CN116894166A - Soil environment parameter information monitoring system based on intelligent sensing network - Google Patents
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Abstract
The application discloses a soil environment parameter information monitoring system based on an intelligent sensing network, which belongs to the technical field of soil monitoring, and is characterized in that soil monitoring data are collected by using a sensor, a data quality index and a wireless transmission quality index are combined, a perception advantage measurement coefficient is generated by an intelligent model and is used for judging data reliability, and a reliable monitoring effective signal is generated when the coefficient is lower than a state analysis threshold value; generating a monitoring adjustment signal between the first state analysis threshold and the second state analysis threshold, wherein the monitoring adjustment signal indicates that the monitoring adjustment signal can be used but has errors; and when the state analysis threshold value II is exceeded, a monitoring invalid signal is generated, one monitoring point is selected from a plurality of monitoring points to serve as a reference point when the signal is definitely invalid, and the soil similarity is calculated to determine whether the sensor is divided into the same area, so that the sensor is ensured to have consistent configuration in the similar soil area, the data consistency, the management efficiency and the system stability are improved, the configuration errors and the fault risks are reduced, and the resources are saved.
Description
Technical Field
The application relates to the technical field of soil monitoring, in particular to a soil environment parameter information monitoring system based on an intelligent sensing network.
Background
Soil is a fundamental medium for crop growth, and modern agriculture is very focused on monitoring the soil environment. The method is characterized in that technologies such as soil sensors, geographic information systems and the like are widely adopted, and aims to optimize resource management, improve yield and quality and reduce environmental impact. Soil environment monitoring has a central role in the fields of scientific research, crop improvement, agricultural management, fertilizer use, soil remediation and the like, and is helpful for promoting the development of innovation and sustainable agricultural practice. This emphasis on soil environment reflects the firm promise of modern agriculture for resource sustainability and food safety.
In some soil research institutions, sensors are typically placed in the soil equidistantly, in a matrix fashion, with each sensor monitoring the soil in its own jurisdiction. And then, summarizing the data to the background through the intelligent sensing network for analysis. Based on the data monitored by the sensor, the environmental condition of the soil is known, and then the soil condition is classified. The monitoring modes of the sensor are adjusted according to different grades so as to adjust the monitoring data quantity in a targeted mode, thereby ensuring the monitoring effect and reducing the resource consumption.
However, the existing adjusting mode is used for adjusting the sensors independently, so that a background control center needs to process a plurality of pieces of data simultaneously, the data processing load is increased, the system performance is easily reduced, even the system is down, and the continuity and the reliability of soil environment monitoring are affected. Secondly, each sensor needs to be configured with an adjusting parameter independently, so that the risk of parameter configuration errors is increased, potential faults in the operation of the sensor are increased, time and maintenance resources are increased, the risk of data processing errors is also increased, the monitoring result is inaccurate, the long-term evaluation of the soil environment is influenced, and finally, more calculation and storage resources are needed, and the cost of system maintenance and operation is increased.
In order to solve the above problems, a technical solution is now provided.
Disclosure of Invention
In order to overcome the defects in the prior art, the embodiment of the application provides that a sensor is used for collecting soil monitoring data, and a perceived dominance measurement coefficient is generated through an intelligent model by combining a data quality index and a wireless transmission quality index and is used for judging the reliability of the data, and a reliable monitoring effective signal is generated when the coefficient is lower than a threshold value; between the first and second thresholds, generating a monitor adjustment signal indicating that the signal is usable but has an error; and when the signal is definitely invalid without monitoring, one monitoring point is selected as a reference point, and the soil similarity is calculated to determine whether the sensor is divided into the same area, so that the sensor is ensured to have consistent configuration in the similar soil area, the data consistency, the management efficiency and the system stability are improved, the configuration errors and the fault risks are reduced, and the resources are saved, so that the problems in the background technology are solved.
