an electromagnetic excess value, and the electromagnetic excess value is divided into multiple electromagnetic excess value intervals. The electromagnetic excess value intervals are sorted in order from small to large according to the electromagnetic interference intensity to obtain the electromagnetic influence interval serial number, and 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, and 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, and the electromagnetic influence value corresponding to each detection time point of each detection cycle is obtained. When the electromagnetic influence value is greater than the preset threshold, the corresponding detection time point is recorded as an electromagnetic abnormal point, and the ratio of the electromagnetic abnormal point to the total number of detection time points is calculated to obtain the electromagnetic abnormality ratio value; the normal temperature and humidity influence information and the electromagnetic abnormality ratio value corresponding to each detection cycle are combined to obtain the sensor influence state.

[0012] Step 2: Dynamic sensor deployment planning, obtain the detection requirement information of the detection target, and conduct a comprehensive analysis of the detection requirement information to obtain the detection requirement impact information, and obtain the sensor impact status corresponding to the detection target, and conduct a comprehensive analysis of the detection requirement impact information and the sensor impact status to obtain the sensor deployment optimization information.

[0013] As a further improvement of the present invention, a comprehensive analysis is performed on the detection demand information, and the specific analysis steps are as follows:

[0014] By identifying the detection requirement information, the key component positions, obstacle distribution and accuracy requirement data of the detection target are obtained; the obstacle position and three-dimensional size of each obstacle are obtained according to the obstacle distribution, and the pre-set overlapping influence range is obtained. The obstacle position corresponding to each obstacle is overlapped and matched with the overlapping influence range corresponding to the key component positions of the detection target. When the obstacle position of the obstacle is within the overlapping influence range, the corresponding obstacle is marked as an affecting obstacle, the number of affecting obstacles is counted and recorded as the number of affecting obstacles, and the affected space volume corresponding to the overlapping influence range is obtained according to the overlapping influence range. The obstacle volume ratio is calculated by ratioing the number of affecting obstacles to the affected space volume.

[0015] The data demand frequency and error data are obtained according to the accuracy requirement data; the number of times the data collected by each sensor is used per unit time is obtained, and the data demand frequency is calculated according to the number of uses and the unit time, and the data demand frequency is divided into multiple demand frequency intervals. Each demand frequency interval corresponds to a frequency influence coefficient, and the data demand frequency corresponding to each sensor is matched with each demand frequency interval to obtain the corresponding frequency influence coefficient. The data demand frequency of each sensor is multiplied by the corresponding frequency influence coefficient to obtain the demand frequency influence value.

[0016] Obtain the error data of each sensor and the preset error upper limit value, compare the error data corresponding to each sensor with the corresponding error upper limit value, and when the error data exceeds the error upper limit value, calculate the difference between the error data and the error upper limit value to obtain the error abnormality value.

[0017] The obstacle volume ratio, demand frequency influence value and error influence value are normalized and their values ​​are taken. The formula is used to The demand state value CX is calculated; among them, zat, xpz, wxz represent the obstacle volume ratio, demand frequency influence value and error influence value respectively, and Δwxz represents the allowable value of error influence; L1, L2, L3 are all preset weight factors, and the values ​​are 2.042, 1.265 and 2.284 respectively; when the demand state value is greater than the preset threshold, the corresponding detection demand influence information is generated as the demand state abnormality.

[0018] As a further improvement of the present invention, the detection demand impact information and the sensor impact state are comprehensively analyzed, and the specific analysis steps are as follows:

[0019] According to the sensor influence state, the normal temperature and humidity influence information and the electromagnetic anomaly proportion value corresponding to each detection cycle are obtained; according to the normal temperature and humidity influence information, the temperature and humidity influence law of each sensor corresponding to each detection time point is obtained; and according to the temperature and humidity influence law, the optimal environment duration of the optimal operating environment corresponding to each sensor is obtained; the preset electromagnetic anomaly threshold corresponding to the electromagnetic anomaly proportion value is obtained; when the electromagnetic anomaly proportion value is greater than the electromagnetic anomaly threshold, the corresponding sensor is marked as an electromagnetic anomaly sensor, and the position information corresponding to the electromagnetic anomaly sensor is marked as an abnormal installation position; conversely, when the electromagnetic anomaly proportion value is less than the electromagnetic anomaly threshold, the electromagnetic anomaly duration information corresponding to the electromagnetic anomaly proportion value is obtained, the optimal environment duration is overlapped and compared with the electromagnetic anomaly duration information, the part of the optimal environment duration corresponding to the electromagnetic anomaly duration information that overlaps with the electromagnetic anomaly duration information is marked as a removal duration, the optimal environment duration is subtracted from the corresponding removal duration, and the remaining part of the duration corresponding to the optimal environment duration is recorded as the optimal operating duration; when the optimal operating duration corresponding to the sensor is less than the preset threshold, the corresponding sensor layout optimization information is generated.

