CN114862641A - Ecological environment monitoring and management system and method based on block chain - Google Patents
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Abstract
The invention discloses an ecological environment monitoring and management system and method based on a block chain, which comprises the following steps: an ecological environment perception module, a data management center, a monitoring data acquisition module, a monitoring data analysis module and an environment monitoring optimization module, setting an environment monitoring point through an ecological environment perception module, utilizing the monitoring point to monitor the soil in a monitoring range in real time, the monitored soil data is stored in the block chain link points through the data management center, the soil data and the monitoring point data stored in the nodes are collected through the monitoring data acquisition module, soil data are retrieved through a monitoring data analysis module, the soil data analysis reference illumination stored in each node is judged, the soil data collection efficiency is predicted, the soil monitoring point position is adjusted through the environment monitoring optimization module, the data collection efficiency before and after the monitoring point is adjusted is compared, the optimal monitoring point is selected, the block chain utility maximization is realized, and the whole monitoring range is enlarged while invalid repeated data is reduced.
Description
Technical Field
The invention relates to the technical field of environmental monitoring, in particular to an ecological environment monitoring and managing system and method based on a block chain.
Background
The ecological environment monitoring refers to monitoring, determining and monitoring various mark data reflecting the environmental quality and the change trend thereof by using modern scientific and technological means such as chemistry, physics and the like, is essential basic work for environmental protection, comprises air quality monitoring, soil monitoring, water quality monitoring and the like, and is helpful for describing other potential risks of soil abnormal conditions on the health of the surrounding environment, animals and human beings;
the existing soil monitoring management working mode has the following problems: firstly, monitoring soil in different areas by setting monitoring points, storing monitored data into block chain link points, wherein each block chain link point can acquire all monitoring data, the same monitoring data exists, the data are analyzed without reference, and the data cannot be compared and analyzed to realize the maximum effectiveness of a block chain, so that the analysis efficiency of the monitoring data is influenced; secondly, data stored by the nodes are required to be integrated and analyzed, so that the environment monitoring range cannot be expanded while invalid data are reduced and data analysis comprehension is realized; finally, the data have abnormal conditions, the reasons for the same abnormal conditions are possibly different, the soil abnormal data caused by different reasons have reference values, and the data are easy to ignore and have deviation when invalid data are reduced, so that the accuracy of the analysis result of the monitoring data is influenced.
Therefore, there is a need for a block chain-based ecological environment monitoring and management system and method to solve the above problems.
Disclosure of Invention
The present invention provides a block chain-based ecological environment monitoring and management system and method, so as to solve the problems in the background art.
In order to solve the technical problems, the invention provides the following technical scheme: the utility model provides an ecological environment monitoring management system based on block chain which characterized in that: the system comprises: the system comprises an ecological environment perception module, a data management center, a monitoring data acquisition module, a monitoring data analysis module and an environment monitoring optimization module;
setting an environment monitoring point through the ecological environment sensing module, and monitoring soil in a monitoring range in real time by using the monitoring point;
storing the soil data monitored by all monitoring points into block link points through the data management center;
collecting soil data stored in each block chain node, positions of monitoring points and effective monitoring range data through the monitoring data collection module;
the monitoring data analysis module is used for calling soil data, judging the soil data analysis illumination parameters stored in each node and predicting the soil data collection efficiency;
and adjusting the position of a soil monitoring point through the environment monitoring optimization module, comparing the soil data collection efficiency before and after the monitoring point is adjusted, and selecting an optimal monitoring point.