In order to achieve the above purpose, the present application provides the following technical solutions: the system comprises a data acquisition unit, a state analysis unit and a comprehensive analysis unit;
the data acquisition unit is used for collecting measuring and calculating information and communication information of the sensor and sending the measuring and calculating information and the communication information to the state analysis unit;
the state analysis unit comprehensively analyzes the measuring and calculating information and the communication information to obtain a perception advantage measurement coefficient, analyzes the perception advantage measurement coefficient, generates a monitoring effective signal, a monitoring adjustment signal and a monitoring ineffective signal, and sends the monitoring effective signal, the monitoring adjustment signal and the monitoring ineffective signal to the data collection unit;
the comprehensive analysis unit collects soil parameters monitored by each sensor aiming at generating monitoring effective signals and monitoring adjustment signals, performs summarization analysis on the soil parameters, generates soil similarity, analyzes the soil similarity, and divides the corresponding sensors into a unified scheduling set.
In a preferred embodiment, the measurement information includes a data quality index and the communication information includes a wireless transmission quality index.
In a preferred embodiment, the data quality index acquisition logic is:
step a11, acquiring a plurality of monitoring data acquired by a sensor in t1 time, and counting each monitoring data to construct a data set;
step a12, calculating the mean value and standard deviation of the data set, wherein the mean value represents the central position of the data set, and the standard deviation measures the discrete degree of the data point relative to the mean value; calculating a mean value:wherein->Mean value->Representing the sum of all data points +.>Representing the number of data points; calculating standard deviation: />Wherein->The standard deviation is indicated as such,representing the sum of squares of the differences between each data point and the mean;
step a13, calculating a standard score of each data point, wherein the standard score is used for determining whether the data point is an outlier, and calculating the standard score:wherein->Representing a standard score.
Step a14, comparing the standard score of each data point with a standard threshold, if the standard score is greater than the standard threshold, identifying the corresponding data point as an abnormal value, counting the occurrence times of the abnormal value, and calculating the interference degree of the abnormal value, namely the data quality index, wherein the calculation formula is as follows:wherein->Which represents the quality index of the data,the number of times of abnormal value occurrence, the total number of data, the average standard fraction and the maximum standard fraction are respectively.
In a preferred embodiment, the wireless transmission quality index acquisition logic is configured to:
step a21 of establishing a communication link between the sensors and selecting a known, encoded binary data sequence as the transmission data;
step a22, transmitting data by using wireless communication transmission, wherein the data is transmitted through a channel and reaches a receiving end;
step a23, the receiving end receives the transmitted data and decodes the data to restore the binary data sequence;
step a24 of comparing the received binary data sequence with the known original data sequence, checking for the presence of errors for each bit position;
step a25, calculating a correct rate, wherein the correct rate refers to the ratio of the number of bits in which correct data occurs to the total number of bits;
step a26, collecting a plurality of correct rates in t2 time, calculating standard deviation and average value of the correct rates, and calculating the ratio of the standard deviation to the average value, namely the wireless transmission quality index.
In a preferred embodiment, the data quality index and the wireless transmission quality index are comprehensively analyzed and calculated to obtain a perception advantage metric coefficient, and the calculation formula is as follows:wherein->For perceiving the quality metric, +.>Data quality index, wireless transmission quality index, < >>Preset proportionality coefficients of data quality index and radio transmission quality index respectively, and +.>Are all greater than 0.
In a preferred embodiment, the perceptual dominance metric coefficient is compared with a first state classification threshold and a second state classification threshold, respectively, wherein the first state classification threshold is smaller than the second state classification threshold, and if the perceptual dominance metric coefficient is greater than or equal to the second state classification threshold, a monitoring invalidation signal is generated; if the sensing dominance measurement coefficient is larger than or equal to the state classification threshold I and smaller than the state analysis threshold II, generating a monitoring adjustment signal; and if the perception advantage metric coefficient is smaller than the state classification threshold value I, generating a monitoring effective signal.