[0020] Step 3: Dynamically collect frequency settings, obtain the sensor operation data corresponding to each sensor and analyze the sensor operation data to obtain the sensor operation status value, and obtain the sensor impact status of the corresponding detection target, and comprehensively analyze the sensor operation status value and the sensor impact status to obtain the collection frequency setting information.

[0021] As a further improvement of the present invention, the sensor operation data is analyzed, and the specific analysis steps are as follows:

[0022] The sensor operation data includes acquisition frequency and power consumption data. The acquisition frequency corresponding to each acquisition time point in the detection cycle of each sensor is obtained according to the acquisition frequency, and the pre-set acquisition reference frequency is obtained. When the acquisition frequency is greater than the acquisition reference frequency, the acquisition frequency and the acquisition reference frequency are calculated to obtain the frequency impact value; the acquisition frequency of each sensor is obtained, and the acquisition frequency is the number of acquisition time points in the detection cycle of each sensor. The acquisition frequency is divided into multiple acquisition frequency intervals, each acquisition frequency interval corresponds to a frequency impact value, and the acquisition frequency corresponding to the sensor is matched with each acquisition frequency interval to obtain the corresponding frequency impact value.

[0023] The power consumption value of each sensor is obtained according to the power consumption data, and a 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 value of each sensor is calculated by difference with the power consumption reference value to obtain the power consumption impact value.

[0024] The frequency impact value, frequency impact value and power consumption impact value are normalized and their values ​​are taken. The operating status value yxt is calculated; wherein PC, PL and ghz represent the frequency impact value, frequency impact value and power consumption impact value respectively; k1, k2 and k3 are all preset weight factors, and their values ​​are 2.254, 2.587 and 2.014 respectively.

[0025] As a further improvement of the present invention, a comprehensive analysis is performed on the operating state value of the sensor and the sensor impact state, and the specific analysis steps are as follows:

[0026] According to the sensor influence status, the normal temperature and humidity influence information and the electromagnetic anomaly ratio are obtained; according to the normal temperature and humidity influence information, the operating temperature and humidity of the operating environment corresponding to each sensor are obtained, and a right triangle is constructed with the values ​​of the operating temperature and humidity and the electromagnetic anomaly ratio as the two right-angled sides of the right triangle, and then the right-angled vertex of the right triangle is used as the starting point to draw a straight line perpendicular to the right triangle, and the length of the straight line is equal to the operating status value, and then a triangular pyramid is constructed with the right triangle and the straight line, and the volume of the triangular pyramid is calculated and the value of the volume is marked as the acquisition total shadow value; a pre-set acquisition total shadow value interval is obtained, and when the acquisition total shadow value is greater than the pre-acquisition total shadow value interval, the corresponding acquisition frequency setting information is generated to reduce the acquisition frequency.

[0027] Step 4: Obtain sensor layout optimization information and acquisition frequency setting information, and perform sensor layout optimization according to the sensor layout optimization information, and perform corresponding acquisition frequency adjustment according to the acquisition frequency table setting information.

[0028] Step 5: Process and analyze the data collected by the sensor, which is specifically: clean and transform the data collected by the sensor to obtain collected data of uniform specifications, and group and package the collected data according to the time information of the collected data to obtain the collected data packets corresponding to each time point.

[0029] Step 6: Perform intelligent diagnosis on the collected data packets obtained after processing and analysis to obtain the status information of the detection target, and generate corresponding early warning display instructions based on the status information, then perform early warning display according to the early warning display instructions, and store the collected data packets.

[0030] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0031] 1. The present invention obtains detection demand impact information by comprehensively analyzing the detection demand information, and obtains the sensor impact status corresponding to the detection target, and comprehensively analyzes the detection demand impact information and the sensor impact status to obtain sensor layout optimization information. According to the optimization of sensor layout, targeted layout optimization can be performed for each detection target to ensure the normal operation of the sensor.