Furthermore, the ecological environment sensing module comprises a soil temperature sensing unit, a soil humidity sensing unit and a pH value detection unit, wherein the soil temperature sensing unit monitors the soil temperature in real time by using a temperature sensor; the soil humidity sensing unit monitors the soil humidity in real time by using a humidity sensor; the pH value detection unit monitors the pH value of the soil by using a pH value sensing probe and transmits real-time monitoring data to the data management center;
the monitoring data acquisition module comprises a node data acquisition unit and a monitoring point information acquisition unit, soil data monitored by all set monitoring points stored by the data management center are acquired through the node data acquisition unit, the data monitored by different monitoring points are stored on different block chain nodes, and each monitoring point is provided with a temperature sensor, a humidity sensor and a pH value sensing probe; and acquiring the position information of the monitoring point and the range data of the monitoring area through the monitoring point information acquisition unit.
Furthermore, the monitoring data analysis module comprises a data association analysis unit, an information effective prediction unit and a data abnormity analysis unit, the soil data collected by the monitoring data collection module is called by the data association analysis unit, and the analysis reference intensity of the soil data monitored by the monitoring point is analyzed; predicting the data collection efficiency of the currently set block chain node through the information effective prediction unit; and analyzing the soil data through the data abnormity analysis unit, inquiring whether the soil abnormity reasons monitored by the monitoring points are the same or not, obtaining an effective prediction error parameter of the information, and adjusting the data collection efficiency.
Furthermore, the environment monitoring optimization module comprises a node selection adjusting unit and a processing efficiency testing unit, the position of the monitoring point is adjusted and selected through the node selection adjusting unit, the temperature, the humidity and the PH value of the soil are monitored by the reselected monitoring point, and the monitored data are transmitted to the processing efficiency testing unit; and selecting an optimal monitoring point to monitor the soil by comparing the data collection efficiency of the processing efficiency testing unit before and after the adjustment of the testing monitoring point.
An ecological environment monitoring and management method based on a block chain is characterized in that: the method comprises the following steps:
s1: setting an environment monitoring point, utilizing the monitoring point to monitor soil in real time, and storing data monitored by the currently set monitoring point to different block chain nodes;
s2: collecting soil data stored in the block chain nodes, and judging the soil data analysis illumination parameters stored in each node;
s3: predicting the soil data collection efficiency of the currently set block chain node;
s4: acquiring soil abnormal data, inquiring the reason of soil data abnormality to obtain a predicted result deviation parameter, and adjusting the predicted result;
s5: and adjusting the position of the monitoring point, and testing the data collection efficiency of the monitoring point before and after adjustment.
Further, in steps S1-S2: setting n soil monitoring points at random, monitoring soil data in a monitoring range of the monitoring points by using a temperature sensor, a humidity sensor and a pH value sensing probe, storing the monitored data into block chain link points, and acquiring the soil data stored in the block chain nodes: during the time period t1 to t 2: the set of the times of acquiring the completely same soil data monitored by two adjacent monitoring points is M = { M = } 1 ，M 2 ，…，M n-1 N is larger than or equal to 2, when two adjacent monitoring points monitor the same soil data randomly, the difference set of the soil area areas corresponding to the two same data is s = { s1, s2, …, sk }, wherein k = M i And k represents random two adjacent guardiansThe times that the soil data monitored by the measuring points are completely the same are calculated according to the following formula, and the data contact ratio F monitored by two adjacent monitoring points is calculated i ：
Wherein M is i The method comprises the steps of representing the number of times that soil data monitored by two adjacent monitoring points are completely the same, representing the difference between the areas of soil areas corresponding to two same data when the two adjacent monitoring points monitor the same soil data at one time, representing the difference between the temperature, the humidity and the pH value of the soil monitored by the two adjacent monitoring points when the two adjacent monitoring points monitor the same soil data at one time, storing the data into nodes, adding the area data corresponding to the same data when analyzing the data overlap ratio, wherein the smaller the area difference is, the higher the possibility of data repetition is shown to be, and calculating the data overlap ratio is beneficial to judging the referability of the data monitored by the monitoring points.