In a preferred embodiment, the operation of the integrated analysis unit comprises the following:
step b11, installing a sensor at each monitoring point, and representing the soil data of each monitoring point as a feature vector, wherein each dimension represents a soil parameter or feature, for example, taking parameters such as pH value, humidity, temperature and the like of the soil as different dimensions;
step b12, carrying out standard normalization processing on the soil data of each dimension;
step b13, selecting a monitoring point for generating a monitoring effective signal as a reference point, and comparing other monitoring points with the reference point;
step b14, for each monitoring point to be compared;
if the to-be-monitored site generates a monitoring effective signal, the calculation formula of the soil similarity is as follows:
；
if the point to be monitored generates the monitoring adjustment signal, the calculation formula of the soil similarity is as follows
Wherein->Is a monitoring point->And->Similarity of soil between->And->Two monitoring points are respectively at the +.>Values in the individual feature dimensions +.>Is the total number of feature dimensions; />Is a regulatory factor, and->Greater than 0->Is a perceptual dominance metric coefficient;
and b15, comparing the soil similarity with a soil similarity threshold, if the soil similarity is greater than or equal to the soil similarity threshold, dividing the monitoring point and the reference point into the same soil area, otherwise, dividing the monitoring point and the reference point into different land areas.
And b16, counting the sensors which are in the same soil area and generate signals which are non-monitoring invalid signals, and dividing the sensors into a unified scheduling set.
The soil environment parameter information monitoring system based on the intelligent sensing network has the technical effects and advantages that:
1. establishing an analysis model by utilizing a data quality index and a wireless transmission quality index when the sensor collects soil monitoring data, generating a perception advantage measurement coefficient through the model, comparing the perception advantage measurement coefficient with a state classification threshold value to intelligently distinguish the reliability of the soil data monitored by the sensor, and generating a reliable monitoring effective signal by a system when the perception advantage measurement coefficient is lower than the state classification threshold value, so as to ensure that scientific researchers obtain accurate and reliable data; when the perception dominance measurement coefficient is larger than or equal to the state classification threshold value I and smaller than the state analysis threshold value II, the system generates a monitoring adjustment signal which indicates that the system can be used, but has a certain error, and specific and usage scene analysis is needed to be combined; if the coefficient exceeds the second state classification threshold, the system sends out a monitoring invalid signal to remind scientific researchers that the data is unreliable so as to prevent unreliable data from misleading research work, thereby improving the data quality and reliability of soil monitoring and providing more powerful support for scientific research work;
2. the sensor is used to monitor soil data when it is clear that the sensor generated signal is a non-monitoring null signal. First, a monitoring point generating a monitoring effective signal is selected from a plurality of monitoring points, and is used as a reference point, and then other monitoring points are compared with the reference point. For each monitoring point to be compared, if a monitoring effective signal is generated, calculating the soil similarity between the monitoring point and a reference point by using cosine similarity, if a monitoring adjustment signal is generated by the monitoring point, calculating the soil similarity by combining a perception dominance measurement coefficient, then comparing the calculated soil similarity with a preset soil similarity threshold value to determine whether to divide the two monitoring points into the same soil area or different blocks, and finally summarizing the sensors which are positioned in the same soil area and generate signals which are non-monitoring ineffective signals into a unified scheduling set; further, the sensors working in similar soil areas are ensured to have the same configuration and parameters, so that the consistency and comparability of data acquisition are ensured, and the monitoring accuracy is improved; through centralized control, the system can more efficiently manage a plurality of sensors, and only needs to send the same adjustment parameters once, so that the workload of independently configuring each sensor is reduced, and the working efficiency of the system is improved; in addition, the risk of human configuration errors is reduced, potential faults in the operation of the sensor are reduced, the stability and reliability of the system are enhanced, and meanwhile time and maintenance resources are saved.
Drawings
Fig. 1 is a schematic structural diagram of a soil environment parameter information monitoring system based on an intelligent sensing network.