[0032] 2. The present invention obtains the operating status value of the sensor by analyzing the sensor operating data, and obtains the sensor influence status of the corresponding detection target, and comprehensively analyzes the sensor operating status value and the sensor influence status to obtain the acquisition frequency setting information, and increases the adaptability to different detection environments by adaptively adjusting the sensor acquisition frequency. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. The following drawings are not intentionally scaled to the actual size, and the focus is on illustrating the main purpose of the present application.

[0034] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 In an embodiment of the present invention, an embodiment of a sensor data collection and processing method based on big data includes the following steps:

[0037] Step 1: Environmental adaptability analysis: Use big data analysis technology to obtain historical environmental feature data of the detection target, and perform environmental adaptability analysis based on the historical environmental feature data to obtain the sensor impact status.

[0038] The environmental usability analysis is conducted based on historical environmental characteristic data. The specific analysis method is as follows:

[0039] The historical environmental characteristic data includes environmental parameter data corresponding to multiple detection cycles of the detection target and the 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;

[0040] Obtain the detection time point corresponding to each detection cycle, and obtain the environmental temperature and humidity data corresponding to each detection time point, obtain the environmental temperature and environmental humidity according to the environmental temperature and humidity data, match the environmental temperature and environmental humidity at each detection time point with the three-dimensional model of the corresponding detection target to obtain the spatial temperature thermal distribution data and spatial humidity thermal distribution data of the three-dimensional model corresponding to the detection target, obtain the preset extreme operating temperature range and extreme operating humidity range corresponding to the sensor, match the spatial temperature thermal distribution data with the extreme operating temperature range to obtain the corresponding temperature feasible space information; similarly, obtain the corresponding humidity feasible space information according to the spatial humidity thermal distribution data and the extreme operating humidity range, overlap and match the temperature feasible space information with the humidity feasible space information to obtain the healthy operation space corresponding to the detection target, obtain the corresponding location information of each sensor, match the corresponding location information of each sensor with the healthy operation space of the detection target to obtain the corresponding temperature and humidity influence state, and aggregate the temperature and humidity influence states corresponding to each detection time point to obtain the temperature and humidity influence law corresponding to each detection cycle;

[0041] The temperature and humidity influence law is expressed as the temperature and humidity change law at each detection time point corresponding to each detection cycle, and the law of temperature and humidity influence state change caused by the change of healthy operating space caused by the temperature and humidity change law; the temperature and humidity influence laws corresponding to each detection cycle are compared, and the temperature and humidity influence law with the highest repetition rate is marked as the normal temperature and humidity influence information corresponding to the detected target.

[0042] The electromagnetic data corresponding to each sensor of the detection target is identified to obtain the electromagnetic interference intensity corresponding to each sensor, and a preset interference intensity threshold is obtained. The part of the electromagnetic interference intensity that exceeds the interference intensity threshold is recorded as an electromagnetic excess value, and the electromagnetic excess value is divided into multiple electromagnetic excess value intervals. The electromagnetic excess value intervals are sorted in order from small to large according to the electromagnetic interference intensity to obtain the electromagnetic influence interval serial number, and 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, and 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, and the electromagnetic influence value corresponding to each detection time point of each detection cycle is obtained. When the electromagnetic influence value is greater than the preset threshold, the corresponding detection time point is recorded as an electromagnetic abnormal point, and the ratio of the electromagnetic abnormal point to the total number of detection time points is calculated to obtain the electromagnetic abnormality ratio value; the normal temperature and humidity influence information and the electromagnetic abnormality ratio value corresponding to each detection cycle are combined to obtain the sensor influence state.

[0043] Step 2: Dynamic sensor deployment planning, obtain the detection requirement information of the detection target, and conduct a comprehensive analysis of the detection requirement information to obtain the detection requirement impact information, and obtain the sensor impact status corresponding to the detection target, and conduct a comprehensive analysis of the detection requirement impact information and the sensor impact status to obtain the sensor deployment optimization information.

[0044] Comprehensively analyze the testing demand information. The specific analysis steps are as follows:

[0045] By identifying the detection requirement information, the key component positions, obstacle distribution and accuracy requirement data of the detection target are obtained; the obstacle position and three-dimensional size of each obstacle are obtained according to the obstacle distribution, and the pre-set overlapping influence range is obtained. The obstacle position corresponding to each obstacle is overlapped and matched with the overlapping influence range corresponding to the key component positions of the detection target. When the obstacle position of the obstacle is within the overlapping influence range, the corresponding obstacle is marked as an affecting obstacle, the number of affecting obstacles is counted and recorded as the number of affecting obstacles, and the affected space volume corresponding to the overlapping influence range is obtained according to the overlapping influence range. The obstacle volume ratio is calculated by ratioing the number of affecting obstacles to the affected space volume.

