A natural resource intelligent monitoring method and system

Abstract

The present invention discloses a natural resource intelligent monitoring method and system, and relates to the technical field of natural resource data processing. The natural resource intelligent monitoring method comprises the following steps: obtaining sub-region data; obtaining sub-region index; monitoring anomaly adjustment; and sensor distortion adjustment. The present invention obtains sensor network layout data and outputs monitoring data, and then obtains resource transmission anomaly index and sensor anomaly index in combination with reference resource anomaly data and reference sensor anomaly data, and then performs monitoring anomaly adjustment according to the resource transmission anomaly index and the sensor anomaly index, and finally obtains distortion evaluation index in combination with transmission distortion data and reference distortion data and performs sensor distortion adjustment, thereby achieving a more accurate effect of performing natural resource intelligent monitoring, and solving the problem of inaccurate data in the natural resource intelligent monitoring process in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of natural resource data processing, and in particular to a natural resource intelligent monitoring method and system. Background Art

[0002] In recent years, with the rapid development of remote sensing technology, artificial intelligence, big data and other technologies, strong technical support has been provided for the intelligent monitoring of natural resources. Remote sensing technology can obtain a wide range of natural resource information in real time, providing a rich data source for monitoring; artificial intelligence technology can intelligently interpret and analyze remote sensing data to improve the accuracy and efficiency of monitoring; big data technology can store, process and analyze massive data, providing comprehensive data support for monitoring. The organic combination of these technologies has formed the core technical system of the intelligent monitoring method of natural resources.

[0003] Existing methods obtain multi-temporal, multi-spectral, and high-resolution remote sensing image data of natural resources through remote sensing platforms such as satellites and drones.

[0004] For example, the invention patent with announcement number: CN117196226B announces a forest tree network system and a forest resource monitoring method, including: a forest tree network design module, a forest Internet of Things monitoring station construction module, a forest tree network operation collection module, a forest resource assessment module, a forest tree network monitoring and early warning module, and a forest tree network scheduling and maintenance module, and uses the forest tree network system to implement the forest resource monitoring method. This invention can achieve high-frequency monitoring of the growth status of trees in the forest ecosystem by designing and building a forest Internet of Things monitoring station, directly output important indicator data such as forest growth, consumption, loss, forest stock per unit area, and forest biomass per unit area, and carry out regulation, scheduling, control and protection of the forest ecosystem.

[0005] For example, the invention patent announcement with announcement number: CN117391548B announces a land resource carrying capacity monitoring and early warning method, including: by utilizing the land resource environmental carrying capacity evaluation results, the monitored objects are divided into three different carrying states: loadable, critical overload, and overload. In addition, adaptive weights of influencing factors based on different carrying states are formulated from the three aspects of resource utilization, ecological protection, and spatial structure. A land resource carrying capacity remote sensing monitoring and early warning indicator system that takes into account both economic development and ecological protection is constructed, so as to achieve seamless connection and full life cycle management of the land resource carrying capacity "evaluation-monitoring-early warning" results, and serve the land resource supervision and resource and environmental carrying capacity monitoring and early warning work.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, in remote areas, due to the long transmission distance and strong interference, the data collected by sensor nodes are easily lost or distorted during the transmission process. The energy of sensor nodes is limited and cannot be maintained regularly, resulting in inaccurate data in the process of intelligent monitoring of natural resources. Summary of the invention

[0008] The embodiments of the present application solve the problem of inaccurate data in the process of natural resource intelligent monitoring in the prior art by providing a natural resource intelligent monitoring method and system, thereby achieving more accurate natural resource intelligent monitoring.

[0009] The embodiment of the present application provides a natural resource intelligent monitoring method, comprising the following steps: S1, dividing the natural resource monitoring area to obtain sub-areas, and obtaining sensor network deployment data and output monitoring data through preset sensor measurement devices deployed in the sub-areas; S2, obtaining reference resource anomaly data from a preset database and combining the obtained output monitoring data to obtain a resource transmission anomaly index, obtaining reference sensor anomaly data from a preset database and combining the obtained sensor network deployment data to obtain a sensor anomaly index, the resource transmission anomaly index is used to evaluate the abnormal degree of natural resource output in the sub-area, and the sensor anomaly index is used to evaluate the monitoring anomaly of the sensor in the sub-area; S3, obtaining a preset transmission threshold range and a preset sensor threshold range from the preset database and combining the resource transmission anomaly index and the sensor anomaly index to determine whether to perform monitoring anomaly adjustment; S4, obtaining transmission distortion data of the preset sensor measurement device in the sub-area and combining the reference distortion data obtained from the preset library to obtain a distortion evaluation index, judging whether to perform sensor distortion adjustment based on the distortion evaluation index and the preset distortion threshold range obtained from the preset database, and the distortion evaluation index is used to evaluate the distortion monitored by the preset sensor measurement device.

[0010] Furthermore, the specific process of obtaining reference resource anomaly data from a preset database and combining it with the obtained output monitoring data to obtain a resource transmission anomaly index is as follows: obtaining reference resource anomaly data from a preset database, the reference resource anomaly data including a preset maximum propagation time threshold, a preset data transmission rate threshold and a preset output rate threshold; obtaining transmission delay deviation, transmission rate deviation and resource output rate deviation in combination with the reference resource anomaly data and the output monitoring data; judging whether the transmission delay deviation, transmission rate deviation and resource output rate deviation are not greater than 0, if the transmission delay deviation, transmission rate deviation and resource output rate deviation are not greater than 0, then recording the corresponding transmission delay deviation, transmission rate deviation and resource output rate deviation as 0, otherwise the obtained transmission delay deviation, transmission rate deviation and resource output rate deviation are not changed; obtaining resource transmission allocation weights from a preset database and obtaining a resource transmission anomaly index in combination with the corresponding transmission delay deviation, transmission rate deviation and resource output rate deviation, the resource transmission allocation weights including a first resource transmission allocation weight, a second resource transmission allocation weight and a third resource transmission allocation weight.

[0011] Furthermore, the resource transmission anomaly index is calculated using the following formula:

[0012] ;

[0013] ;

[0014] ;

[0015] ;

[0016] In the formula, represents the resource transmission anomaly index of the natural resources monitored in the mth sub-area during the rth preset time period, , r represents the number of the preset time period, n represents the total number of preset time periods, , m represents the number of the sub-region, s represents the total number of sub-regions, The transmission delay deviation of the natural resources monitored in the mth sub-area during the rth preset time period, represents the transmission rate deviation of the natural resources monitored in the mth sub-area in the rth preset time period, represents the resource output rate deviation of the natural resources monitored in the mth sub-area during the rth preset time period, represents the signal propagation distance of the natural resources monitored in the mth sub-area during the rth preset time period, represents the signal propagation speed of the natural resources monitored in the mth sub-area during the rth preset time period, represents the bandwidth of the natural resources monitored in the mth sub-area during the rth preset time period, represents the signal-to-noise ratio of the natural resources monitored in the mth sub-area during the rth preset time period, represents the resource output of natural resources monitored in the mth sub-area during the rth preset time period, represents the output time of the natural resources monitored in the mth sub-area during the rth preset time period, represents the preset maximum propagation time threshold, Indicates the preset data transmission rate threshold. Indicates the preset output rate threshold, represents the first allocation weight of resource transmission, represents the second allocation weight of resource transmission, represents the third allocation weight of resource transmission, and e represents a natural constant.

[0017] Furthermore, the specific process of obtaining the reference sensor abnormality data from the preset database and combining it with the obtained sensor network layout data to obtain the sensor abnormality index is as follows: obtaining the reference sensor abnormality data from the preset database, the reference sensor abnormality data including a preset power consumption threshold, a preset electric power threshold, a preset power consumption deviation threshold and a preset electric power deviation threshold; obtaining a power consumption deviation value and an electric power deviation value in combination with the sensor network layout data and the reference sensor abnormality data, the power consumption deviation value indicating the difference between the power consumption of the resource flow sensor monitored in the preset time period of the sub-area and the preset power consumption threshold, the electric power deviation value indicating the difference between the preset electric power threshold and the electric power of the resource flow sensor monitored in the preset time period of the sub-area value; determine whether the power consumption deviation value and the electric power deviation value are not greater than 0. If the power consumption deviation value and the electric power deviation value are not greater than 0, the corresponding power consumption deviation value and the electric power deviation value are recorded as 0, otherwise the acquired power consumption deviation value and the electric power deviation value are not changed; obtain the preset power consumption deviation threshold, the preset electric power deviation threshold, the first sensor allocation weight and the second sensor allocation weight from the preset database; obtain the abnormal situation of power consumption monitoring through the power consumption deviation value, the preset power consumption deviation threshold and the first sensor allocation weight; obtain the abnormal situation of power monitoring through the electric power deviation value, the preset electric power deviation threshold and the second sensor allocation weight; combine the acquired abnormal situation of power consumption monitoring and the abnormal situation of power monitoring to perform summation operation to obtain the sensor abnormality index.