Further, during the time period t1 to t 2: the set of completely different times of soil data monitored by two adjacent monitoring points is collected to be A = { A = } 1 ，A 2 ，…，A n-1 When completely different soil data are monitored corresponding to two adjacent monitoring points, the difference set of the soil area areas corresponding to the two completely different data is S = { S1, S2, …, SI }, wherein I = A = i I represents the times of completely different soil data monitored corresponding to two adjacent monitoring points, and the data difference degree E monitored by two adjacent monitoring points is calculated according to the following formula i ：
A i Representing the times of monitoring the soil data monitored by two adjacent monitoring points at random which are completely different, and Si representing the soil area corresponding to the two completely different data when the adjacent monitoring points monitor the completely different soil data at random onceAnd obtaining soil data analysis parameters corresponding to two adjacent monitoring points according to the difference of the area of the areas, wherein the analysis parameters are as follows: w i =E i -F i And obtaining a soil data analysis reference degree set of adjacent monitoring points as W = { W = 1 ，W 2 ，…，W n-1 And completely different data indicate that the soil temperature, the soil humidity and the soil pH value of two adjacent monitoring points are different, the purpose of analyzing the data difference degree is to obtain the analysis reference illumination of the soil data monitored by the adjacent monitoring points, the larger the difference degree is, the smaller the contact ratio is, the higher the reference value of the monitored data is, the higher the data of the integrated reference value is stored and collected, the block chain utility maximization is favorably realized, and the obtained data are more comprehensive.
Further, in steps S3-S4: the data volumes monitored by two adjacent monitoring points randomly in the time period from t1 to t2 are collected as B1 and B2 respectively according to the formula
Predicting the soil data collection efficiency Q of the block chain node storing the data monitored by two random adjacent monitoring points i And in the time period from t1 to t2, the frequency of monitoring the same soil abnormal data corresponding to adjacent monitoring points is N, and the data abnormal reason is inquired: inquiring that the abnormal soil monitoring corresponding to the adjacent monitoring points is abnormal, the abnormal data is the same, the times of different abnormal reasons are L, and obtaining a prediction result deviation parameter: L/N, adjusting the prediction result: obtaining the soil data collection efficiency Q of the block chain node for storing the data monitored by two adjacent monitoring points after adjustment i ’ ：
And the adjusted soil data collection efficiency set is Q ’ ={ Q 1 ’ ， Q 2 ’ ，…， Q n-1 ’ Sieving out the powder smaller than
The adjacent monitoring points corresponding to the data collection efficiency and the selected monitoring points are adjustedThe data quantity monitored by the monitoring points in the same time period is different, the data collection efficiency is analyzed through the data quantity and the data reference value, the proper monitoring point position is selected and adjusted, the invalid data stored in the block link points are reduced, the whole monitoring range is expanded, the same soil abnormal data caused by different reasons exist, although the data are the same, the reference value exists, the data are compared and analyzed, the speed of inquiring the abnormal reason is accelerated, the deviation data is added, and the accuracy of monitoring point screening is improved.
