CN116754738A - Water area carbon dynamic monitoring method, equipment, robot and computer storage medium - Google Patents

Water area carbon dynamic monitoring method, equipment, robot and computer storage medium Download PDF

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CN116754738A
CN116754738A CN202311030269.XA CN202311030269A CN116754738A CN 116754738 A CN116754738 A CN 116754738A CN 202311030269 A CN202311030269 A CN 202311030269A CN 116754738 A CN116754738 A CN 116754738A
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carbon
grid
environmental information
weight
water
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CN116754738B (en
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王爱杰
肖鹏
许铁夫
陶彧
张聪超
吴景瑞
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Harbin Institute Of Technology shenzhen Shenzhen Institute Of Science And Technology Innovation Harbin Institute Of Technology
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Abstract

The application discloses a method, equipment, a robot and a computer storage medium for dynamically monitoring carbon in a water area, and belongs to the technical field of ecological environment. Dividing a target water area into grid cells, wherein the grid cells are three-dimensional grid cells; adjusting each of the grid cells based on first environmental information of the grid cell, wherein the first environmental information includes one or more indicators strongly related to carbon circulation of the target water area; and quantitatively calculating the carbon dynamic quantity of the target water area based on the adjusted second environmental information of each grid unit, wherein the second environmental information at least comprises dissolved organic carbon concentration, granular organic carbon concentration, dissolved inorganic carbon concentration, total carbon concentration, sediment organic carbon content and sediment carbon deposition rate, and the first environmental information and the second environmental information are obtained through robot sampling, so that the accuracy of dynamically monitoring the carbon of the water area is improved.

Description

Water area carbon dynamic monitoring method, equipment, robot and computer storage medium
Technical Field
The application relates to the technical field of ecological environment, in particular to a method, equipment, a robot and a computer storage medium for dynamically monitoring carbon in a water area.
Background
At present, technologies for dynamically monitoring carbon in a water area mainly comprise a manual measurement mode, a fixed-point monitoring mode, a remote sensing technology mode and the like. The manual measurement mode requires professional personnel to sample and monitor in the field, and the cost of consumption is high and inefficiency, receives the environmental impact simultaneously and leads to the error, can't realize stable monitoring. Although the fixed point monitoring mode can realize long-term stable monitoring, only the fixed point can be sampled, data related to carbon dynamic in water cannot be comprehensively collected, and errors of monitoring data still exist. The remote sensing technology can acquire data in a large range and a long term, but can only acquire data of the surface layer of the water area, and can only evaluate the overall carbon dynamic quantity of the water area by using the data of the partial area of the water area, so that the carbon dynamic of the whole water area cannot be accurately monitored and analyzed. Therefore, the dynamic monitoring of carbon in a water area has the problem of low accuracy.
Disclosure of Invention
The application mainly aims to provide a water area carbon dynamic monitoring method, equipment, a robot and a computer storage medium, which aim at solving the technical problem of how to improve the accuracy of water area carbon dynamic monitoring.
In order to achieve the above purpose, the application provides a method for dynamically monitoring carbon in a water area, which comprises the following steps:
dividing a target water area into grid cells, wherein the grid cells are three-dimensional grid cells;
adjusting each of the grid cells based on first environmental information of the grid cell, wherein the first environmental information includes one or more indicators strongly related to carbon circulation of the target water area;
and quantitatively calculating the carbon dynamic quantity of the target water area based on the adjusted second environmental information of each grid unit, wherein the second environmental information at least comprises dissolved organic carbon concentration, granular organic carbon concentration, dissolved inorganic carbon concentration, total carbon concentration, sediment organic carbon content and sediment carbon deposition rate, and the first environmental information and the second environmental information are obtained through robot sampling.
Optionally, the step of adjusting the grid cells based on the first environmental information of each grid cell includes:
calculating a first weight of each grid cell according to the first environment information;
and adjusting the grid unit according to the first weight.
Optionally, the step of calculating the first weight of each grid cell according to the first environment information includes:
Calculating the information entropy of each index in the target water area according to the first environmental information of all the grid units;
calculating a second weight corresponding to each index according to the information entropy of each index;
and calculating the first weight of the grid unit according to the first environment information of the grid unit and the second weight corresponding to each index.