[0018] Furthermore, the specific process of obtaining the preset transmission threshold range and the preset sensor threshold range from the preset database, and performing monitoring abnormality adjustment in combination with the resource transmission abnormality index and the sensor abnormality index is as follows: Q1, determining whether the resource transmission abnormality index is within the preset transmission threshold range. If the resource transmission abnormality index is within the preset transmission threshold range, no monitoring abnormality adjustment is performed, otherwise Q2 is executed; Q2, sending a prompt to add a preset relay device type to the preset personnel, and stopping execution when the monitored resource transmission abnormality index is within the preset transmission threshold range, otherwise Q3 is executed; Q3, sending a prompt to add a preset signal enhancement device type to the preset personnel, and stopping execution when the monitored resource transmission abnormality index is within the preset transmission threshold range, otherwise feedback is given to the preset personnel for sensor replacement and redeployment.

[0019] Furthermore, the monitoring abnormality adjustment also includes: P1, judging whether the sensor abnormality index is within the preset sensor threshold range. If the resource transmission abnormality index is within the preset sensor threshold range, no monitoring abnormality adjustment is performed, otherwise P2 is executed; P2, obtaining the sensor sampling frequency threshold and preset multiple from the preset database, sending a prompt to the system to gradually reduce the sampling frequency of the resource flow sensor by the preset multiple of the sensor sampling frequency threshold, and when the monitored resource transmission abnormality index is within the preset sensor threshold range, stop executing, otherwise execute P3; P3, obtaining the preset resolution threshold and preset multiple from the preset database, sending a prompt to the system to gradually reduce the resolution of the resource flow sensor by the preset multiple of the preset resolution threshold, and when the monitored resource transmission abnormality index is within the preset sensor threshold range, stop executing, otherwise feedback is given to the preset personnel for sensor replacement and redeployment.

[0020] Furthermore, the specific process of obtaining the transmission distortion data of the preset sensing measurement device in the sub-area and combining it with the reference distortion data obtained from the preset library to obtain the distortion evaluation index is as follows: obtaining the transmission distortion data of the resource flow sensor through the preset distortion measurement equipment in the preset time period, the transmission distortion data including the phase value, input power and output power; obtaining the reference distortion data from the preset database, the reference distortion data including the preset phase threshold, the preset attenuation threshold, the preset phase deviation threshold and the preset attenuation deviation threshold; performing a difference operation on the phase value and the preset phase threshold to obtain the phase deviation value, the phase deviation value indicating the difference between the phase value and the preset phase threshold; performing a difference operation on the input power, the output power and the preset attenuation threshold to obtain the phase deviation value. The attenuation deviation value is obtained by performing difference operation on the values, and the attenuation deviation value represents the difference between the signal attenuation monitored by the resource flow sensor and the preset attenuation threshold value; it is determined whether the phase deviation value and the attenuation deviation value are not greater than 0, and if the phase deviation value and the attenuation deviation value are not greater than 0, the corresponding phase deviation value and the attenuation deviation value are recorded as 0, otherwise the obtained phase deviation value and the attenuation deviation value are not changed; the preset phase deviation threshold value is obtained from the preset database, and the abnormal situation of phase monitoring is obtained in combination with the phase deviation value; the preset attenuation deviation threshold value is obtained from the preset database, and the abnormal situation of attenuation monitoring is obtained in combination with the attenuation deviation value; the obtained abnormal situation of phase monitoring and the abnormal situation of attenuation monitoring are summed up to obtain the distortion assessment index.

[0021] Furthermore, the specific process of judging whether to perform sensor distortion adjustment based on the distortion assessment index and the preset distortion threshold range obtained from the preset database is as follows: W1, judging whether the distortion assessment index is within the preset distortion threshold range. When the distortion assessment index is within the preset distortion threshold range, no sensor distortion adjustment is performed, otherwise W2 is executed; W2, performing denoising processing, stopping execution when the monitored distortion assessment index is within the preset distortion threshold range, otherwise W3 is executed, and the denoising processing is used to improve the accuracy of natural resource output; W3, obtaining a preset gain threshold and a preset multiple from the preset database, sending a prompt to the system to gradually increase the gain of the resource flow sensor by a preset multiple of the preset gain threshold, stopping execution when the monitored distortion assessment index is within the preset distortion threshold range, otherwise feedback is given to the preset personnel to perform system maintenance and redeploy the sensor.

[0022] The embodiment of the present application provides a natural resource intelligent monitoring system, including: a sub-region data acquisition module, a sub-region index acquisition module, a monitoring anomaly adjustment module and a sensor distortion adjustment module; wherein the sub-region data acquisition module is used to divide the natural resource monitoring area into sub-regions, and obtain sensor network deployment data and output monitoring data through preset sensor measurement devices deployed in the sub-regions; the sub-region index acquisition module is used to obtain reference resource anomaly data from a preset database and combine the obtained output monitoring data to obtain a resource transmission anomaly index, obtain reference sensor anomaly data from a preset database and combine the obtained sensor network deployment data to obtain a sensor anomaly index, and the resource transmission anomaly index is used to evaluate The abnormal degree of natural resource output in the sub-area, the sensor abnormality index is used to evaluate the monitoring abnormality of the sensor in the sub-area; the monitoring abnormality adjustment module is used to obtain the preset transmission threshold range and the preset sensor threshold range from the preset database and determine whether to perform monitoring abnormality adjustment in combination with the resource transmission abnormality index and the sensor abnormality index; the sensor distortion adjustment module is used to obtain the transmission distortion data of the preset sensor measurement device in the sub-area and obtain the distortion evaluation index in combination with the reference distortion data obtained from the preset library, and determine whether to perform sensor distortion adjustment based on the distortion evaluation index and the preset distortion threshold range obtained from the preset database, and the distortion evaluation index is used to evaluate the distortion situation monitored by the preset sensor measurement device.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0024] 1. By acquiring sensor network deployment data and output monitoring data, and then combining reference resource anomaly data and output monitoring data to obtain the resource transmission anomaly index, and then combining reference sensor anomaly data and sensor network deployment data to obtain the sensor anomaly index, and finally combining transmission distortion data and reference distortion data to obtain the distortion assessment index and perform sensor distortion adjustment, the real-time performance of natural resource intelligent monitoring is improved, and more accurate natural resource intelligent monitoring is achieved, effectively solving the problem of inaccurate data in the natural resource intelligent monitoring process in the prior art.

[0025] 2. By obtaining the preset transmission threshold range and the preset sensor threshold range and combining the resource transmission anomaly index and the sensor anomaly index to determine whether to make monitoring anomaly adjustments, and based on the distortion assessment index and the preset distortion threshold range to determine whether to make sensor distortion adjustments, the effectiveness of natural resource intelligent monitoring is improved, thereby achieving an improvement in the reliability of natural resource intelligent monitoring.

[0026] 3. The preset sensor measurement devices deployed in the sub-areas are used to obtain the sensor network layout data and output monitoring data, and the preset distortion measurement equipment is used to obtain the transmission distortion data of the resource flow sensor, thereby improving the real-time performance of obtaining relevant data for natural resource monitoring, and further improving the accuracy of obtaining relevant data for natural resource monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flowchart of a natural resource intelligent monitoring method provided in an embodiment of the present application;

[0028] Figure 2 A statistical graph of changes in the sensor abnormality index provided in the embodiment of the present application;

[0029] Figure 3 A schematic diagram of the structure of a natural resource intelligent monitoring system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The embodiment of the present application solves the problem of inaccurate data in the process of intelligent monitoring of natural resources in the prior art by providing a method and system for intelligent monitoring of natural resources. The method divides the natural resource monitoring area into sub-areas, and obtains sensor network deployment data and output monitoring data through preset sensor measurement devices deployed in the sub-areas. Then, the resource transmission anomaly index is obtained by combining reference resource anomaly data and the obtained output monitoring data. Then, the sensor anomaly index is obtained by combining reference sensor anomaly data and the obtained sensor network deployment data. Monitoring anomaly adjustment is performed based on the resource transmission anomaly index and the sensor anomaly index. Finally, the transmission distortion data and the reference distortion data are combined to obtain the distortion assessment index and the sensor distortion adjustment is performed, thereby achieving more accurate intelligent monitoring of natural resources.