Further, in step S5: when the adjacent monitoring points screened out monitor the same data, confirming the soil area corresponding to the same data, and adjusting the position of the monitoring point: selecting a random monitoring point from two adjacent monitoring points, moving the position of the monitoring point until the soil area corresponding to the same monitored data is not in the monitoring range of the monitoring point, monitoring the soil after adjustment, and obtaining the data collection efficiency q corresponding to the adjacent monitoring point after adjustment i Test data collection efficiency: comparison q i And Q i ’’ Wherein Q is i ’’ Representing the soil data collection efficiency of the block chain nodes for storing the data monitored by the two randomly screened adjacent monitoring points: if it is
If the data collection efficiency after adjustment is higher than the data collection efficiency before adjustment, selecting the monitoring point after adjustment as the optimal monitoring point; if it is
And if the adjusted data collection efficiency is not higher than the data collection efficiency before adjustment, selecting the monitoring point before adjustment as the optimal monitoring point, monitoring the soil, selecting and adjusting the position of the monitoring point for the selected monitoring point, so that repeated data stored in the link points of the block can be reduced, the whole soil monitoring range can be effectively expanded, and the monitoring efficiency can be improved.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, the soil is monitored by the temperature sensor, the humidity sensor and the pH value sensing probe, the monitoring points are set, and the data monitored by the monitoring points are stored in different block chain nodes, so that the monitoring data can be shared, and the data analysis efficiency is improved; analyzing the referential of the stored data, namely the reference value, by analyzing the contact ratio and the difference of the stored data on the blockchain nodes through the big data, storing and collecting the data with high integrated reference value, which can not be contrasted and analyzed among the completely same data and has no reference value, thereby realizing the maximization of the utility of the blockchain and obtaining more comprehensive data; abnormal data exists in the completely same data, the abnormal data caused by different reasons still has referential property, and the data are eliminated, so that the accuracy of screening the monitoring point results of which the positions need to be adjusted is improved; and one monitoring point is selected to adjust the monitoring position between the adjacent monitoring points, so that the repeated data without reference is reduced, and the whole monitoring range is expanded.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a block chain-based ecological environment monitoring and management system;
fig. 2 is a step diagram of an ecological environment monitoring and management method based on a block chain according to the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Referring to fig. 1-2, the present invention provides a technical solution: the utility model provides an ecological environment monitoring management system based on block chain which characterized in that: the system comprises: the system comprises an ecological environment perception module, a data management center, a monitoring data acquisition module, a monitoring data analysis module and an environment monitoring optimization module;
setting an environment monitoring point through an ecological environment perception module, and monitoring soil in a monitoring range in real time by using the monitoring point;
storing soil data monitored by all monitoring points into block link points through a data management center;
collecting soil data stored in each block chain node, positions of monitoring points and effective monitoring range data through a monitoring data collection module;
the soil data are called through a monitoring data analysis module, the soil data analysis illumination parameters stored in each node are judged, and the soil data collection efficiency is predicted;
and adjusting the position of the soil monitoring point through an environment monitoring optimization module, comparing the soil data collection efficiency before and after the monitoring point is adjusted, and selecting the optimal monitoring point.
The ecological environment sensing module comprises a soil temperature sensing unit, a soil humidity sensing unit and a pH value detection unit, wherein the soil temperature sensing unit monitors the soil temperature in real time by using a temperature sensor; the soil humidity sensing unit monitors the soil humidity in real time by using a humidity sensor; the pH value detection unit monitors the pH value of the soil by using a pH value sensing probe and transmits real-time monitoring data to the data management center;
the monitoring data acquisition module comprises a node data acquisition unit and a monitoring point information acquisition unit, soil data monitored by all set monitoring points stored by the data management center is acquired through the node data acquisition unit, the data monitored by different monitoring points are stored on different block chain nodes, and each monitoring point is provided with a temperature sensor, a humidity sensor and a pH value sensing probe; and acquiring the position information of the monitoring points and the range data of the monitoring area through the monitoring point information acquisition unit.
The monitoring data analysis module comprises a data association analysis unit, an information effective prediction unit and a data abnormity analysis unit, and the data association analysis unit is used for calling the soil data collected by the monitoring data collection module and analyzing the analysis reference intensity of the soil data monitored by the monitoring point; predicting the data collection efficiency of the currently set block chain node through an information effective prediction unit; and analyzing the soil data through the data anomaly analysis unit, inquiring whether the soil anomaly reasons monitored by the monitoring points are the same or not, obtaining effective prediction error parameters of the information, and adjusting the data collection efficiency.
The environment monitoring optimization module comprises a node selection adjusting unit and a processing efficiency testing unit, the position of a monitoring point is adjusted and selected through the node selection adjusting unit, the temperature, the humidity and the PH value of soil are monitored by the reselected monitoring point, and the monitored data are transmitted to the processing efficiency testing unit; and comparing the data collection efficiency before and after the adjustment of the test monitoring points by the processing efficiency test unit, and selecting an optimal monitoring point to monitor the soil.