Optionally, the step of adjusting the grid cell according to the first weight includes:
sorting all the first weights, and screening from the sorted first weights according to preset conditions to obtain a first weight threshold and a second weight threshold, wherein the first weight threshold is used for dividing the grid cells, the second weight threshold is used for merging the grid cells, and the first weight threshold is larger than the second weight threshold;
if the first weight of the grid cell is greater than the first weight threshold, dividing the grid cell into a plurality of new grid cells;
if the first weight of the grid cell is smaller than the second weight threshold, the grid cell is integrated into a target grid cell, wherein the target grid cell is adjacent to the grid cell;
Calculating the number of the grid cells, and if the number is greater than a preset number threshold, executing: the step of adjusting the grid cells according to the first weights.
Optionally, after the step of calculating the number of grid cells, the method further comprises:
traversing the adjusted grid cells if the number is less than or equal to a preset number threshold, and executing if the first weight of at least one grid cell is greater than the first weight threshold or less than the second weight threshold: the step of adjusting the grid cells according to the first weights.
Optionally, the step of quantitatively calculating the carbon dynamic quantity of the target water area based on the adjusted second environmental information of each grid cell includes:
calculating the carbon vertical flux, the carbon horizontal flux and the carbon storage amount of the target water area according to the second environmental information;
calculating the carbon flux of the target water area according to the carbon vertical flux and the carbon horizontal flux;
and calculating the carbon dynamic quantity of the target water area according to the carbon flux and the carbon storage quantity.
Optionally, the first environmental information includes at least one or more of temperature, salinity, dissolved oxygen concentration, ph, turbidity, dissolved organic carbon concentration, particulate organic carbon concentration, dissolved inorganic carbon concentration, total carbon concentration, sediment organic carbon content, and sediment carbon deposition rate.
The application also provides a robot, which comprises a body, a joint, a sensor module, a water sample acquisition module and a sediment acquisition module, wherein,
the sensor module, the water sample acquisition module and the sediment acquisition module are used for acquiring first environmental information and second environmental information;
the sensor module comprises a mounting plate, a protective shell and a plurality of sensors, each sensor is fixed on the mounting plate, the mounting plate and the sensors are arranged in an inner cavity of the protective shell, and the sensor module is connected with the front outer wall of the machine body through the connector;
the water sample collection module comprises a multichannel collector, each channel of the multichannel collector comprises a spring driving device and a valve, the spring driving device is used for controlling the opening and closing of the valve, and the water sample collection module is connected with the side outer wall of the machine body through the connector;
the sediment collection module comprises a sediment collector and a packaging device, a plurality of tubular columns are arranged in an inner cavity of the packaging device, one end of the sediment collector is of a conical structure, the other end of the sediment collector is axially connected with the packaging device, and the sediment collection module is connected with the outer wall of the bottom side of the machine body through a connector.
The application also provides a water area carbon dynamic monitoring device, which is entity equipment, and comprises: the method comprises a memory, a processor and a program of the dynamic monitoring method of the water carbon, wherein the program of the dynamic monitoring method of the water carbon is stored in the memory and can run on the processor, and the steps of the dynamic monitoring method of the water carbon can be realized when the program of the dynamic monitoring method of the water carbon is executed by the processor.
The application also provides a computer storage medium, wherein the computer storage medium stores a program for realizing the water area carbon dynamic monitoring method, and the program for realizing the water area carbon dynamic monitoring method is executed by a processor to realize the steps of the water area carbon dynamic monitoring method.