[0031] The technical solution in the embodiment of the present application is to solve the problem of inaccurate data in the above-mentioned natural resource intelligent monitoring process, and the overall idea is as follows:

[0032] The resource transmission anomaly index and the sensor anomaly index are obtained by sensing network deployment data, output monitoring data, reference resource anomaly data and reference sensor anomaly data. Then, the monitoring anomaly adjustment is performed based on the resource transmission anomaly index and the sensor anomaly index. Finally, the distortion assessment index is obtained by combining the transmission distortion data and the reference distortion data, and the sensor distortion adjustment is performed, thereby achieving a more accurate effect of intelligent monitoring of natural resources.

[0033] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0034] like Figure 1As shown, it is a flow chart of a natural resource intelligent monitoring method provided by an embodiment of the present application, and the method includes the following steps: S1, dividing the natural resource monitoring area into sub-areas, and obtaining sensor network deployment data and output monitoring data through preset sensor measurement devices deployed in the sub-areas, wherein the sensor network deployment data represents the specification information of the deployed preset sensor measurement devices, and the output monitoring data is used to describe the natural resource output monitored by the preset sensor measurement devices; S2, obtaining reference resource anomaly data from a preset database and combining the obtained output monitoring data to obtain a resource transmission anomaly index, obtaining reference sensor anomaly data from a preset database and combining the obtained sensor network deployment data to obtain a sensor anomaly index, the resource transmission anomaly index is used to evaluate the degree of abnormality of the natural resource output in the sub-area, and the sensor anomaly index Used to evaluate the monitoring anomaly of sensors in the sub-area; S3, obtain the preset transmission threshold range and the preset sensing threshold range from the preset database and determine whether to perform monitoring anomaly adjustment in combination with the resource transmission anomaly index and the sensing anomaly index, the monitoring anomaly adjustment is used to adjust the resource transmission anomaly index to within the preset transmission threshold range and the sensing anomaly index to within the preset sensing threshold range; S4, obtain the transmission distortion data of the preset sensing measurement device in the sub-area and obtain the distortion evaluation index in combination with the reference distortion data obtained from the preset library, determine whether to perform sensing distortion adjustment based on the distortion evaluation index and the preset distortion threshold range obtained from the preset database, the distortion evaluation index is used to evaluate the distortion situation monitored by the preset sensing measurement device, and the sensing distortion adjustment is used to adjust the distortion evaluation index to within the preset distortion threshold range.

[0035] Among them, the preset sensing and measuring devices include resource flow sensors, voltmeters, ammeters, timers, bandwidth testers, signal-to-noise ratio testers, multimeters, time domain reflectometers, electromagnetic wave rangefinders and network performance testers; the output monitoring data include bandwidth, signal-to-noise ratio, signal propagation distance, signal propagation speed, output time and resource output; the sensing network layout data include sensing voltage, sensing resistance and sensing current; the sensing voltage represents the voltage of the resource flow sensor monitored in the preset time period of the sub-area; the sensing resistance represents the resistance of the resource flow sensor monitored in the preset time period of the sub-area; the sensing current represents the current of the resource flow sensor monitored in the preset time period of the sub-area; the output time represents the total output duration of the natural resources monitored in the preset time period of the sub-area; the resource output represents the total output of the natural resources monitored in the preset time period of the sub-area.

[0036] In this embodiment, the natural resource monitoring area is divided by using GIS (Geographic Information System) based on vegetation coverage and soil moisture, and a preset location point with the least degree of electromagnetic interference is selected to install a preset sensing and measuring device. If there are multiple preset location points, the preset location point farthest from the interference source is selected to install the preset sensing and measuring device. When an abnormal situation occurs in the resource flow sensor monitoring, the data monitored by the sensor in the sub-area will be erroneous, resulting in the resource transmission anomaly index not being within the preset transmission threshold range, thereby achieving more accurate intelligent monitoring of natural resources.

[0037] Specifically, a voltmeter is used to directly measure the supply voltage of the resource flow sensor, an ammeter is used to measure the working current of the sensor, a multimeter is used to directly measure the resistance of the sensor, a bandwidth tester is used to measure the bandwidth of the sensor network, a signal-to-noise ratio tester is used to measure the signal-to-noise ratio of the sensor network, a timer is used to record the time when the sensor outputs data, a time domain reflectometer is used to obtain the signal propagation speed, an electromagnetic wave rangefinder and a timer are used to calculate the propagation distance by measuring the time difference or phase difference from the transmission to the reception of the signal, and the signal propagation distance is obtained, and special instruments such as resource flow sensors are used to measure the actual output of natural resources in the monitoring area, such as gas emissions in forest resources; in the field of forest resource monitoring, comprehensive, efficient and accurate monitoring and management of forest resources can be achieved by monitoring the output monitoring data; more accurate intelligent monitoring of natural resources is achieved.

[0038] Furthermore, the specific process of obtaining the reference resource anomaly data from the preset database and combining it with the obtained output monitoring data to obtain the resource transmission anomaly index is as follows: obtaining the reference resource anomaly data from the preset database, the reference resource anomaly data including a preset maximum propagation time threshold, a preset data transmission rate threshold and a preset output rate threshold; combining the reference resource anomaly data and the output monitoring data to obtain the transmission delay deviation, the transmission rate deviation and the resource output rate deviation, the transmission delay deviation is used to evaluate the deviation degree of the natural resource transmission delay in the sub-region, the transmission rate delay is used to evaluate the deviation degree of the natural resource transmission rate in the sub-region, and the resource output rate deviation is used to evaluate the natural resource output rate in the sub-region. Deviation degree; determine whether the transmission delay deviation, transmission rate deviation and resource output rate deviation are not greater than 0. If the transmission delay deviation, transmission rate deviation and resource output rate deviation are not greater than 0, the corresponding transmission delay deviation, transmission rate deviation and resource output rate deviation are recorded as 0, otherwise the acquired transmission delay deviation, transmission rate deviation and resource output rate deviation are not changed; obtain the resource transmission allocation weight from the preset database and obtain the resource transmission anomaly index in combination with the corresponding transmission delay deviation, transmission rate deviation and resource output rate deviation. The resource transmission allocation weight includes the first resource transmission allocation weight, the second resource transmission allocation weight and the third resource transmission allocation weight. The first resource transmission allocation weight is used to evaluate the influence of the transmission delay deviation on the resource transmission anomaly index, the second resource transmission allocation weight is used to evaluate the influence of the transmission rate deviation on the resource transmission anomaly index, and the third resource transmission allocation weight is used to evaluate the influence of the resource output rate deviation on the resource transmission anomaly index.

[0039] The resource transmission anomaly index is calculated using the following formula:

[0040] ;

[0041] ;

[0042] ;

[0043] ;

[0044] In the formula, represents the resource transmission anomaly index of the natural resources monitored in the mth sub-area during the rth preset time period, , r represents the number of the preset time period, n represents the total number of preset time periods, , m represents the number of the sub-region, s represents the total number of sub-regions, The transmission delay deviation of the natural resources monitored in the mth sub-area during the rth preset time period, represents the transmission rate deviation of the natural resources monitored in the mth sub-area in the rth preset time period, represents the resource output rate deviation of the natural resources monitored in the mth sub-area during the rth preset time period, represents the signal propagation distance of the natural resources monitored in the mth sub-area during the rth preset time period, represents the signal propagation speed of the natural resources monitored in the mth sub-area during the rth preset time period, represents the bandwidth of the natural resources monitored in the mth sub-area during the rth preset time period, represents the signal-to-noise ratio of the natural resources monitored in the mth sub-area during the rth preset time period, represents the resource output of natural resources monitored in the mth sub-area during the rth preset time period, represents the output time of the natural resources monitored in the mth sub-area during the rth preset time period, represents the preset maximum propagation time threshold, Indicates the preset data transmission rate threshold. Indicates the preset output rate threshold, represents the first allocation weight of resource transmission, represents the second allocation weight of resource transmission, represents the third allocation weight of resource transmission, and e represents a natural constant.

[0045] In this embodiment, the preset maximum propagation time threshold is represented by the maximum value of the propagation delay time data in the natural resource monitoring process in the historical period in the preset database, the preset data transmission rate threshold is represented by the average value of the transmission rate data of the natural resource monitoring in the historical period in the preset database, and the preset output rate threshold is represented by the average value of the output rate data of the natural resource monitoring in the historical period in the preset database; thereby achieving more accurate intelligent monitoring of natural resources.