An ecological environment monitoring and management method based on a block chain is characterized in that: the method comprises the following steps:
s1: setting an environment monitoring point, utilizing the monitoring point to monitor soil in real time, and storing data monitored by the currently set monitoring point to different block chain nodes;
s2: collecting soil data stored in the block chain nodes, and judging the soil data analysis illumination parameters stored in each node;
s3: predicting the soil data collection efficiency of the currently set block chain node;
s4: acquiring soil abnormal data, inquiring the reason of soil data abnormality to obtain a predicted result deviation parameter, and adjusting the predicted result;
s5: and adjusting the position of the monitoring point, and testing the data collection efficiency of the monitoring point before and after adjustment.
In steps S1-S2: setting n soil monitoring points at random, monitoring soil data in a monitoring range of the monitoring points by using a temperature sensor, a humidity sensor and a pH value sensing probe, storing the monitored data into block chain link points, and acquiring the soil data stored in the block chain nodes: during the time period t1 to t 2: the set of the times of acquiring the completely same soil data monitored by two adjacent monitoring points is M = { M = } 1 ，M 2 ，…，M n-1 N is more than or equal to 2, and when the same soil data is monitored at two adjacent monitoring points randomly, the two same dataThe difference set of the corresponding soil region areas is s = { s1, s2, …, sk }, where k = M i K represents the number of times that the soil data monitored by two adjacent monitoring points are completely the same, and the contact ratio F of the data monitored by the two adjacent monitoring points is calculated according to the following formula i ：
Wherein M is i The number of times that the soil data monitored by two adjacent monitoring points are completely the same is shown, si shows that when the same soil data is monitored by two adjacent monitoring points at random once, the difference of the soil area corresponding to the two same data is added when the data overlap ratio is analyzed, and the accuracy of data analysis is improved.
During the time period t1 to t 2: the set of completely different times of soil data monitored by two adjacent monitoring points is collected to be A = { A = } 1 ，A 2 ，…，A n-1 When completely different soil data are monitored corresponding to two adjacent monitoring points, the difference set of the soil area areas corresponding to the two completely different data is S = { S1, S2, …, SI }, wherein I = A = i I represents the times of completely different soil data monitored corresponding to two adjacent monitoring points, and the data difference degree E monitored by two adjacent monitoring points is calculated according to the following formula i ：
A i The number of times that the soil data monitored by two adjacent monitoring points are completely different is shown at random, and when Si shows that the soil data monitored by the two adjacent monitoring points are completely different at one time at random, the difference of the areas of the soil areas corresponding to the two completely different data is obtained, and the analysis reference illumination of the soil data corresponding to the two adjacent monitoring points is as follows: w i =E i -F i And obtaining a soil data analysis reference degree set of adjacent monitoring points as W = { W = 1 ，W 2 ，…，W n-1 And storing and collecting data with high integration reference value, so that the maximization of the utility of the block chain is realized, and the obtained data is more comprehensive.
In steps S3-S4: the data volumes monitored by two adjacent monitoring points randomly in the time period from t1 to t2 are collected as B1 and B2 respectively according to the formula
Predicting the soil data collection efficiency Q of the block chain node storing the data monitored by two random adjacent monitoring points i And in the time period from t1 to t2, the frequency of monitoring the same soil abnormal data corresponding to adjacent monitoring points is N, and the data abnormal reason is inquired: inquiring that the abnormal soil monitoring corresponding to the adjacent monitoring points is abnormal, the abnormal data is the same, the times of different abnormal reasons are L, and obtaining a prediction result deviation parameter: L/N, adjusting the prediction result: obtaining the soil data collection efficiency Q of the block chain node for storing the data monitored by two adjacent monitoring points after adjustment i ’ ：
Obtaining an adjusted soil data collection efficiency set of Q ’ ={ Q 1 ’ ， Q 2 ’ ，…， Q n-1 ’ Sieving out the powder smaller than
The adjacent monitoring points corresponding to the data collection efficiency are adjusted, the positions of the screened monitoring points are adjusted, and deviation data are added, so that the screening accuracy of the monitoring points is improved.