The application provides a method, equipment, a robot and a computer storage medium for dynamically monitoring carbon in a water area, wherein a target water area to be detected is divided into a plurality of three-dimensional grid units. And the carbon dynamic data of the water surface layer can be detected through the three-dimensional grid positioned on the water surface layer, so that the carbon dynamic monitoring can be more comprehensively carried out compared with the remote sensing technology. Compared with a manual collection mode, the method reduces the interference of instability factors caused by environmental changes. And then, based on the acquired data, the grid unit is adjusted again, the accuracy of data acquisition is further improved, and finally, the carbon dynamic quantity of the target water area is obtained through quantitative calculation of the acquired carbon dynamic data of each grid.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the technical solutions of the present embodiment or the prior art, the drawings used in the description of the embodiment or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a flow chart of a first embodiment of the present application;
FIG. 2 is an overall flow chart of the present application for adjusting grid cells;
FIG. 3 is a schematic diagram of the workflow of the method for dynamically monitoring carbon in a water area according to the present application;
fig. 4 is a block diagram of a robot according to the present application;
fig. 5 is a schematic diagram of a hardware structure of the water carbon dynamic monitoring device according to the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
In order to make the above objects, features and advantages of the present application more comprehensible, the following description of the embodiments accompanied with the accompanying drawings will be given in detail. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
At present, technologies for dynamically monitoring carbon in a water area mainly comprise a manual measurement mode, a fixed-point monitoring mode, a remote sensing technology mode and the like. The manual measurement mode requires professional personnel to sample and monitor in the field, and the cost of consumption is high and inefficiency, receives the environmental impact simultaneously and leads to the error, can't realize stable monitoring. Although stable monitoring can be realized by the fixed-point monitoring mode, only the fixed point can be sampled, data related to carbon dynamic in the water can not be comprehensively collected, and errors of monitoring data still exist. The remote sensing technology can acquire data in a large range and a long term, but can only acquire data of the surface layer of the water area, and can only evaluate the overall carbon dynamic quantity of the water area by using the data of the partial area of the water area, so that the carbon dynamic of the whole water area cannot be accurately monitored and analyzed. Therefore, the prior art has the problem of low accuracy in dynamically monitoring carbon in a water area.
The application provides a method, equipment, a robot and a computer storage medium for dynamically monitoring carbon in a water area, wherein a target water area to be detected is divided into a plurality of three-dimensional grid units. And the carbon dynamic data of the water surface layer can be detected through the three-dimensional grid positioned on the water surface layer, so that the carbon dynamic monitoring can be more comprehensively carried out compared with the remote sensing technology. Compared with a manual collection mode, the method reduces the interference of instability factors caused by environmental changes. And then, based on the acquired data, the grid units are adjusted again, the accuracy of data acquisition is further improved, and finally, the carbon dynamic quantity of the target water area is obtained through quantitative calculation of the acquired carbon dynamic data of each grid. Meanwhile, the application considers a plurality of indexes reflecting the dynamic characteristics of carbon when adjusting the grid, calculates the carbon dynamic quantity based on multidimensional data of the second environmental information, and can reduce the monitoring error by increasing the variety number of the evaluation indexes, thereby improving the accuracy of the dynamic monitoring of the carbon in the water area.
Referring to fig. 1, the present application provides a method for dynamically monitoring carbon in a water area, in a first embodiment of the method for dynamically monitoring carbon in a water area, the method comprises the following steps:
step S100, dividing a target water area into grid cells, wherein the grid cells are three-dimensional grid cells;
in this embodiment, the grid unit is a three-dimensional grid unit, as shown in fig. 2, firstly, the target water area is grid-divided to obtain an initial grid unit, so as to obtain carbon dynamic data in the three-dimensional grid, when the area of the water area to be monitored is large, the target water area can be a segment of a river or an area of a lake, when the area of the water area is small, the target water area can be a whole river or a lake, the grid unit is a part of a water body in the water area geographically, the grid unit can be square or cuboid, and the size specification of the grid unit can be 40m by 20m, which is set by a person skilled in the art by himself, then, the grid unit can be initialized and divided according to the map information of the target water area.
Step S200, adjusting the grid cells based on first environmental information of each grid cell, wherein the first environmental information includes one or more indexes strongly related to carbon circulation of the target water area;
According to the robot provided by the application, referring to fig. 2, a tour path is required to be planned before carbon dynamic data is acquired, and when a certain lake aquatic weed ecosystem is required to conduct carbon dynamic monitoring, the planned tour path is required to cover a region with dense aquatic weed distribution, and factors such as the speed, the power consumption, the sampling frequency and the monitoring range of a carried sensor of the robot are required to be considered, so that the robot can complete as many sampling tasks as possible within limited time and electric quantity. For example, if the aquatic weed distribution is known, a genetic algorithm or simulated annealing algorithm may be used to determine the optimal tour path so that the robot collects the most data for a limited amount of time and power. If the water grass distribution condition is unknown, an autonomous planning algorithm based on deep learning can be used, and the robot is self-adaptive and optimizes the tour path by continuously learning the environment. After the tour path is planned, the robot can tour the grid cells according to the tour path, and the first environmental information of each grid cell is acquired and obtained.