[0046] Specifically, the sum of the first allocation weight of resource transmission, the second allocation weight of resource transmission and the third allocation weight of resource transmission is 1, for example, the first allocation weight of resource transmission is 0.4, the second allocation weight of resource transmission is 0.3, and the third allocation weight of resource transmission is 0.3; the first allocation weight of resource transmission is the weight corresponding to the transmission delay value preset in the preset database, which represents the degree of influence of the transmission delay value on the resource transmission anomaly index. When used, the weight corresponding to the preset transmission delay value can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the transmission delay in the natural resource monitoring training set and the weight corresponding to the preset transmission delay value in the preset database form a mapping set, and the real-time transmission delay is input into the mapping set to obtain the corresponding weight, wherein the mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, its value range is [0, 1].

[0047] Specifically, the second allocation weight of resource transmission is the weight corresponding to the transmission rate value preset in the preset database, which represents the value of the influence of the transmission rate value on the resource transmission anomaly index. When used, the weight corresponding to the preset transmission rate value can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the transmission rate of the natural resource monitoring training set and the weight corresponding to the transmission rate value preset in the preset database form a mapping set, and the real-time transmission rate is input into the mapping set to obtain the corresponding weight, wherein the mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, its value range is [0, 1].

[0048] The algorithm of this embodiment combines the output monitoring data and comprehensively analyzes to obtain the resource transmission anomaly index. In this formula, there is a common adjustment relationship between the output monitoring data. When the transmission delay deviation, transmission rate deviation and resource output rate deviation are greater than 0, for example, the higher the bandwidth, the greater the amount of data that can be transmitted per unit time, thereby improving the frequency and efficiency of data transmission. In forest resource monitoring, the higher the signal-to-noise ratio, the more accurately the state of forest resources can be identified and analyzed. The longer the signal propagation distance, the wider the forest area can be covered. As the propagation distance increases, the signal attenuation may increase. Increasing the signal propagation speed can shorten the data transmission time and improve the response speed of the monitoring system. The faster the propagation speed, the shorter the output time. The shortening of the output time can improve the timeliness of monitoring. As the signal propagation speed and resource output increase, the signal propagation distance and output time decrease, and the resource transmission anomaly index decreases, which means that the signal propagation speed and resource output are inversely proportional to the resource transmission anomaly index, and the signal propagation distance and output time are directly proportional to the resource transmission anomaly index;

[0049] Specifically, assuming that the transmission delay skew The range is 0-1, the transmission rate deviation The range is 0-1, resource output rate deviation The range is 0-1, the first allocation weight of resource transmission is 0.4, the second allocation weight of resource transmission is 0.3, and the third allocation weight of resource transmission is 0.3. As shown in Table 1, it is a statistical table of changes in the resource transmission anomaly index provided in an embodiment of the present application:

[0050] Table 1 Statistics of changes in resource transmission anomaly index

[0051] <![CDATA[Transmission delay deviation Ω 1rm > <![CDATA[Transmission rate deviation Ω 2rm > <![CDATA[Resource output rate deviation Ω 3rm > <![CDATA[Resource transmission exception index X 1rm > 0.93 0.89 0.91 1.4219 0.78 0.67 0.47 1.2887 0.54 0.34 0.21 1.1601 .. ... ... ...

[0052] It can be seen from the table that the transmission delay deviation , Transmission rate deviation and resource output rate deviation When the transmission delay deviation is greater than 0, , Transmission rate deviation and resource output rate deviation The resource transmission anomaly index gradually decreases, which means that the abnormal degree of natural resource output in the sub-region is gradually reduced; the abnormal degree of natural resource output in the sub-region is quantified, and then the abnormal situation of natural resource output in the sub-region can be discovered in time, realizing more accurate intelligent monitoring of natural resources.

[0053] Furthermore, the specific process of obtaining the reference sensor abnormality data from the preset database and combining it with the obtained sensor network layout data to obtain the sensor abnormality index is as follows: obtain the reference sensor abnormality data from the preset database, the reference sensor abnormality data including a preset power consumption threshold, a preset electric power threshold, a preset power consumption deviation threshold and a preset electric power deviation threshold; obtain the power consumption deviation value and the electric power deviation value in combination with the sensor network layout data and the reference sensor abnormality data, the power consumption deviation value indicating the difference between the power consumption of the resource flow sensor monitored in the preset time period of the sub-area and the preset power consumption threshold, the electric power deviation value indicating the difference between the preset electric power threshold and the electric power of the resource flow sensor monitored in the preset time period of the sub-area; determine whether the power consumption deviation value and the electric power deviation value are not greater than 0, if the power consumption deviation value and the electric power deviation value are greater than 0, If the value is not greater than 0, the corresponding power consumption deviation value and electric power deviation value are recorded as 0, otherwise the acquired power consumption deviation value and electric power deviation value are not changed; the preset power consumption deviation threshold, the preset electric power deviation threshold, the first sensor allocation weight and the second sensor allocation weight are obtained from the preset database, the first sensor allocation weight is used to evaluate the influence of the power consumption deviation value on the sensor abnormality index, and the second sensor allocation weight is used to evaluate the influence of the electric power deviation value on the sensor abnormality index; the abnormal situation of power consumption monitoring is obtained through the power consumption deviation value, the preset power consumption deviation threshold and the first sensor allocation weight; the abnormal situation of power monitoring is obtained through the electric power deviation value, the preset electric power deviation threshold and the second sensor allocation weight; the sensor abnormality index is obtained by summing up the acquired abnormal situation of power consumption monitoring and the abnormal situation of power monitoring.

[0054] The sensor anomaly index is calculated using the following formula:

[0055] ;

[0056] ;

[0057] ;

[0058] In the formula, represents the sensor abnormality index of the resource flow sensor monitored in the mth sub-area during the rth preset time period, , r represents the number of the preset time period, n represents the total number of preset time periods, , m represents the number of the sub-region, s represents the total number of sub-regions, represents the power consumption deviation value of the resource flow sensor monitored in the mth sub-area in the rth preset time period, represents the electric power deviation value of the resource flow sensor monitored in the mth sub-area in the rth preset time period, represents the sensor voltage of the resource flow sensor monitored in the mth sub-area during the rth preset time period, represents the sensing current of the resource flow sensor monitored in the mth sub-area during the rth preset time period, represents the sensor resistance of the resource flow sensor monitored in the mth sub-area during the rth preset time period, Indicates the preset power consumption threshold. Indicates the preset electric power threshold, Indicates the preset power consumption deviation threshold. represents the preset electric power deviation threshold, and e represents a natural constant.

[0059] In this embodiment, the preset power consumption threshold is represented by the average value of the natural resource monitoring power consumption data in the historical period in the preset database, the preset electric power threshold is represented by the average value of the natural resource monitoring electric power data in the historical period in the preset database, the preset power consumption deviation threshold is represented by the variance value of the natural resource monitoring power consumption data in the historical period in the preset database, and the preset electric power deviation threshold is represented by the variance value of the natural resource monitoring electric power data in the historical period in the preset database;

[0060] The algorithm of this embodiment combines the sensor network layout data and comprehensively analyzes to obtain the sensor anomaly index. In this formula, there is a common adjustment relationship between the sensor network layout data. When the power consumption deviation value and the electric power deviation value are greater than 0, for example, the sensor voltage usually reflects the potential level of the sensor output signal, and the sensor current is the current flowing through the sensor. The change of sensor resistance directly affects the magnitude of the sensor current. When the sensor voltage is constant, the increase of sensor resistance will cause the sensor current to decrease. The change of sensor voltage may affect the resistance characteristics of the sensor. As the sensor resistance and sensor current increase, the sensor anomaly index increases, which means that the sensor resistance and sensor current are proportional to the sensor anomaly index; the mutual influence between sensor voltage, sensor resistance and sensor current is crucial for evaluating the monitoring anomalies of sensors in natural resource areas. Through continuous monitoring and analysis of these physical quantities, sensor anomalies can be discovered in a timely manner;

[0061] Specifically, the sum of the first sensor allocation weight and the second sensor allocation weight is 1, for example, the first sensor allocation weight is 0.5, and the second sensor allocation weight is 0.5; the first sensor allocation weight is the weight corresponding to the power consumption value preset in the preset database, which represents the value of the influence of the power consumption value on the resource transmission anomaly index. When used, the weight corresponding to the preset power consumption value can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the power consumption of the natural resource monitoring training set and the weight corresponding to the power consumption value preset in the preset database form a mapping set, and the real-time power consumption is input into the mapping set to obtain the corresponding weight, wherein the mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, its value range is [0, 1].

[0062] Specifically, when the power consumption deviation value and the electric power deviation value are greater than 0, it is assumed that the power consumption deviation value The range is 0-1 (W), the power deviation value The range is 0-1 (W), and the power consumption deviation threshold is preset The preset power deviation threshold is 0.3 (W). is 0.5 (W), the first sensing allocation weight is 0.5, and the second sensing allocation weight is 0.5, such as Figure 2 As shown in the figure, it is a statistical diagram of the change of the sensor abnormality index provided in the embodiment of the present application. It can be seen from the figure that as the power consumption deviation value And electric power deviation The sensor anomaly index gradually increases, which means that the monitoring anomaly of sensors in the area gradually increases; more accurate intelligent monitoring of natural resources is achieved.