In step S5: when the adjacent monitoring points screened out monitor the same data, confirming the soil area corresponding to the same data, and adjusting the position of the monitoring point: selecting a random monitoring point from two adjacent monitoring points, moving the position of the monitoring point until the soil area corresponding to the same monitored data is not in the monitoring range of the monitoring point, monitoring the soil after adjustment, and obtaining the data collection efficiency q corresponding to the adjacent monitoring point after adjustment i Test data collection efficiency: comparison q i And Q i ’’ Wherein Q is i ’’ Representing the soil data collection efficiency of the block chain nodes for storing the data monitored by the two randomly screened adjacent monitoring points: if it is
If the adjusted data collection efficiency is higher than the data collection efficiency before adjustment, selecting the adjusted monitoring point as the optimal monitoring point; if it is
And if the adjusted data collection efficiency is not higher than the data collection efficiency before adjustment, selecting the monitoring point before adjustment as the optimal monitoring point to monitor the soil, so that the overall monitoring range is expanded while repeated and non-reference data stored on the block chain nodes are reduced.
The first embodiment is as follows: randomly setting n =3 soil monitoring points, monitoring soil data in a monitoring point monitoring range by using a temperature sensor, a humidity sensor and a PH value sensing probe, and at t1= 8: 00 to t2= 8: within 20 time periods: the set of the times of acquiring the completely same soil data monitored by two adjacent monitoring points is M = { M = } 1 ，M 2 And when the same soil data is monitored at the second monitoring point and the third monitoring point, the difference set of the soil area areas corresponding to the two same data is s = { s1, s2, s3} = {10, 5, 20}, and the soil area areas corresponding to the two same data are calculated according to a formula
Calculating the data contact ratio F monitored by corresponding adjacent monitoring points i Is approximately equal to 0.54, and the completely different times of the soil data monitored by the adjacent monitoring points are collected to be A = { A = { (A) } 1 ，A 2 That = {3, 5}, and that when completely different soil data is monitored at the second and third monitoring points, the set of differences between the soil area areas corresponding to the two completely different data sets is S = { S1, S2, S3, S4, S5} = {2, 12, 6, 8, 22}, according to the formula
Calculating the data difference degree E monitored by corresponding adjacent monitoring points i And the soil data are approximately equal to 0.77, and the analysis reference illumination corresponding to two adjacent monitoring points is obtained as follows: w i =E i -F i =0.23, and the data volume collected in the time period from t1 to t2 and monitored at two adjacent monitoring points randomly is respectively B1=16 and B2=64 according to the formula
Predicting the soil data collection efficiency Q of the block chain node storing the data monitored by two random adjacent monitoring points i =0.92, the number of times that the same soil abnormal data is monitored corresponding to the adjacent monitoring points is N =5, and the reason of the data abnormality is inquired: inquiring that the abnormal soil monitoring corresponding to the adjacent monitoring points is abnormal, the abnormal data is the same, the times of different abnormal reasons are L =2, and obtaining a prediction result deviation parameter: L/N =0.4, adjust prediction: obtaining the soil data collection efficiency Q of the block chain node which stores the data monitored by two adjacent monitoring points after adjustment i ’ ：
=2.52, the adjusted soil data collection efficiency set is Q ’ ={ Q 1 ’ ， Q 2 ’ }={5，2.52}，
，
The second and third monitoring point locations need to be adjusted.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described above, or equivalents may be substituted for elements thereof. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. The utility model provides an ecological environment monitoring management system based on block chain which characterized in that: the system comprises: the system comprises an ecological environment perception module, a data management center, a monitoring data acquisition module, a monitoring data analysis module and an environment monitoring optimization module;
setting an environment monitoring point through the ecological environment sensing module, and monitoring soil in a monitoring range in real time by using the monitoring point;
storing the soil data monitored by all monitoring points into block link points through the data management center;
collecting soil data stored in each block chain node, positions of monitoring points and effective monitoring range data through the monitoring data collection module;
the monitoring data analysis module is used for calling soil data, judging the soil data analysis illumination parameters stored in each node and predicting the soil data collection efficiency;
and adjusting the position of a soil monitoring point through the environment monitoring optimization module, comparing the soil data collection efficiency before and after the monitoring point is adjusted, and selecting an optimal monitoring point.