[0046] The data demand frequency and error data are obtained according to the accuracy requirement data; the number of times the data collected by each sensor is used per unit time is obtained, and the data demand frequency is calculated according to the number of uses and the unit time, and the data demand frequency is divided into multiple demand frequency intervals. Each demand frequency interval corresponds to a frequency influence coefficient, and the data demand frequency corresponding to each sensor is matched with each demand frequency interval to obtain the corresponding frequency influence coefficient. The data demand frequency of each sensor is multiplied by the corresponding frequency influence coefficient to obtain the demand frequency influence value.

[0047] Obtain the error data of each sensor and the preset error upper limit value, compare the error data corresponding to each sensor with the corresponding error upper limit value, and when the error data exceeds the error upper limit value, calculate the difference between the error data and the error upper limit value to obtain the error abnormality value.

[0048] The obstacle volume ratio, demand frequency influence value and error influence value are normalized and their values ​​are taken. The formula is used to The demand state value CX is calculated; among them, zat, xpz, wxz represent the obstacle volume ratio, demand frequency influence value and error influence value respectively, and Δwxz represents the allowable value of error influence; L1, L2, L3 are all preset weight factors, and the values ​​are 2.042, 1.265 and 2.284 respectively; when the demand state value is greater than the preset threshold, the corresponding detection demand influence information is generated as the demand state abnormality.

[0049] Comprehensively analyze the impact information of detection requirements and sensor impact status. The specific analysis steps are as follows:

[0050] According to the sensor influence state, the normal temperature and humidity influence information and the electromagnetic anomaly proportion value corresponding to each detection cycle are obtained; according to the normal temperature and humidity influence information, the temperature and humidity influence law of each sensor corresponding to each detection time point is obtained; and according to the temperature and humidity influence law, the optimal environment duration of the optimal operating environment corresponding to each sensor is obtained; the preset electromagnetic anomaly threshold corresponding to the electromagnetic anomaly proportion value is obtained; when the electromagnetic anomaly proportion value is greater than the electromagnetic anomaly threshold, the corresponding sensor is marked as an electromagnetic anomaly sensor, and the position information corresponding to the electromagnetic anomaly sensor is marked as an abnormal installation position; conversely, when the electromagnetic anomaly proportion value is less than the electromagnetic anomaly threshold, the electromagnetic anomaly duration information corresponding to the electromagnetic anomaly proportion value is obtained, the optimal environment duration is overlapped and compared with the electromagnetic anomaly duration information, the part of the optimal environment duration corresponding to the electromagnetic anomaly duration information that overlaps with the electromagnetic anomaly duration information is marked as a removal duration, the optimal environment duration is subtracted from the corresponding removal duration, and the remaining part of the duration corresponding to the optimal environment duration is recorded as the optimal operating duration; when the optimal operating duration corresponding to the sensor is less than the preset threshold, the corresponding sensor layout optimization information is generated.

[0051] Step 3: Dynamically collect frequency settings, obtain the sensor operation data corresponding to each sensor and analyze the sensor operation data to obtain the sensor operation status value, and obtain the sensor impact status of the corresponding detection target, and comprehensively analyze the sensor operation status value and the sensor impact status to obtain the collection frequency setting information.

[0052] The sensor operation data is analyzed, and the specific analysis steps are as follows:

[0053] The sensor operation data includes acquisition frequency and power consumption data. The acquisition frequency corresponding to each acquisition time point in the detection cycle of each sensor is obtained according to the acquisition frequency, and the pre-set acquisition reference frequency is obtained. When the acquisition frequency is greater than the acquisition reference frequency, the acquisition frequency and the acquisition reference frequency are calculated to obtain the frequency impact value; the acquisition frequency of each sensor is obtained, and the acquisition frequency is the number of acquisition time points in the detection cycle of each sensor. The acquisition frequency is divided into multiple acquisition frequency intervals, each acquisition frequency interval corresponds to a frequency impact value, and the acquisition frequency corresponding to the sensor is matched with each acquisition frequency interval to obtain the corresponding frequency impact value.