Detailed Description
The following description of the embodiments of the present application 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 application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Examples
FIG. 1 shows a soil environment parameter information monitoring system based on an intelligent sensing network, which comprises a data acquisition unit, a state analysis unit and a comprehensive analysis unit;
the data acquisition unit is used for collecting measuring and calculating information and communication information of the sensor and sending the measuring and calculating information and the communication information to the state analysis unit;
the state analysis unit comprehensively analyzes the measuring and calculating information and the communication information to obtain a perception advantage measurement coefficient, analyzes the perception advantage measurement coefficient, generates a monitoring effective signal, a monitoring adjustment signal and a monitoring ineffective signal, and sends the monitoring effective signal, the monitoring adjustment signal and the monitoring ineffective signal to the data collection unit;
the comprehensive analysis unit collects soil parameters monitored by each sensor aiming at generating monitoring effective signals and monitoring adjustment signals, performs summarization analysis on the soil parameters, generates soil similarity, analyzes the soil similarity, and divides the corresponding sensors into a unified scheduling set.
The operation process of the data acquisition unit comprises the following steps:
the measurement information includes a data quality index and the communication information includes a wireless transmission quality index.
The data quality index acquisition logic is as follows:
step a11, acquiring a plurality of monitoring data acquired by a sensor in t1 time, and counting each monitoring data to construct a data set;
step a12, calculating the mean value and standard deviation of the data set, wherein the mean value represents the central position of the data set, and the standard deviation measures the discrete degree of the data point relative to the mean value; calculating a mean value:wherein->Mean value->Representing the sum of all data points +.>Representing the number of data points; calculating standard deviation: />Wherein->The standard deviation is indicated as such,representing the sum of squares of the differences between each data point and the mean;
step a13, calculating a standard score of each data point, wherein the standard score is used for determining whether the data point is an outlier, and calculating the standard score:wherein->Representing a standard score.
Step a14, comparing the standard score of each data point with a standard threshold, if the standard score is greater than the standard threshold, identifying the corresponding data point as an abnormal value, counting the occurrence times of the abnormal value, and calculating the interference degree of the abnormal value, namely the data quality index, wherein the calculation formula is as follows:wherein->Which represents the quality index of the data,the number of times of abnormal value occurrence, the total number of data, the average standard fraction and the maximum standard fraction are respectively.
The data quality index is used for evaluating the influence degree of abnormal values in the sensor data on the whole data set, the greater the data quality index is, the higher the frequency of occurrence of the abnormal values in the data set is, the larger the standard score is, the noise or abnormal condition exists in the sensor data to a certain extent, the abnormal values have obvious influence on the whole characteristics of the data, the lower the reliability degree of the sensor is, and further investigation and repair are needed; when the data quality index is smaller, the occurrence frequency of the abnormal value in the data set is lower, the standard score is smaller, the sensor data is relatively stable, the abnormal condition is smaller, the influence of the abnormal value on the overall characteristics of the data is smaller, and the reliability of the sensor is high.
The acquisition logic of the wireless transmission quality index is as follows:
step a21 of establishing a communication link between the sensors and selecting a known, encoded binary data sequence as the transmission data;
step a22, transmitting data by using wireless communication transmission, wherein the data is transmitted through a channel and reaches a receiving end;
step a23, the receiving end receives the transmitted data and decodes the data to restore the binary data sequence;
step a24 of comparing the received binary data sequence with the known original data sequence, checking for the presence of errors for each bit position;
step a25, calculating a correct rate, wherein the correct rate refers to the ratio of the number of bits in which correct data occurs to the total number of bits;
step a26, collecting a plurality of correct rates in t2 time, calculating standard deviation and average value of the correct rates, and calculating the ratio of the standard deviation to the average value, namely the wireless transmission quality index.
The wireless transmission quality index is used for evaluating the wireless communication quality of the wireless sensor, and the smaller the value of the wireless transmission quality is, the better the quality of the wireless communication is, which indicates that the accuracy has a higher level and the accuracy fluctuation is smaller, and the sensor has higher wireless communication quality; in contrast, if the value of the wireless transmission quality index is large, this indicates that the wireless communication quality is poor, indicating that the accuracy is at a low level and that the accuracy fluctuates greatly, indicating that the wireless communication quality of the transmitter is unstable.