2. The ecological environment monitoring and management system based on the block chain as claimed in claim 1, wherein: the ecological environment sensing module comprises a soil temperature sensing unit, a soil humidity sensing unit and a pH value detection unit, wherein the soil temperature sensing unit monitors the soil temperature in real time by using a temperature sensor; the soil humidity sensing unit monitors the soil humidity in real time by using a humidity sensor; the pH value detection unit monitors the pH value of the soil by using a pH value sensing probe and transmits real-time monitoring data to the data management center;
the monitoring data acquisition module comprises a node data acquisition unit and a monitoring point information acquisition unit, soil data monitored by all set monitoring points stored by the data management center are acquired through the node data acquisition unit, the data monitored by different monitoring points are stored on different block chain nodes, and each monitoring point is provided with a temperature sensor, a humidity sensor and a pH value sensing probe; and acquiring the position information of the monitoring point and the range data of the monitoring area through the monitoring point information acquisition unit.
3. The ecological environment monitoring and management system based on the block chain as claimed in claim 1, wherein: the monitoring data analysis module comprises a data association analysis unit, an information effective prediction unit and a data abnormity analysis unit, and the data association analysis unit is used for calling the soil data collected by the monitoring data collection module and analyzing the analysis parameter of the soil data monitored by the monitoring point; predicting the data collection efficiency of the currently set block chain node through the information effective prediction unit; and analyzing the soil data through the data abnormity analysis unit, inquiring whether the soil abnormity reasons monitored by the monitoring points are the same or not, obtaining an effective prediction error parameter of the information, and adjusting the data collection efficiency.
4. The ecological environment monitoring and management system based on the block chain as claimed in claim 1, wherein: the environment monitoring optimization module comprises a node selection adjusting unit and a processing efficiency testing unit, the position of a monitoring point is adjusted and selected through the node selection adjusting unit, the temperature, the humidity and the PH value of soil are monitored by the reselected monitoring point, and the monitored data are transmitted to the processing efficiency testing unit; and comparing the data collection efficiency before and after the adjustment of the test monitoring points by the processing efficiency test unit, and selecting an optimal monitoring point to monitor the soil.
5. An ecological environment monitoring and management method based on a block chain is characterized in that: the method comprises the following steps:
s1: setting an environment monitoring point, utilizing the monitoring point to monitor soil in real time, and storing data monitored by the currently set monitoring point to different block chain nodes;
s2: collecting soil data stored in the block chain nodes, and judging the soil data analysis illumination parameters stored in each node;
s3: predicting the soil data collection efficiency of the currently set block chain node;
s4: acquiring soil abnormal data, inquiring the reason of soil data abnormality to obtain a predicted result deviation parameter, and adjusting the predicted result;
s5: and adjusting the position of the monitoring point, and testing the data collection efficiency of the monitoring point before and after adjustment.
6. The ecological environment monitoring and management method based on the block chain as claimed in claim 5, wherein: in steps S1-S2: setting n soil monitoring points at random, monitoring soil data in a monitoring range of the monitoring points by using a temperature sensor, a humidity sensor and a pH value sensing probe, storing the monitored data into block chain link points, and acquiring the soil data stored in the block chain nodes: during the time period t1 to t 2: the set of the times of acquiring the completely same soil data monitored by two adjacent monitoring points is M = { M = } 1 ，M 2 ，…，M n-1 N is larger than or equal to 2, when two adjacent monitoring points monitor the same soil data randomly, the difference set of the soil area areas corresponding to the two same data is s = { s1, s2, …, sk }, wherein k = M i K represents the number of times that the soil data monitored by two adjacent monitoring points are completely the same, and the contact ratio F of the data monitored by the two adjacent monitoring points is calculated according to the following formula i ：
Wherein M is i The number of times that the soil data monitored by two adjacent monitoring points are completely the same is shown, and si shows the difference of the areas of the soil areas corresponding to two same data when the same soil data are monitored by two adjacent monitoring points at one time.