[0054] The power consumption value of each sensor is obtained according to the power consumption data, and a 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 value of each sensor is calculated by difference with the power consumption reference value to obtain the power consumption impact value.

[0055] The frequency impact value, frequency impact value and power consumption impact value are normalized and their values ​​are taken. The operating status value yxt is calculated; wherein PC, PL and ghz represent the frequency impact value, frequency impact value and power consumption impact value respectively; k1, k2 and k3 are all preset weight factors, and their values ​​are 2.254, 2.587 and 2.014 respectively.

[0056] Comprehensively analyze the operating status value of the sensor and the sensor impact status. The specific analysis steps are as follows:

[0057] According to the sensor influence status, the normal temperature and humidity influence information and the electromagnetic anomaly ratio are obtained; according to the normal temperature and humidity influence information, the operating temperature and humidity of the operating environment corresponding to each sensor are obtained, and a right triangle is constructed with the values ​​of the operating temperature and humidity and the electromagnetic anomaly ratio as the two right-angled sides of the right triangle, and then the right-angled vertex of the right triangle is used as the starting point to draw a straight line perpendicular to the right triangle, and the length of the straight line is equal to the operating status value, and then a triangular pyramid is constructed with the right triangle and the straight line, and the volume of the triangular pyramid is calculated and the value of the volume is marked as the acquisition total shadow value; a pre-set acquisition total shadow value interval is obtained, and when the acquisition total shadow value is greater than the pre-acquisition total shadow value interval, the corresponding acquisition frequency setting information is generated to reduce the acquisition frequency.

[0058] Step 4: Analyze the sensor control information, obtain the sensor layout optimization information and the acquisition frequency setting information, optimize the sensor layout according to the sensor layout optimization information, and adjust the corresponding acquisition frequency according to the acquisition frequency table setting information.

[0059] Step 5: Data processing and analysis, processing and analyzing the data collected by the sensor, specifically: cleaning and converting the data collected by the sensor to obtain collected data of uniform specifications, and grouping and packaging the collected data according to the time information of the collected data to obtain the collected data packets corresponding to each time point.

[0060] Step 6: Intelligent diagnosis analysis and storage. Perform intelligent diagnosis on the collected data packets obtained after processing and analysis to obtain the status information of the detection target, and generate corresponding early warning display instructions based on the status information. Then, perform early warning display according to the early warning display instructions and store the collected data packets.

[0061] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A sensor data collection and processing method based on big data, characterized in that: The steps include: Step 1: Obtain historical environmental characteristic data of the detection target through big data analysis technology, and perform environmental adaptability analysis based on the historical environmental characteristic data to obtain the sensor impact status; Step 2: Acquire the detection requirement information of the detection target, and perform a comprehensive analysis on the detection requirement information to obtain the detection requirement impact information, and obtain the sensor impact status corresponding to the detection target, and perform a comprehensive analysis on the detection requirement impact information and the sensor impact status to obtain the sensor deployment optimization information; Step 3: Obtain sensor operation data corresponding to each sensor and analyze the sensor operation data to obtain the sensor operation state value, obtain the sensor influence state corresponding to the detection target, and comprehensively analyze the sensor operation state value and the sensor influence state to obtain the acquisition frequency setting information; Step 4: Sensor control information execution analysis; Step 5: Process and analyze the data collected by the sensor; Step 6: Intelligent diagnosis analysis and storage.