[0063] Furthermore, the preset transmission threshold range and the preset sensing threshold range are obtained from the preset database, and the specific process of performing monitoring abnormality adjustment in combination with the resource transmission abnormality index and the sensing abnormality index is as follows: Q1, determine whether the resource transmission abnormality index is within the preset transmission threshold range. If the resource transmission abnormality index is within the preset transmission threshold range, no monitoring abnormality adjustment is performed, otherwise Q2 is executed; Q2, send a prompt to increase the preset relay device type to the preset personnel, and stop executing when the monitored resource transmission abnormality index is within the preset transmission threshold range, otherwise execute Q3, the preset relay device is used to shorten the signal propagation distance, and the preset relay device includes a wireless repeater and a fiber optic repeater; Q3, send a prompt to increase the preset signal enhancement device type to the preset personnel, and stop executing when the monitored resource transmission abnormality index is within the preset transmission threshold range, otherwise feedback is given to the preset personnel for sensor replacement and redeployment, and the preset signal enhancement equipment includes a fiber optic signal enhancer, a signal amplifier, and an extender.

[0064] In this embodiment, a preset transmission threshold and a preset multiple are obtained from a preset database. The preset transmission threshold is represented by the average value of the natural resource transmission data in the historical period. 0.5 times the preset transmission threshold is used as the lower limit of the preset transmission threshold range, and 1.5 times the preset transmission threshold is used as the upper limit of the preset transmission threshold range. By increasing the type of relay equipment, the signal propagation distance can be shortened and the signal attenuation can be reduced, thereby improving the quality and stability of data transmission. In an environment with weak signals or severe interference, adding signal enhancement equipment types can improve signal strength and ensure the reliability of data transmission; more accurate intelligent monitoring of natural resources is achieved.

[0065] Furthermore, the monitoring anomaly adjustment also includes: P1, judging whether the sensor anomaly index is within the preset sensor threshold range. If the resource transmission anomaly index is within the preset sensor threshold range, no monitoring anomaly adjustment is performed, otherwise P2 is executed; P2, obtaining the sensor sampling frequency threshold and preset multiple from the preset database, sending a prompt to the system to gradually reduce the sampling frequency of the resource flow sensor by the preset multiple of the sensor sampling frequency threshold, and when the monitored resource transmission anomaly index is within the preset sensor threshold range, stop executing, otherwise execute P3; P3, obtaining the preset resolution threshold and preset multiple from the preset database, sending a prompt to the system to gradually reduce the resolution of the resource flow sensor by the preset multiple of the preset resolution threshold, and when the monitored resource transmission anomaly index is within the preset sensor threshold range, stop executing, otherwise feedback is given to the preset personnel for sensor replacement and redeployment.

[0066] In this embodiment, a preset sensor threshold and a preset multiple are obtained from a preset database, the preset sensor threshold is represented by the average value of the power consumption data of the natural resource sensor in the historical period, 0.5 times the preset sensor threshold is used as the lower limit of the preset sensor threshold range, and 1.5 times the preset sensor threshold is used as the upper limit of the preset sensor threshold range, the sensor sampling frequency threshold is represented by the average value of the sampling frequency data of the natural resource sensor in the historical period, and the preset resolution threshold is represented by the average value of the resolution data of the natural resource sensor in the historical period. By reducing the sampling frequency, the data output of the sensor can be reduced, thereby reducing the network burden and improving the stability of data transmission. Reducing the resolution of the sensor can reduce the complexity of data processing and resource consumption, which helps to improve the overall performance of the system; more accurate intelligent monitoring of natural resources is achieved.

[0067] Furthermore, the specific process of obtaining the transmission distortion data of the preset sensor measurement device in the sub-area and combining it with the reference distortion data obtained from the preset library to obtain the distortion evaluation index is as follows: obtaining the transmission distortion data of the resource flow sensor through the preset distortion measurement equipment in the preset time period, the transmission distortion data including the phase value, input power and output power, the preset distortion measurement equipment including the vector network analyzer, the power meter, and the phase meter, the preset distortion measurement equipment is deployed in the sub-area by the preset personnel at the preset position in the sub-area; obtaining the reference distortion data from the preset database, the reference distortion data including the preset phase threshold, the preset attenuation threshold, the preset phase deviation threshold and the preset attenuation deviation threshold; performing the difference operation between the phase value and the preset phase threshold to obtain the phase deviation value, the phase deviation value indicating the difference between the phase value and the preset The difference of the phase threshold; the input power, the output power and the preset attenuation threshold are differenced to obtain the attenuation deviation value, the attenuation deviation value represents the difference between the signal attenuation monitored by the resource flow sensor and the preset attenuation threshold; it is determined whether the phase deviation value and the attenuation deviation value are not greater than 0, if the phase deviation value and the attenuation deviation value are not greater than 0, then the corresponding phase deviation value and the attenuation deviation value are recorded as 0, otherwise the obtained phase deviation value and the attenuation deviation value are not changed; the preset phase deviation threshold is obtained from the preset database, and the abnormal situation of phase monitoring is obtained in combination with the phase deviation value; the preset attenuation deviation threshold is obtained from the preset database, and the abnormal situation of attenuation monitoring is obtained in combination with the attenuation deviation value; the obtained abnormal situation of phase monitoring and the abnormal situation of attenuation monitoring are summed up to obtain the distortion assessment index.

[0068] The distortion assessment index is calculated using the following formula:

[0069] ;

[0070] ;

[0071] ;

[0072] In the formula, represents the sensor abnormality index of the resource flow sensor monitored in the mth sub-area during the rth preset time period, , r represents the number of the preset time period, n represents the total number of preset time periods, , m represents the number of the sub-region, s represents the total number of sub-regions, represents the phase deviation value of the resource flow sensor monitored in the mth sub-area during the rth preset time period, represents the attenuation deviation value of the resource flow sensor monitored in the mth sub-area during the rth preset time period, represents the phase value of the resource flow sensor monitored in the mth sub-area during the rth preset time period, represents the input power of the resource flow sensor monitored in the mth sub-area during the rth preset time period, represents the output power of the resource flow sensor monitored in the mth sub-area during the rth preset time period, Indicates the preset phase threshold, Indicates the preset attenuation threshold, Indicates the preset phase deviation threshold, represents the preset attenuation deviation threshold, and e represents a natural constant.

[0073] In this embodiment, the preset phase threshold is represented by the average value of the natural resource monitoring phase data of the historical period in the preset database, the preset attenuation threshold is represented by the average value of the natural resource monitoring attenuation data of the historical period in the preset database, the preset phase deviation threshold is represented by the variance value of the natural resource monitoring phase data of the historical period in the preset database, and the preset attenuation deviation threshold is represented by the variance value of the natural resource monitoring attenuation data of the historical period in the preset database;

[0074] The algorithm of this embodiment combines the transmission distortion data and comprehensively analyzes to obtain the distortion evaluation index. In this formula, there is a common adjustment association between the transmission distortion data. When the phase deviation value and the attenuation deviation value are greater than 0, for example, the distortion of the phase value may cause instability of the input power and the output power, because the phase deviation may cause a nonlinear response of the signal, thereby affecting the power transmission efficiency. In addition, the instability of the input power may also cause the phase distortion of the output signal, because the change in power may affect the stability and quality of the signal. Phase distortion may lead to inaccurate data collection, and the imbalance between the input power and the output power may cause the efficiency of the sensor or monitoring equipment to decrease. When the input power increases to the preset maximum input power value, the system may enter the saturation zone. At this time, the output power no longer increases linearly with the input power, and may even decrease, thereby affecting the quality and integrity of the data. As the phase value and input power increase, the output power decreases, and the distortion evaluation index increases, which means that the phase value and input power are proportional to the distortion evaluation index, and the output power is inversely proportional to the distortion evaluation index; more accurate intelligent monitoring of natural resources is achieved.

[0075] Furthermore, the specific process of determining whether to perform sensor distortion adjustment based on the distortion assessment index and the preset distortion threshold range obtained from the preset database is as follows: W1, determining whether the distortion assessment index is within the preset distortion threshold range. When the distortion assessment index is within the preset distortion threshold range, no sensor distortion adjustment is performed, otherwise W2 is executed; W2, performing denoising processing. When the monitored distortion assessment index is within the preset distortion threshold range, the execution is stopped. Otherwise, W3 is executed. The denoising processing is used to improve the accuracy of natural resource output; W3, obtaining the preset gain threshold and preset multiple from the preset database, sending a prompt to the system to gradually increase the gain of the resource flow sensor by the preset multiple of the preset gain threshold. When the monitored distortion assessment index is within the preset distortion threshold range, the execution is stopped. Otherwise, feedback is given to the preset personnel to perform system maintenance and redeploy the sensor.