In this embodiment, the first environmental information includes at least one or more of temperature, salinity, dissolved oxygen concentration, PH (Potential of Hydrogen, PH value), turbidity, DOC (Dissolved Organic Carbon ) concentration, POC (Particulate Organic Carbon, particulate Organic Carbon) concentration, DIC (Dissolved Inorganic Carbon ) concentration, TC (Total Carbon) concentration, sediment OC (Organic Carbon) content, sediment Carbon deposition rate, and SPM (Suspended Particulate Matter, particulate suspended matter) content or water depth, and the like, and may be a comprehensive selection of various indexes to adjust the grid unit. The first environmental information includes one or more indicators that are strongly related to the carbon circulation of the target water area, and the selection of the indicators may be determined based on year-round monitoring of the water area. Illustratively, the temperature of the water body has a significant effect on the carbon circulation process, and can affect the concentration of dissolved oxygen, biological activity and the like in the water body; salinity reflects water density and hydrologic characteristics, and can influence vertical and horizontal transportation of carbon; the dissolved oxygen concentration reflects the underwater biological activity and the oxygen exchange condition and is closely related to the carbon circulation; the pH affects the distribution of DIC by affecting the balance of the carbonate system; turbidity may reflect the content of particulate matter, a parameter related to the distribution of POC; DOC is organic carbon in a water body in a dissolved state, and is an important component of carbon circulation; POC is organic carbon existing in a granular state in a water body and mainly derived from plant residues, microorganisms and algae; DIC is an inorganic carbon in the water body that exists in a dissolved state, mainly comprising bicarbonate ions, carbonate ions, and carbon dioxide; TC is the sum of all forms of carbon in the water body, including organic and inorganic carbon; the OC content of the sediment can represent the long-term deposition and storage conditions of carbon; the sediment carbon deposition rate can characterize the loss of carbon during the deposition process. The mesh unit adjustment may be to redetermine the size specification of the divided mesh unit according to the obtained first environmental information, or to perform the dividing and merging operation on the mesh, referring to fig. 2, calculate the weight of the mesh unit according to the collected first environmental information, and adjust the resolution (the size specification of the mesh unit) according to the weight, so as to obtain a multi-resolution mesh (the mesh unit with various sizes specification) after adjustment.
And step S300, quantitatively calculating the carbon dynamic quantity of the target water area based on the adjusted second environmental information of each grid unit, wherein the second environmental information at least comprises dissolved organic carbon concentration, granular organic carbon concentration, dissolved inorganic carbon concentration, total carbon concentration, sediment organic carbon content and sediment carbon deposition rate, and the first environmental information and the second environmental information are obtained through robot sampling.
In this embodiment, the second environmental information may include two parts, one part is the DOC concentration, POC concentration, DIC concentration and TC concentration of the water body collected from each grid unit, and the other part is the OC content and carbon deposition rate of the bottom mud collected from each grid unit, and it is understood that some grid units may not contain the bottom mud, and thus, only the second environmental information of some grid units includes the OC content and carbon deposition rate. The first environmental information and the second environmental information are obtained by sampling each grid cell of the robot tour. For example, some index data may be directly collected by a robot and then uploaded to the cloud, such as temperature, salinity, dissolved oxygen concentration, PH, turbidity, while some index data may be obtained by collecting water samples and sediments by the robot and then uploading the data to the cloud, such as DOC concentration, POC concentration, DIC concentration, TC concentration, sediment OC content and sediment carbon deposition rate. The carbon dynamic quantity of the target water area reflects the total change of carbon in the target water area over a period of time, including the carbon storage amount and the carbon flux. The carbon storage amount in the target water area refers to the total amount of carbon in the target water area in a certain time period, and it is understood that the carbon dynamic amount can be regarded as a dynamic value of the carbon storage amount, and the carbon flux comprises a carbon horizontal flux and a carbon vertical flux, wherein the horizontal flux refers to the total amount of carbon passing through a certain horizontal area in a unit time, and is generally used for describing the speed of carbon in the water body passing through a river section, and the carbon vertical flux refers to the total amount of carbon passing through a certain vertical area in a unit time, and is generally used for describing the speed of organic carbon in bottom sludge passing through the bottom of the water body. Before the calculation of the carbon dynamic quantity, data needs to be acquired from the cloud, which usually has partial missing or noise-containing data, after the data is acquired, data preprocessing can be performed to clean out noise data, such as sensor detection parameters, water sample analysis parameters and sediment analysis parameters which are beyond the normal range, and the missing sensor detection parameters, water sample analysis parameters and sediment analysis parameters are filled in, wherein the filling method can be interpolation methods (such as linear interpolation, polynomial interpolation and the like), statistical methods (such as mean value, median and the like), and space interpolation methods can be used for filling partial grid cell missing data (such as Kriging interpolation, inverse distance weighted interpolation and the like). After the calculation is completed, the index data in the target water area can be visually displayed, and a thermodynamic diagram, a contour diagram and a stereogram can be drawn by using a Python library such as Matplotlib, seaborn or Plotly, so that the technical staff can conveniently inquire and manage the target water area.