2. The sensor data collection and processing method based on big data according to claim 1 is characterized in that: The environmental usability analysis is performed based on historical environmental characteristic data, and the specific analysis method is as follows: The historical environmental characteristic data includes environmental parameter data corresponding to multiple detection cycles of the detection target and the 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; Obtain the detection time point corresponding to each detection cycle, and obtain the environmental temperature and humidity data corresponding to each detection time point, obtain the environmental temperature and environmental humidity according to the environmental temperature and humidity data, match the environmental temperature and environmental humidity at each detection time point with the three-dimensional model of the corresponding detection target to obtain the spatial temperature thermal distribution data and spatial humidity thermal distribution data of the three-dimensional model corresponding to the detection target, obtain the preset extreme operating temperature range and extreme operating humidity range corresponding to the sensor, match the spatial temperature thermal distribution data with the extreme operating temperature range to obtain the corresponding temperature feasible space information; similarly, obtain the corresponding humidity feasible space information according to the spatial humidity thermal distribution data and the extreme operating humidity range, overlap and match the temperature feasible space information with the humidity feasible space information to obtain the healthy operation space corresponding to the detection target, obtain the corresponding location information of each sensor, match the corresponding location information of each sensor with the healthy operation space of the detection target to obtain the corresponding temperature and humidity influence state, and aggregate the temperature and humidity influence states corresponding to each detection time point to obtain the temperature and humidity influence law corresponding to each detection cycle; Identify the electromagnetic data corresponding to each sensor of the detection target, obtain the electromagnetic interference intensity corresponding to each sensor, obtain a preset interference intensity threshold, record the part of the electromagnetic interference intensity that exceeds the interference intensity threshold as an electromagnetic excess value, divide the electromagnetic excess value into multiple electromagnetic excess value intervals, sort the electromagnetic excess value intervals in order from small to large according to the electromagnetic interference intensity to obtain an electromagnetic influence interval serial number, set an electromagnetic influence coefficient for each electromagnetic influence interval serial number, and the electromagnetic influence coefficient increases with the increase of the electromagnetic influence interval serial number, match the electromagnetic excess value corresponding to each sensor with each electromagnetic excess value interval to obtain the corresponding electromagnetic influence coefficient, add the electromagnetic excess value of each sensor and the corresponding electromagnetic influence coefficient to obtain the electromagnetic influence value, obtain the electromagnetic influence value of each detection time point corresponding to each detection cycle, when the electromagnetic influence value is greater than the preset threshold, record the corresponding detection time point as an electromagnetic abnormal point, and calculate the ratio of the electromagnetic abnormal point to the total number of detection time points to obtain the electromagnetic abnormality ratio value; The normal temperature and humidity impact information and electromagnetic anomaly ratio corresponding to each detection period are combined to obtain the sensor impact status.

3. The sensor data collection and processing method based on big data according to claim 1 is characterized in that: The comprehensive analysis of the detection demand information is performed, and the specific analysis steps are as follows: By identifying the detection requirement information, the key component positions, obstacle distribution and accuracy requirement data of the detection target are obtained; the obstacle position and three-dimensional size of each obstacle are obtained according to the obstacle distribution, and the preset overlap influence range is obtained. The obstacle position corresponding to each obstacle is overlapped and matched with the overlap influence range corresponding to each key component position of the detection target. When the obstacle position of the obstacle is within the overlap influence range, the corresponding obstacle is marked as an impact obstacle, the number of impact obstacles is counted and recorded as the number of impact obstacles, and the impact space volume corresponding to the overlap influence range is obtained according to the overlap influence range. The obstacle volume ratio is calculated by ratioing the number of impact obstacles to the impact space volume; Obtain data requirement frequency and error data according to accuracy requirement data; Obtain the number of times the data collected by each sensor is used per unit time, calculate the data demand frequency according to the number of times used and the unit time, divide the data demand frequency into multiple demand frequency intervals, each demand frequency interval corresponds to a frequency influence coefficient, match the data demand frequency corresponding to each sensor with each demand frequency interval to obtain the corresponding frequency influence coefficient, and multiply the data demand frequency of each sensor by the corresponding frequency influence coefficient to obtain the demand frequency influence value; Obtain the error data of each sensor and obtain a preset error upper limit value, compare the error data corresponding to each sensor with the corresponding error upper limit value, and when the error data exceeds the error upper limit value, calculate the difference between the error data and the error upper limit value to obtain an error abnormality value; The obstacle volume ratio, demand frequency influence value and error influence value are comprehensively calculated to obtain the demand state value; when the demand state value is greater than the preset threshold, the corresponding detection demand influence information is generated as the demand state abnormality.

4. The sensor data collection and processing method based on big data according to claim 3 is characterized in that: The obstacle volume ratio, demand frequency impact value and error impact value are comprehensively calculated. The specific calculation method is: the obstacle volume ratio, demand frequency impact value and error impact value are normalized and their values ​​are taken, and the formula is used The demand state value CX is calculated; among them, zat, xpz, wxz represent the obstacle volume ratio, demand frequency influence value and error influence value respectively, Δwxz represents the allowable value of error influence; L1, L2, L3 are all preset weight factors.