[0076] In this embodiment, a preset distortion threshold and a preset multiple are obtained from a preset database. The preset distortion threshold is represented by the average value of the error code data transmitted by the natural resource sensor in the historical period. 0.5 times the preset distortion threshold is used as the lower limit of the preset distortion threshold range, and 1.5 times the preset distortion threshold is used as the upper limit of the preset distortion threshold range. The preset gain threshold is represented by the average value of the gain data of the natural resource sensor in the historical period. De-noising is used to reduce the noise in the monitoring data, and the signal is smoothed by filtering out high-frequency noise, impulse noise, and salt and pepper noise components to improve the accuracy of the data. By gradually increasing the gain of the resource flow sensor by a preset multiple of the preset gain threshold, the sensitivity of the sensor can be improved, thereby improving the quality of the monitoring data; more accurate intelligent monitoring of natural resources is achieved.

[0077] like Figure 3As shown, it is a structural schematic diagram of a natural resource intelligent monitoring system provided by an embodiment of the present application. The natural resource intelligent monitoring system provided by an embodiment of the present application includes: a sub-region data acquisition module, a sub-region index acquisition module, a monitoring anomaly adjustment module and a sensor distortion adjustment module; wherein the sub-region data acquisition module is used to divide the natural resource monitoring area into sub-regions, and obtain sensor network layout data and output monitoring data through preset sensor measurement devices deployed in the sub-regions, the sensor network layout data represents the specification information of the deployed preset sensor measurement devices, and the output monitoring data is used to describe the natural resource output monitored by the preset sensor measurement devices; the sub-region index acquisition module is used to obtain reference resource anomaly data from a preset database and obtain a resource transmission anomaly index in combination with the obtained output monitoring data, obtain reference sensor anomaly data from a preset database and obtain a sensor anomaly index in combination with the obtained sensor network layout data, and the resource transmission anomaly index is obtained. The anomaly index is used to evaluate the degree of anomaly of natural resource output in the sub-area, and the sensor anomaly index is used to evaluate the monitoring anomaly of the sensor in the sub-area; the monitoring anomaly adjustment module is used to obtain the preset transmission threshold range and the preset sensor threshold range from the preset database and determine whether to perform monitoring anomaly adjustment in combination with the resource transmission anomaly index and the sensor anomaly index. The monitoring anomaly adjustment is used to adjust the resource transmission anomaly index to within the preset transmission threshold range and adjust the sensor anomaly index to within the preset sensor threshold range at the same time; the sensor distortion adjustment module is used to obtain the transmission distortion data of the preset sensor measurement device in the sub-area and obtain the distortion evaluation index in combination with the reference distortion data obtained from the preset library. Based on the distortion evaluation index and the preset distortion threshold range obtained from the preset database, it is determined whether to perform sensor distortion adjustment. The distortion evaluation index is used to evaluate the distortion situation monitored by the preset sensor measurement device. The sensor distortion adjustment is used to adjust the distortion evaluation index to within the preset distortion threshold range.

[0078] In this embodiment, through the collaborative work of the sub-region data acquisition module, the sub-region index acquisition module, the monitoring anomaly adjustment module and the sensor distortion adjustment module, efficient management of the natural resource monitoring area is achieved, and more accurate intelligent monitoring of natural resources is achieved.

[0079] To summarize, the embodiment of the present application obtains sensor network deployment data and output monitoring data, and then combines the resource abnormality data and the output monitoring data to obtain the resource transmission abnormality index, and then combines the reference sensor abnormality data and the sensor network deployment data to obtain the sensor abnormality index, and finally combines the transmission distortion data and the reference distortion data to obtain the distortion assessment index and perform sensor distortion adjustment, thereby achieving the improvement of the real-time performance of natural resource intelligent monitoring, and further achieving more accurate natural resource intelligent monitoring, effectively solving the problem of inaccurate data in the natural resource intelligent monitoring process in the prior art.