The operation process of the state analysis unit comprises the following steps:
the data quality index and the wireless transmission quality index are comprehensively analyzed and calculated to obtain a perception advantage measurement coefficient, and the calculation formula is as follows:wherein->For perceiving the quality metric, +.>Data quality index, wireless transmission quality index, < >>Preset proportionality coefficients of data quality index and radio transmission quality index respectively, and +.>Are all greater than 0.
The sensor is used for evaluating the reliability of monitoring data of the sensor, and the smaller the sensor is, the better the sensor is in terms of data monitoring quality and data transmission, and reliable and high-quality monitoring soil data can be provided; in contrast, when the perceived dominance metric coefficient is larger, it indicates that the sensor has poor performance in terms of data monitoring quality and data transmission, which means that the transmitted data quality is lower, and stable and reliable monitoring soil data cannot be provided.
Comparing the perception advantage measurement coefficient with a first state classification threshold and a second state classification threshold respectively, wherein the first state classification threshold is smaller than the second state classification threshold, and if the perception advantage measurement coefficient is larger than or equal to the second state classification threshold, the reliability of monitoring soil quality by the sensor is low, a monitoring invalid signal is generated, and the sensor needs to be replaced so as to ensure that the monitoring soil data is accurate and reliable; if the sensing dominance measurement coefficient is larger than or equal to the state classification threshold value I and smaller than the state analysis threshold value II, the sensor monitoring data has certain reliability, but has certain error, and a monitoring adjustment signal is generated; and if the perception advantage measurement coefficient is smaller than the first state classification threshold value, the sensor is high in reliability of monitoring soil quality, and a monitoring effective signal is generated.
According to the application, the data quality index and the wireless transmission quality index are utilized when the sensor collects the soil monitoring data, an analysis model is established, a perception advantage measurement coefficient is generated through the model, the perception advantage measurement coefficient is compared with a state classification threshold value, so that the reliability of the soil data monitored by the sensor can be intelligently distinguished, and when the perception advantage measurement coefficient is lower than the state classification threshold value, a system generates a reliable monitoring effective signal, so that scientific researchers can obtain accurate and reliable data; when the perception dominance measurement coefficient is larger than or equal to the state classification threshold value I and smaller than the state analysis threshold value II, the system generates a monitoring adjustment signal which indicates that the system can be used, but has a certain error, and specific and usage scene analysis is needed to be combined; if the coefficient exceeds the second state classification threshold, the system will send out monitoring invalid signals to remind scientific researchers that the data is unreliable so as to prevent unreliable data from misleading research work, thereby improving the data quality and reliability of soil monitoring and providing more powerful support for scientific research work.
The operation process of the comprehensive analysis unit comprises the following steps:
step b11, installing a sensor at each monitoring point, and representing the soil data of each monitoring point as a feature vector, wherein each dimension represents a soil parameter or feature, for example, taking parameters such as pH value, humidity, temperature and the like of the soil as different dimensions;
step b12, carrying out standard normalization processing on the soil data of each dimension;
step b13, selecting a monitoring point for generating a monitoring effective signal as a reference point, and comparing other monitoring points with the reference point;
step b14, for each monitoring point to be compared;
if the to-be-monitored site generates a monitoring effective signal, the calculation formula of the soil similarity is as follows:
；
if the point to be monitored generates the monitoring adjustment signal, the calculation formula of the soil similarity is as follows
Wherein->Is a monitoring point->And->Similarity of soil between->And->Two monitoring points are respectively at the +.>Values in the individual feature dimensions +.>Is the total number of feature dimensions; />Is a regulatory factor, and->Greater than 0->Is a perceptual dominance metric coefficient;
for monitoring adjustment signals generated by sensors of the points to be monitored, this means that there is a certain degree of error in the measured soil data. Therefore, when calculating the soil similarity between the reference point and the point to be monitored, such errors need to be considered, which need to be reflected in the result of the similarity calculation. In particular, if the perceived dominance metric coefficient of the sensor of the point to be monitored is greater, it indicates that the monitored data of the sensor is affected by greater errors or uncertainties, and therefore the value of the soil similarity with the reference point is lower. In this case, a smaller soil similarity value indicates that there is a larger difference in soil characteristics between the data of the point to be monitored and the reference point, and the credibility and accuracy of the data need to be carefully considered. Therefore, in the analysis and application of soil data, sensor errors must be considered to ensure the reliability of the data.