5. A sensor data collection and processing method based on big data according to claim 2 or 3, characterized in that: The detection demand impact information and sensor impact status 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 corresponding to each detection cycle are obtained; according to the normal temperature and humidity influence information, the temperature and humidity influence law of each sensor corresponding to each detection time point is obtained; and according to the temperature and humidity influence law, the optimal environment duration of the optimal operating environment corresponding to each sensor is obtained; the preset electromagnetic anomaly threshold corresponding to the electromagnetic anomaly proportion value is obtained; when the electromagnetic anomaly proportion value is greater than the electromagnetic anomaly threshold, the corresponding sensor is marked as an electromagnetic anomaly sensor, and the position information corresponding to the electromagnetic anomaly sensor is marked as an abnormal installation position; conversely, when the electromagnetic anomaly proportion value is less than the electromagnetic anomaly threshold, the electromagnetic anomaly duration information corresponding to the electromagnetic anomaly proportion value is obtained, the optimal environment duration is overlapped and compared with the electromagnetic anomaly duration information, the part of the optimal environment duration corresponding to the electromagnetic anomaly duration information that overlaps with the electromagnetic anomaly duration information is marked as a removal duration, the optimal environment duration is subtracted from the corresponding removal duration, and the remaining part of the duration corresponding to the optimal environment duration is recorded as the optimal operating duration; when the optimal operating duration corresponding to the sensor is less than the preset threshold, the corresponding sensor layout optimization information is generated.

6. The sensor data collection and processing method based on big data according to claim 1 is characterized in that: The sensor operation data is analyzed, and the specific analysis steps are as follows: The sensor operation data includes the acquisition frequency and power consumption data. The acquisition frequency corresponding to each acquisition time point in the detection cycle of each sensor is obtained according to the acquisition frequency, and the preset acquisition reference frequency is obtained. When the acquisition frequency is greater than the acquisition reference frequency, the acquisition frequency and the acquisition reference frequency are calculated to obtain the frequency impact value; the acquisition frequency of each sensor is obtained, and the acquisition frequency is divided into multiple acquisition frequency intervals. Each acquisition frequency interval corresponds to a frequency impact value. The acquisition frequency corresponding to the sensor is matched with each acquisition frequency interval to obtain the corresponding frequency impact value; The power consumption value of each sensor is obtained according to the power consumption data, and a 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 differentially with the power consumption reference value to obtain the power consumption impact value; the frequency impact value, frequency impact value and power consumption impact value are comprehensively calculated to obtain the operating status value.

7. A sensor data collection and processing method based on big data according to claim 2 or 6, characterized in that: A comprehensive analysis is performed on the operating status value of the sensor and the sensor impact status, and the specific analysis steps are as follows: According to the sensor influence status, the normal temperature and humidity influence information and the electromagnetic anomaly ratio are obtained; according to the normal temperature and humidity influence information, the operating temperature and humidity of the operating environment corresponding to each sensor are obtained, and a right triangle is constructed with the values ​​of the operating temperature and humidity and the electromagnetic anomaly ratio as the two right-angled sides of the right triangle, and then the right-angled vertex of the right triangle is used as the starting point to draw a straight line perpendicular to the right triangle, and the length of the straight line is equal to the operating status value, and then a triangular pyramid is constructed with the right triangle and the straight line, and the volume of the triangular pyramid is calculated and the value of the volume is marked as the acquisition total shadow value; a pre-set acquisition total shadow value interval is obtained, and when the acquisition total shadow value is greater than the pre-acquisition total shadow value interval, the corresponding acquisition frequency setting information is generated to reduce the acquisition frequency.

8. The sensor data collection and processing method based on big data according to claim 1 is characterized in that: The sensor control information execution analysis is specifically: obtaining sensor layout optimization information and acquisition frequency setting information, and performing sensor layout optimization according to the sensor layout optimization information, and performing corresponding acquisition frequency adjustment according to the acquisition frequency table setting information.

9. The sensor data collection and processing method based on big data according to claim 1 is characterized in that: The processing and analysis of the data collected by the sensor is specifically as follows: the data collected by the sensor is cleaned and converted to obtain collected data of uniform specifications, and the collected data is aggregated and packaged according to the time information of the collected data to obtain collected data packets corresponding to each time point.

10. The sensor data collection and processing method based on big data according to claim 1 is characterized in that: The intelligent diagnosis analysis and storage specifically includes: performing intelligent diagnosis on the collected data packets obtained after processing and analysis to obtain status information of the detection target, and generating corresponding early warning display instructions based on the status information, and then performing early warning display according to the early warning display instructions, and storing the collected data packets.

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