Referring to fig. 3, the method for dynamically monitoring carbon in a water area according to the present embodiment sends all obtained data to a cloud end for storage through a target water area with a network of underwater robots, wherein a part of the data can be directly obtained through sensors carried by the robots, a part of the data can be obtained through samples collected by laboratory analysis robots, and after the cloud end data is subjected to data preprocessing, the carbon in the water area is dynamically evaluated (the carbon in the water area is dynamically monitored) through quantitative calculation. According to the embodiment, the target water area to be detected is divided into a plurality of three-dimensional grid units, and the target water area is divided into a plurality of sub-water areas, so that the data sample is more comprehensively covered, the sampling points are more uniformly distributed, the accuracy of data monitoring is improved, and compared with a fixed-point monitoring mode, the method can be used for more comprehensively and dynamically monitoring carbon. And the carbon dynamic data of the water surface layer can be detected through the three-dimensional grid positioned on the water surface layer, so that the carbon dynamic monitoring can be more comprehensively carried out compared with the remote sensing technology. Compared with a manual collection mode, the method reduces the interference of instability factors caused by environmental changes. And then, based on the acquired data, readjusting the grid units to further improve the accuracy of data acquisition, and finally, calculating the carbon dynamic quantity of the target water area according to the acquired carbon dynamic data of each grid. And when the calculated carbon dynamic quantity is calculated based on multidimensional data of the second environmental information, the high-precision quantitative calculation of the water area carbon dynamic quantity is realized by fitting each key index, so that the accuracy of monitoring the water area carbon dynamic is improved.
Based on the first embodiment of the present application, another embodiment of the present application is presented, and step S200, adjusting the grid cells based on the first environmental information of each grid cell, further includes:
step A10, calculating a first weight of each grid cell according to the first environment information;
and step A20, adjusting the grid unit according to the first weight.
In this embodiment, the first weight is the weight of the grid cell, and the grid cell may be adjusted by calculating the weight of each grid cell and then dividing or merging the grid cells according to the weight. The weight calculation method can be an index-based normalization processing method, a fuzzy weighted average method based on a fuzzy mathematical theory, or a multi-index decision algorithm based on an entropy weight method. In carbon dynamic monitoring, if a grid cell has a higher weight, it is represented that the grid cell has a higher carbon content or the change is complex, requiring more storage and computing resources to be devoted to the grid cell. If the weight of the grid cell is too high, the grid cell needs to be segmented, and if the weight of the grid cell is too low, the data representing these areas is more stable or less important, so that it is possible to avoid putting excessive storage and computing resources into the grid cell with lower weight by merging the grid cells.
In this embodiment, the weight of each grid cell is calculated through the multiple indexes of the first environmental information, so that the error of the analysis result is reduced, the storage and calculation resources are reasonably distributed to the areas with higher carbon content or frequent change, and the waste of the storage and calculation resources is reduced.
Further, step a10 includes:
step B10, calculating the information entropy of each index in the target water area according to a preset information entropy calculation formula, wherein the preset information entropy calculation formula is H (X) = - [ sigma ] (p (xi) ] log2p (xi) ] and p (xi) ] represents the frequency of occurrence of the ith numerical value of the index in all grid units according to the first environment information of all grid units;
step B20, calculating a second weight corresponding to each index according to a preset second weight calculation formula according to the information entropy of each index, wherein the preset weight calculation formula is W (Xi) =1-H (Xi)/log 2n, n represents the number of index types contained in the first environment information, and H (Xi) represents the information entropy of each index;
and step B30, calculating the first weight of the grid unit according to a preset first weight calculation formula, wherein the preset first weight calculation formula is W= Σ (Wixi), wi represents the weight of the ith index in the first environment information, and Xi represents the normalized value of the ith index according to the first environment information of the grid unit and the second weight corresponding to each index.