[0080] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0082] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0083] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0084] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0085] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A natural resource intelligent monitoring method, characterized in that: The following steps are involved: S1, divide the natural resource monitoring area into sub-areas, and obtain sensor network deployment data and output monitoring data through preset sensor measurement devices deployed in the sub-areas; S2, obtaining reference resource anomaly data from a preset database and combining it with the obtained output monitoring data to obtain a resource transmission anomaly index, obtaining reference sensor anomaly data from a preset database and combining it with the obtained sensor network deployment data to obtain a sensor anomaly index, the resource transmission anomaly index is used to evaluate the degree of anomaly of natural resource output in the sub-area, and the sensor anomaly index is used to evaluate the monitoring anomaly of sensors in the sub-area; S3, obtaining a preset transmission threshold range and a preset sensing threshold range from a preset database and determining whether to perform monitoring abnormality adjustment in combination with a resource transmission abnormality index and a sensing abnormality index; S4, obtaining transmission distortion data of a preset sensor measurement device in the sub-area and combining it with reference distortion data obtained from a preset database to obtain a distortion evaluation index, and judging whether to perform sensor distortion adjustment based on the distortion evaluation index and a preset distortion threshold range obtained from the preset database, wherein the distortion evaluation index is used to evaluate the distortion condition monitored by the preset sensor measurement device; The specific process of obtaining the reference resource anomaly data from the preset database and combining the obtained output monitoring data to obtain the resource transmission anomaly index is as follows: Acquire reference resource anomaly data from a preset database, wherein the reference resource anomaly data includes a preset maximum propagation time threshold, a preset data transmission rate threshold, and a preset output rate threshold; Combining the reference resource abnormal data and the output monitoring data, the transmission delay deviation, the transmission rate deviation and the resource output rate deviation are obtained; Determine whether the transmission delay deviation, transmission rate deviation and resource output rate deviation are not greater than 0. If the transmission delay deviation, transmission rate deviation and resource output rate deviation are not greater than 0, then the corresponding transmission delay deviation, transmission rate deviation and resource output rate deviation are recorded as 0. Otherwise, the obtained transmission delay deviation, transmission rate deviation and resource output rate deviation are not changed. Obtain resource transmission allocation weights from a preset database and obtain a resource transmission anomaly index in combination with corresponding transmission delay deviation, transmission rate deviation, and resource output rate deviation, wherein the resource transmission allocation weights include a first resource transmission allocation weight, a second resource transmission allocation weight, and a third resource transmission allocation weight; The resource transmission anomaly index is calculated using the following formula: ； ； ； ； In the formula, represents the resource transmission anomaly index of the natural resources monitored in the mth sub-area during the rth preset time period, , r represents the number of the preset time period, n represents the total number of preset time periods, , m represents the number of the sub-region, s represents the total number of sub-regions, The transmission delay deviation of the natural resources monitored in the mth sub-area during the rth preset time period, represents the transmission rate deviation of the natural resources monitored in the mth sub-area in the rth preset time period, represents the resource output rate deviation of the natural resources monitored in the mth sub-area during the rth preset time period, represents the signal propagation distance of the natural resources monitored in the mth sub-area during the rth preset time period, represents the signal propagation speed of the natural resources monitored in the mth sub-area during the rth preset time period, represents the bandwidth of the natural resources monitored in the mth sub-area during the rth preset time period, represents the signal-to-noise ratio of the natural resources monitored in the mth sub-area during the rth preset time period, represents the resource output of natural resources monitored in the mth sub-area during the rth preset time period, represents the output time of the natural resources monitored in the mth sub-area during the rth preset time period, represents the preset maximum propagation time threshold, Indicates the preset data transmission rate threshold. Indicates the preset output rate threshold, represents the first allocation weight of resource transmission, represents the second allocation weight of resource transmission, represents the third allocation weight of resource transmission, and e represents a natural constant; The specific process of obtaining the reference sensor abnormality data from the preset database and combining the obtained sensor network layout data to obtain the sensor abnormality index is as follows: Acquire reference sensor abnormality data from a preset database, wherein the reference sensor abnormality data includes a preset power consumption threshold, a preset electric power threshold, a preset power consumption deviation threshold, and a preset electric power deviation threshold; Acquire a power consumption deviation value and an electric power deviation value by combining the sensor network deployment data and the reference sensor abnormal data, wherein the power consumption deviation value represents the difference between the power consumption of the resource flow sensor monitored in the preset time period of the sub-area and the preset power consumption threshold, and the electric power deviation value represents the difference between the preset electric power threshold and the electric power of the resource flow sensor monitored in the preset time period of the sub-area; Determine whether the power consumption deviation value and the electric power deviation value are not greater than 0. If the power consumption deviation value and the electric power deviation value are not greater than 0, the corresponding power consumption deviation value and the electric power deviation value are recorded as 0. Otherwise, the obtained power consumption deviation value and the electric power deviation value are not changed. Acquire a preset power consumption deviation threshold, a preset electric power deviation threshold, a first sensing allocation weight, and a second sensing allocation weight from a preset database; Obtaining an abnormal situation of power consumption monitoring through a power consumption deviation value, a preset power consumption deviation threshold and a first sensing allocation weight; Obtaining abnormal conditions of power monitoring through an electric power deviation value, a preset electric power deviation threshold, and a second sensor allocation weight; The acquired abnormal conditions of power consumption monitoring and power monitoring are combined and summed to obtain a sensor abnormality index; The sensor abnormality index is calculated using the following formula: ； ； ； In the formula, represents the sensor abnormality index of the resource flow sensor monitored in the mth sub-area during the rth preset time period, , r represents the number of the preset time period, n represents the total number of preset time periods, , m represents the number of the sub-region, s represents the total number of sub-regions, represents the power consumption deviation value of the resource flow sensor monitored in the mth sub-area in the rth preset time period, represents the electric power deviation value of the resource flow sensor monitored in the mth sub-area in the rth preset time period, represents the sensor voltage of the resource flow sensor monitored in the mth sub-area during the rth preset time period, represents the sensing current of the resource flow sensor monitored in the mth sub-area during the rth preset time period, represents the sensor resistance of the resource flow sensor monitored in the mth sub-area during the rth preset time period, Indicates the preset power consumption threshold. Indicates the preset electric power threshold, Indicates the preset power consumption deviation threshold. represents the preset electric power deviation threshold, and e represents a natural constant; The specific process of obtaining the transmission distortion data of the preset sensor measurement device in the sub-area and combining it with the reference distortion data obtained from the preset library to obtain the distortion evaluation index is as follows: Acquiring transmission distortion data of a resource flow sensor by using a preset distortion measurement device in a preset time period, wherein the transmission distortion data includes a phase value, input power, and output power; Acquire reference distortion data from a preset database, wherein the reference distortion data includes a preset phase threshold, a preset attenuation threshold, a preset phase deviation threshold, and a preset attenuation deviation threshold; Performing a difference operation on the phase value and the preset phase threshold to obtain a phase deviation value, wherein the phase deviation value represents the difference between the phase value and the preset phase threshold; Performing a difference operation on the input power, the output power and the preset attenuation threshold to obtain an attenuation deviation value, wherein the attenuation deviation value represents the difference between the signal attenuation monitored by the resource flow sensor and the preset attenuation threshold value; Determine whether the phase deviation value and the attenuation deviation value are not greater than 0. If the phase deviation value and the attenuation deviation value are not greater than 0, the corresponding phase deviation value and the attenuation deviation value are recorded as 0. Otherwise, the obtained phase deviation value and the attenuation deviation value are not changed. Obtaining a preset phase deviation threshold from a preset database, and obtaining an abnormal condition of phase monitoring in combination with the phase deviation value; Obtaining a preset attenuation deviation threshold from a preset database, and obtaining an abnormal condition of attenuation monitoring in combination with the attenuation deviation value; The obtained abnormal conditions of phase monitoring and abnormal conditions of attenuation monitoring are combined and summed to obtain a distortion assessment index; The distortion evaluation index is calculated using the following formula: ； ； ； In the formula, represents the sensor abnormality index of the resource flow sensor monitored in the mth sub-area during the rth preset time period, , r represents the number of the preset time period, n represents the total number of preset time periods, , m represents the number of the sub-region, s represents the total number of sub-regions, represents the phase deviation value of the resource flow sensor monitored in the mth sub-area during the rth preset time period, represents the attenuation deviation value of the resource flow sensor monitored in the mth sub-area during the rth preset time period, represents the phase value of the resource flow sensor monitored in the mth sub-area during the rth preset time period, represents the input power of the resource flow sensor monitored in the mth sub-area during the rth preset time period, represents the output power of the resource flow sensor monitored in the mth sub-area during the rth preset time period, Indicates the preset phase threshold, Indicates the preset attenuation threshold, Indicates the preset phase deviation threshold, represents the preset attenuation deviation threshold, and e represents a natural constant.

2. A natural resource intelligent monitoring method as claimed in claim 1, characterized in that: The preset sensing and measuring device includes a resource flow sensor, a voltmeter, an ammeter, a timer, a bandwidth tester, a signal-to-noise ratio tester, a multimeter, a time domain reflectometer, an electromagnetic wave rangefinder and a network performance tester; The output monitoring data includes bandwidth, signal-to-noise ratio, signal propagation distance, signal propagation speed, output time and resource output; The sensor network layout data includes sensor voltage, sensor resistance and sensor current; The sensor voltage represents the voltage of the resource flow sensor monitored in a preset time period of the sub-area; The sensor resistance represents the resistance of the resource flow sensor monitored during a preset time period in the sub-area; The sensing current represents the current of the resource flow sensor monitored in a preset time period of the sub-area; The output time represents the total output duration of the natural resources monitored in the preset time period of the sub-area; The resource output represents the total output of natural resources monitored in a preset time period of the sub-area.

3. A natural resource intelligent monitoring method as claimed in claim 1, characterized in that: The specific process of obtaining the preset transmission threshold range and the preset sensing threshold range from the preset database and performing monitoring abnormality adjustment in combination with the resource transmission abnormality index and the sensing abnormality index is as follows: Q1, determine whether the resource transmission anomaly index is within the preset transmission threshold range. If the resource transmission anomaly index is within the preset transmission threshold range, no monitoring anomaly adjustment is performed, otherwise Q2 is executed; Q2, sending a reminder to add a preset relay device type to the preset personnel, and stopping the execution when the monitored resource transmission anomaly index is within the preset transmission threshold range, otherwise executing Q3; Q3, sends a reminder to the preset personnel to add the preset signal enhancement device type. When the monitored resource transmission anomaly index is within the preset transmission threshold range, the execution stops. Otherwise, feedback is given to the preset personnel to replace and redeploy the sensor.

4. A natural resource intelligent monitoring method as claimed in claim 1, characterized in that: The monitoring abnormal adjustment also includes: P1, determine whether the sensor anomaly index is within the preset sensor threshold range. If the resource transmission anomaly index is within the preset sensor threshold range, no monitoring anomaly adjustment is performed. Otherwise, P2 is executed; P2, obtains the sensor sampling frequency threshold and preset multiple from the preset database, sends a prompt to the system to gradually reduce the sampling frequency of the resource flow sensor by the preset multiple of the sensor sampling frequency threshold. When the monitored resource transmission anomaly index is within the preset sensor threshold range, stop executing, otherwise execute P3; P3, obtains the preset resolution threshold and preset multiple from the preset database, sends a prompt to the system to gradually reduce the resolution of the resource flow sensor by the preset multiple of the preset resolution threshold. When the monitored resource transmission anomaly index is within the preset sensor threshold range, the execution is stopped. Otherwise, feedback is given to the preset personnel for sensor replacement and redeployment.

5. A natural resource intelligent monitoring method as claimed in claim 1, characterized in that: The specific process of determining whether to perform sensor distortion adjustment based on the distortion evaluation index and the preset distortion threshold range obtained from the preset database is as follows: W1, judging whether the distortion evaluation index is within the preset distortion threshold range. When the distortion evaluation index is within the preset distortion threshold range, no sensor distortion adjustment is performed, otherwise W2 is executed; W2, perform denoising, and stop executing when the monitored distortion evaluation index is within the preset distortion threshold range, otherwise execute W3, the denoising is used to improve the accuracy of natural resource output; W3, obtains the preset gain threshold and preset multiple from the preset database, sends a prompt to the system to gradually increase the gain of the resource flow sensor by the preset multiple of the preset gain threshold, and stops execution when the monitored distortion assessment index is within the preset distortion threshold range. Otherwise, it feeds back to the preset personnel to perform system maintenance and redeploy the sensor.