7. The ecological environment monitoring and management method based on the block chain as claimed in claim 6, wherein: during the time period t1 to t 2: the set of completely different times of soil data monitored by two adjacent monitoring points is collected to be A = { A = } 1 ，A 2 ，…，A n-1 When completely different soil data are monitored corresponding to two adjacent monitoring points, the difference set of the soil area areas corresponding to the two completely different data is S = { S1, S2, …, SI }, wherein I = A = i I represents the times of completely different soil data monitored corresponding to two adjacent monitoring points, and the data difference degree E monitored by two adjacent monitoring points is calculated according to the following formula i ：
A i The number of times that the soil data monitored by two adjacent monitoring points are completely different at random is represented, Si represents the difference of the areas of the soil areas corresponding to two completely different data when the soil data monitored by the two adjacent monitoring points at one time at random is corresponding to the two adjacent monitoring points, and the obtained soil data analysis reference value corresponding to the two adjacent monitoring points is as follows: w i =E i -F i And obtaining a soil data analysis reference degree set of adjacent monitoring points as W = { W = 1 ，W 2 ，…，W n-1 }。
8. The ecological environment monitoring and management method based on the block chain as claimed in claim 7, wherein: in steps S3-S4: the data volumes monitored by two adjacent monitoring points randomly in the time period from t1 to t2 are collected as B1 and B2 respectively according to the formula
Predicting the soil data collection efficiency Q of the block chain node storing the data monitored by two random adjacent monitoring points i When the same soil difference is monitored corresponding to adjacent monitoring points within the time period from t1 to t2The frequency of the constant data is N, and the abnormal reason of the data is inquired: inquiring that the abnormal soil monitoring corresponding to the adjacent monitoring points is abnormal, the abnormal data is the same, the times of different abnormal reasons are L, and obtaining a prediction result deviation parameter: L/N, adjusting the prediction result: obtaining the soil data collection efficiency Q of the block chain node for storing the data monitored by two adjacent monitoring points after adjustment i ’ ：
Obtaining an adjusted soil data collection efficiency set of Q ’ ={ Q 1 ’ ， Q 2 ’ ，…， Q n-1 ’ Sieving out the powder smaller than
Adjusting the position of the screened monitoring point corresponding to the adjacent monitoring point corresponding to the data collection efficiency.
9. The ecological environment monitoring and management method based on the block chain as claimed in claim 5, wherein: in step S5: when the adjacent monitoring points screened out monitor the same data, confirming the soil area corresponding to the same data, and adjusting the position of the monitoring point: selecting a random monitoring point from two adjacent monitoring points, moving the position of the monitoring point until the soil area corresponding to the same monitored data is not in the monitoring range of the monitoring point, monitoring the soil after adjustment, and obtaining the data collection efficiency q corresponding to the adjacent monitoring point after adjustment i Test data collection efficiency: comparison q i And Q i ’’ Wherein, Q i ’’ Representing the soil data collection efficiency of the block chain nodes for storing the data monitored by the two randomly screened adjacent monitoring points: if it is
And explaining that the data collection efficiency after adjustment is higher than that before adjustment, and selecting the monitoring point after adjustment as the optimal monitoring pointMeasuring points; if it is
And if the data collection efficiency after adjustment is not higher than the data collection efficiency before adjustment, selecting the monitoring point before adjustment as the optimal monitoring point to monitor the soil.
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