In the embodiment, the weight of the grid unit is calculated by adopting a multi-index decision algorithm based on an entropy weight method, and the algorithm has the advantages of simplicity, easiness in implementation, mathematical foundation and the like, and is widely applied to the field of hydrologic environments. First, the information entropy of each index is calculated, and the information entropy can reflect the uncertainty of the index and can be used for describing the importance relationship between the indexes. The preset information entropy calculation formula may be H (X) = - Σ (p (xi) log2p (xi)), where p (xi) represents the frequency of occurrence of the ith numerical value of the index in all grid cells, and it is understood that one index may have different values in different grids, and thus, in all grids, the index may have multiple numerical values. In this embodiment, the first weight is the weight of the grid unit, the second weight is the weight of the index, and the preset second weight calculation formula may be W (Xi) =1-H (Xi)/log 2n, where n represents the number of index types included in the first environment information, and H (Xi) represents the information entropy of the index calculated by the preset information entropy calculation formula. After the weight calculation of each class of index is completed, the index data in the target grid unit of the weight to be calculated can be normalized to obtain normalized values so as to eliminate the influence of dimensions and units, then each normalized value is multiplied by a corresponding second weight, and finally the sum value is calculated.
The embodiment adopts a multi-index decision algorithm based on an entropy weight method to calculate the weight of each grid cell, provides judgment conditions for adjusting the grid cells, and simultaneously calculates the weights of the grid cells by fusing a plurality of different indexes to consider the influence of multiple factors on the calculation of the dynamic value of the carbon.
Further, step a20 includes:
step C10, sorting all the first weights, and screening from the sorted first weights according to preset conditions to obtain a first weight threshold and a second weight threshold, wherein the first weight threshold is used for dividing the grid cells, the second weight threshold is used for merging the grid cells, and the first weight threshold is larger than the second weight threshold;
step C20, if the first weight of the grid cell is greater than the first weight threshold, dividing the grid cell into a plurality of new grid cells;
step C30, if the first weight of the grid cell is smaller than the second weight threshold, the grid cell is integrated into a target grid cell, wherein the target grid cell is adjacent to the grid cell;
step C40, calculating the number of the grid cells, and if the number is greater than a preset number threshold, executing: the step of adjusting the grid cells according to the first weights.
In this embodiment, the weights of all grid cells may be sorted from large to small, where the preset condition is that a first weight threshold is calculated according to a first preset percentage, and a second weight threshold is calculated according to a second preset percentage, for example, if the first preset percentage is 30%, the weight ranked 30% may be used as the first weight threshold, and if the second preset percentage is 50%, the weight ranked 50% inverse may be used as the second weight threshold, so that the first weight threshold is greater than the second weight threshold. The shape of the new grid cell formed by dividing the grid cell may be a cube or a cuboid, and the weights thereof need to be recalculated respectively. The target grid unit and the grid unit to be combined are in a physically adjacent relationship, when the grid units are combined, the target grid combination with the lowest weight can be selected preferentially, and if a plurality of target grids with the same weight and the lowest weight exist, one target grid combination can be selected randomly. Alternatively, the target mesh cell having the longest common edge with the mesh cells to be subjected to the merging operation may also be selected for merging to ensure that the merged mesh cells have a more regular shape as much as possible. After merging, the weight of the new grid cell takes the value of the average value of the sum of the weights of the sub-grids. After the segmentation and merging are completed, it needs to determine whether the number of grid cells is greater than a preset number threshold, and since the available resources for calculation are limited, it is to ensure that the number of grid cells is within a certain range, and if the number of grid cells is greater than the preset number threshold, the next round of grid cell adjustment is performed, and the setting of the preset number threshold is not limited in this embodiment.
In this embodiment, grids are split or combined according to the weights of the grid units, so that the grid division is more reasonable, a region with higher weights of the grid units can be effectively determined to be a key monitoring region, and the data collected from each grid unit can reflect the carbon circulation condition of the grid unit more truly and accurately, so that the accuracy of carbon dynamic monitoring is improved.