6. A natural resource intelligent monitoring system, characterized in that: include: Sub-region data acquisition module, sub-region index acquisition module, monitoring anomaly adjustment module and sensor distortion adjustment module; Wherein, the sub-region data acquisition module is used to divide the natural resource monitoring area to obtain sub-regions, and obtain sensor network deployment data and output monitoring data through preset sensor measurement devices deployed in the sub-regions; The sub-region index acquisition module is used to obtain reference resource anomaly data from a preset database and combine it with the obtained output monitoring data to obtain a resource transmission anomaly index, obtain reference sensor anomaly data from a preset database and combine it with the obtained sensor network deployment data to obtain a sensor anomaly index, the resource transmission anomaly index is used to evaluate the degree of anomaly of natural resource output in the sub-region, and the sensor anomaly index is used to evaluate the monitoring anomaly of sensors in the sub-region; The monitoring anomaly adjustment module is used to obtain a preset transmission threshold range and a preset sensing threshold range from a preset database and determine whether to perform monitoring anomaly adjustment in combination with a resource transmission anomaly index and a sensing anomaly index; The sensor distortion adjustment module is used to obtain transmission distortion data of a preset sensor measurement device in the sub-area and obtain a distortion evaluation index in combination with reference distortion data obtained from a preset database, and determine whether to perform sensor distortion adjustment based on the distortion evaluation index and a preset distortion threshold range obtained from the preset database, wherein the distortion evaluation index is used to evaluate the distortion condition monitored by the preset sensor measurement device; The specific process of obtaining the reference resource anomaly data from the preset database and combining the obtained output monitoring data to obtain the resource transmission anomaly index is as follows: Acquire reference resource anomaly data from a preset database, wherein the reference resource anomaly data includes a preset maximum propagation time threshold, a preset data transmission rate threshold, and a preset output rate threshold; Combining the reference resource abnormal data and the output monitoring data, the transmission delay deviation, the transmission rate deviation and the resource output rate deviation are obtained; Determine whether the transmission delay deviation, transmission rate deviation and resource output rate deviation are not greater than 0. If the transmission delay deviation, transmission rate deviation and resource output rate deviation are not greater than 0, then the corresponding transmission delay deviation, transmission rate deviation and resource output rate deviation are recorded as 0. Otherwise, the obtained transmission delay deviation, transmission rate deviation and resource output rate deviation are not changed. Obtain resource transmission allocation weights from a preset database and obtain a resource transmission anomaly index in combination with corresponding transmission delay deviation, transmission rate deviation, and resource output rate deviation, wherein the resource transmission allocation weights include a first resource transmission allocation weight, a second resource transmission allocation weight, and a third resource transmission allocation weight; The resource transmission anomaly index is calculated using the following formula: ； ； ； ； In the formula, represents the resource transmission anomaly index of the natural resources monitored in the mth sub-area during the rth preset time period, , r represents the number of the preset time period, n represents the total number of preset time periods, , m represents the number of the sub-region, s represents the total number of sub-regions, The transmission delay deviation of the natural resources monitored in the mth sub-area during the rth preset time period, represents the transmission rate deviation of the natural resources monitored in the mth sub-area in the rth preset time period, represents the resource output rate deviation of the natural resources monitored in the mth sub-area during the rth preset time period, represents the signal propagation distance of the natural resources monitored in the mth sub-area during the rth preset time period, represents the signal propagation speed of the natural resources monitored in the mth sub-area during the rth preset time period, represents the bandwidth of the natural resources monitored in the mth sub-area during the rth preset time period, represents the signal-to-noise ratio of the natural resources monitored in the mth sub-area during the rth preset time period, represents the resource output of natural resources monitored in the mth sub-area during the rth preset time period, represents the output time of the natural resources monitored in the mth sub-area during the rth preset time period, represents the preset maximum propagation time threshold, Indicates the preset data transmission rate threshold. Indicates the preset output rate threshold, represents the first allocation weight of resource transmission, represents the second allocation weight of resource transmission, represents the third allocation weight of resource transmission, and e represents a natural constant; The specific process of obtaining the reference sensor abnormality data from the preset database and combining the obtained sensor network layout data to obtain the sensor abnormality index is as follows: Acquire reference sensor abnormality data from a preset database, wherein the reference sensor abnormality data includes a preset power consumption threshold, a preset electric power threshold, a preset power consumption deviation threshold, and a preset electric power deviation threshold; Acquire a power consumption deviation value and an electric power deviation value by combining the sensor network deployment data and the reference sensor abnormal data, wherein the power consumption deviation value represents the difference between the power consumption of the resource flow sensor monitored in the preset time period of the sub-area and the preset power consumption threshold, and the electric power deviation value represents the difference between the preset electric power threshold and the electric power of the resource flow sensor monitored in the preset time period of the sub-area; Determine whether the power consumption deviation value and the electric power deviation value are not greater than 0. If the power consumption deviation value and the electric power deviation value are not greater than 0, the corresponding power consumption deviation value and the electric power deviation value are recorded as 0. Otherwise, the obtained power consumption deviation value and the electric power deviation value are not changed. Acquire a preset power consumption deviation threshold, a preset electric power deviation threshold, a first sensing allocation weight, and a second sensing allocation weight from a preset database; Obtaining an abnormal situation of power consumption monitoring through a power consumption deviation value, a preset power consumption deviation threshold and a first sensing allocation weight; Obtaining abnormal conditions of power monitoring through an electric power deviation value, a preset electric power deviation threshold, and a second sensor allocation weight; The acquired abnormal conditions of power consumption monitoring and power monitoring are combined and summed to obtain a sensor abnormality index; The sensor abnormality index is calculated using the following formula: ； ； ； In the formula, represents the sensor abnormality index of the resource flow sensor monitored in the mth sub-area during the rth preset time period, , r represents the number of the preset time period, n represents the total number of preset time periods, , m represents the number of the sub-region, s represents the total number of sub-regions, represents the power consumption deviation value of the resource flow sensor monitored in the mth sub-area in the rth preset time period, represents the electric power deviation value of the resource flow sensor monitored in the mth sub-area in the rth preset time period, represents the sensor voltage of the resource flow sensor monitored in the mth sub-area during the rth preset time period, represents the sensing current of the resource flow sensor monitored in the mth sub-area during the rth preset time period, represents the sensor resistance of the resource flow sensor monitored in the mth sub-area during the rth preset time period, Indicates the preset power consumption threshold. Indicates the preset electric power threshold, Indicates the preset power consumption deviation threshold. represents the preset electric power deviation threshold, and e represents a natural constant; The specific process of obtaining the transmission distortion data of the preset sensor measurement device in the sub-area and combining it with the reference distortion data obtained from the preset library to obtain the distortion evaluation index is as follows: Acquiring transmission distortion data of a resource flow sensor by using a preset distortion measurement device in a preset time period, wherein the transmission distortion data includes a phase value, input power, and output power; Acquire reference distortion data from a preset database, wherein the reference distortion data includes a preset phase threshold, a preset attenuation threshold, a preset phase deviation threshold, and a preset attenuation deviation threshold; Performing a difference operation on the phase value and the preset phase threshold to obtain a phase deviation value, wherein the phase deviation value represents the difference between the phase value and the preset phase threshold; Performing a difference operation on the input power, the output power and the preset attenuation threshold to obtain an attenuation deviation value, wherein the attenuation deviation value represents the difference between the signal attenuation monitored by the resource flow sensor and the preset attenuation threshold value; Determine whether the phase deviation value and the attenuation deviation value are not greater than 0. If the phase deviation value and the attenuation deviation value are not greater than 0, the corresponding phase deviation value and the attenuation deviation value are recorded as 0. Otherwise, the obtained phase deviation value and the attenuation deviation value are not changed. Obtaining a preset phase deviation threshold from a preset database, and obtaining an abnormal condition of phase monitoring in combination with the phase deviation value; Obtaining a preset attenuation deviation threshold from a preset database, and obtaining an abnormal condition of attenuation monitoring in combination with the attenuation deviation value; The obtained abnormal conditions of phase monitoring and abnormal conditions of attenuation monitoring are combined and summed to obtain a distortion assessment index; The distortion evaluation index is calculated using the following formula: ； ； ； In the formula, represents the sensor abnormality index of the resource flow sensor monitored in the mth sub-area during the rth preset time period, , r represents the number of the preset time period, n represents the total number of preset time periods, , m represents the number of the sub-region, s represents the total number of sub-regions, represents the phase deviation value of the resource flow sensor monitored in the mth sub-area during the rth preset time period, represents the attenuation deviation value of the resource flow sensor monitored in the mth sub-area during the rth preset time period, represents the phase value of the resource flow sensor monitored in the mth sub-area during the rth preset time period, represents the input power of the resource flow sensor monitored in the mth sub-area during the rth preset time period, represents the output power of the resource flow sensor monitored in the mth sub-area during the rth preset time period, Indicates the preset phase threshold, Indicates the preset attenuation threshold, Indicates the preset phase deviation threshold, represents the preset attenuation deviation threshold, and e represents a natural constant.

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