And b15, comparing the soil similarity with a soil similarity threshold, if the soil similarity is greater than or equal to the soil similarity threshold, dividing the monitoring point and the reference point into the same soil area, otherwise, dividing the monitoring point and the reference point into different land areas.
And b16, counting the sensors which are in the same soil area and generate signals which are non-monitoring invalid signals, and dividing the sensors into a unified scheduling set.
In the application, when the signal generated by the clear sensor is a non-monitoring invalid signal, the sensor is used for monitoring soil data. First, a monitoring point generating a monitoring effective signal is selected from a plurality of monitoring points, and is used as a reference point, and then other monitoring points are compared with the reference point. For each monitoring point to be compared, if a monitoring effective signal is generated, calculating the soil similarity between the monitoring point and a reference point by using cosine similarity, if a monitoring adjustment signal is generated by the monitoring point, calculating the soil similarity by combining a perception dominance measurement coefficient, then comparing the calculated soil similarity with a preset soil similarity threshold value to determine whether to divide the two monitoring points into the same soil area or different blocks, and finally summarizing the sensors which are positioned in the same soil area and generate signals which are non-monitoring ineffective signals into a unified scheduling set; further, the sensors working in similar soil areas are ensured to have the same configuration and parameters, so that the consistency and comparability of data acquisition are ensured, and the monitoring accuracy is improved; through centralized control, the system can more efficiently manage a plurality of sensors, and only needs to send the same adjustment parameters once, so that the workload of independently configuring each sensor is reduced, and the working efficiency of the system is improved; in addition, the risk of human configuration errors is reduced, potential faults in the operation of the sensor are reduced, the stability and reliability of the system are enhanced, and meanwhile time and maintenance resources are saved.
The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. It will be clear to those skilled in the art that, for convenience and brevity of description, the specific working procedures of the systems, apparatuses and units described above may refer to the corresponding procedures in the foregoing embodiments, and are not repeated here.
In the several embodiments provided in the present application, it should be understood that the disclosed system and apparatus may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) to perform all or part of the steps described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the application are intended to be included within the scope of the application.
Claims (7)
1. The soil environment parameter information monitoring system based on the intelligent sensing network is characterized by comprising a data acquisition unit, a state analysis unit and a comprehensive analysis unit;
the data acquisition unit is used for collecting measuring and calculating information and communication information of the sensor and sending the measuring and calculating information and the communication information to the state analysis unit;
the state analysis unit comprehensively analyzes the measuring and calculating information and the communication information to obtain a perception advantage measurement coefficient, analyzes the perception advantage measurement coefficient, generates a monitoring effective signal, a monitoring adjustment signal and a monitoring ineffective signal, and sends the monitoring effective signal, the monitoring adjustment signal and the monitoring ineffective signal to the data collection unit;
the comprehensive analysis unit collects soil parameters monitored by each sensor aiming at generating monitoring effective signals and monitoring adjustment signals, performs summarization analysis on the soil parameters, generates soil similarity, analyzes the soil similarity, and divides the corresponding sensors into a unified scheduling set.
2. The soil environment parameter information monitoring system based on the intelligent sensing network according to claim 1, wherein:
the measurement information includes a data quality index and the communication information includes a wireless transmission quality index.
3. The soil environment parameter information monitoring system based on the intelligent sensing network according to claim 2, wherein:
the data quality index acquisition logic is as follows:
step a11, acquiring a plurality of monitoring data acquired by a sensor in t1 time, and counting each monitoring data to construct a data set;
step a12, calculating the mean value and standard deviation of the data set, wherein the mean value represents the central position of the data set, and the standard deviation measures the discrete degree of the data point relative to the mean value; calculating a mean value:wherein->Mean value->Representing the sum of all data points +.>Representing the number of data points; calculating standard deviation: />Wherein->The standard deviation is indicated as such,representing the sum of squares of the differences between each data point and the mean;
step a13, calculating a standard score of each data point, wherein the standard score is used for determining whether the data point is an outlier, and calculating the standard score:wherein->Representing a standard score;
step a14, comparing the standard score of each data point with a standard threshold, if the standard score is greater than the standard threshold, identifying the corresponding data point as an abnormal value, counting the occurrence times of the abnormal value, and calculating the interference degree of the abnormal value, namely the data quality index, wherein the calculation formula is as follows:wherein->Representing data quality index>The number of times of abnormal value occurrence, the total number of data, the average standard fraction and the maximum standard fraction are respectively.
4. The soil environment parameter information monitoring system based on the intelligent sensing network according to claim 3, wherein:
the acquisition logic of the wireless transmission quality index is as follows:
step a21 of establishing a communication link between the sensors and selecting a known, encoded binary data sequence as the transmission data;
step a22, transmitting data by using wireless communication transmission, wherein the data is transmitted through a channel and reaches a receiving end;
step a23, the receiving end receives the transmitted data and decodes the data to restore the binary data sequence;
step a24 of comparing the received binary data sequence with the known original data sequence, checking for the presence of errors for each bit position;
step a25, calculating a correct rate, wherein the correct rate refers to the ratio of the number of bits in which correct data occurs to the total number of bits;
step a26, collecting a plurality of correct rates in t2 time, calculating standard deviation and average value of the correct rates, and calculating the ratio of the standard deviation to the average value, namely the wireless transmission quality index.
5. The system for monitoring soil environment parameter information based on intelligent sensor network according to claim 4, wherein:
the data quality index and the wireless transmission quality index are comprehensively analyzed and calculated to obtain a perception advantage measurement coefficient, and the calculation formula is as follows:wherein->For perceiving the quality metric, +.>Data quality index, wireless transmission quality index, < >>Preset proportionality coefficients of data quality index and radio transmission quality index respectively, and +.>Are all greater than 0.
6. The system for monitoring soil environment parameter information based on an intelligent sensor network according to claim 5, wherein:
comparing the perception advantage measurement coefficient with a first state classification threshold and a second state classification threshold respectively, wherein the first state classification threshold is smaller than the second state classification threshold, and generating a monitoring invalid signal if the perception advantage measurement coefficient is larger than or equal to the second state classification threshold; if the sensing dominance measurement coefficient is larger than or equal to the state classification threshold I and smaller than the state analysis threshold II, generating a monitoring adjustment signal; and if the perception advantage metric coefficient is smaller than the state classification threshold value I, generating a monitoring effective signal.
7. The system for monitoring soil environment parameter information based on intelligent sensor network according to claim 6, wherein:
the operation process of the comprehensive analysis unit comprises the following steps:
step b11, installing a sensor at each monitoring point, and representing the soil data of each monitoring point as a feature vector, wherein each dimension represents a soil parameter or feature, for example, taking parameters such as pH value, humidity, temperature and the like of the soil as different dimensions;
step b12, carrying out standard normalization processing on the soil data of each dimension;
step b13, selecting a monitoring point for generating a monitoring effective signal as a reference point, and comparing other monitoring points with the reference point;
step b14, for each monitoring point to be compared;
if the to-be-monitored site generates a monitoring effective signal, the calculation formula of the soil similarity is as follows:
；
if the point to be monitored generates the monitoring adjustment signal, the calculation formula of the soil similarity is as follows
Wherein->Is a monitoring point->And->Similarity of soil between->And->Two monitoring points are respectively at the +.>Values in the individual feature dimensions +.>Is the total number of feature dimensions; />Is a regulatory factor, and->Greater than 0->Is a perceptual dominance metric coefficient;
step b15, comparing the soil similarity with a soil similarity threshold, if the soil similarity is greater than or equal to the soil similarity threshold, dividing the monitoring point and the reference point into the same soil area, otherwise, dividing the monitoring point and the reference point into different land areas;
and b16, counting the sensors which are in the same soil area and generate signals which are non-monitoring invalid signals, and dividing the sensors into a unified scheduling set.
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