In one implementation manner, after the step of calculating the number of the grid cells in the step C40, the method further includes:
step C50, if the number is less than or equal to a preset number threshold, traversing the adjusted grid cells, and if the first weight of at least one grid cell is greater than the first weight threshold or less than the second weight threshold, executing: the step of adjusting the grid cells according to the first weights.
For example, if the number of grid cells is less than or equal to the preset number threshold, it indicates that the existing computing resource may meet the computing requirement, and at this time, it is further required to ensure that the adjusted weight of the grid cells is less than or equal to the first weight threshold and greater than or equal to the second weight threshold. If the weight of at least one grid cell does not meet the condition, all weights are reordered, new first weight threshold and second weight threshold are determined, and then grid cell adjustment of the next round is started.
In the embodiment, the grid is adjusted for multiple times, so that the grid division is more reasonable, the area with higher weight of the grid unit can be effectively determined to be the key monitoring area, the data collected from each grid unit can reflect the carbon circulation condition of the grid unit more truly and accurately, and the accuracy of carbon dynamic monitoring is improved.
Based on the first embodiment and the second embodiment of the present application, another embodiment of the present application is provided, further, step S300 of quantitatively calculating the carbon dynamic quantity of the target water area based on the adjusted second environmental information of each grid cell includes:
step D10, calculating the carbon vertical flux, the carbon horizontal flux and the carbon storage amount of the target water area according to the second environmental information;
step D20, calculating the carbon flux of the target water area according to the carbon vertical flux and the carbon horizontal flux;
and D30, calculating the carbon dynamic quantity of the target water area according to the carbon flux and the carbon storage quantity.
In this embodiment, the carbon storage amount of the target water area is the sum of DOC storage amount, POC storage amount, DIC storage amount, and TC storage amount, where DOC storage amount is in kg, the calculation formula is Σ (DOC concentration×grid volume), grid volume is the volume of each grid unit, DOC concentration is the DOC concentration corresponding to the grid unit, in mg/L, POC storage amount is in kg, the calculation formula is Σ (POC concentration×grid volume), grid volume is the volume of each grid unit, in m, POC concentration is the POC concentration corresponding to the grid unit, in mg/L, DIC storage amount is in kg, the calculation formula is Σ (DIC concentration×grid volume), grid volume is the volume of each grid unit, in m, DIC concentration is the DIC concentration corresponding to the grid unit, in mg/L, TC storage amount is in kg, grid concentration×grid volume of each grid unit, in m, TC concentration is the TC concentration corresponding to the grid unit, in mg/L. The mesh volume may be calculated from the size and depth of the mesh cells.
In this embodiment, the calculation formula of the vertical flux of carbon in the grid unit is vertical flux=deposition rate×carbon content, where the deposition rate represents the deposition rate of bottom sludge carbon in the grid unit, and the carbon content represents the organic carbon content in the bottom sludge in the grid unit, and the unit is the proportion of the organic carbon in the dry weight of the bottom sludge. And performing sum calculation on the carbon vertical flux of all grid cells to obtain the carbon vertical flux of the target water area. The calculation formula of the carbon level flux in the grid unit is horizontal flux=water flow rate×carbon concentration×water depth, wherein the carbon concentration includes DOC concentration, POC concentration, DIC concentration and TC concentration of the grid unit. The unit of carbon concentration is typically mg/L, which represents the carbon content per liter of water, the water flow rate is the flow rate of the water in the grid cell, and the water depth is the absolute water depth of the grid cell. The carbon level flux of the target water area can be obtained by summing the carbon level fluxes of all grid cells. The carbon flux in the target water area may be the sum of the vertical flux and the horizontal flux of carbon in the target water area. The calculation formula of the carbon dynamic quantity of the target water area is carbon storage quantity+carbon flux=carbon dynamic quantity, namely the sum of the carbon storage quantity and the carbon flux of the target water area is the carbon dynamic quantity of the target water area.
In this embodiment, the carbon dynamic quantity of the target water area is calculated, and meanwhile, the quantized values of the carbon horizontal flux and the carbon vertical flux can more clearly reflect the carbon change condition of the water area.
In another embodiment, step D10 further comprises:
e10, calculating a correction coefficient according to the first environmental information;
and E20, correcting the second environmental information according to the correction coefficient, and calculating the carbon vertical flux, the carbon horizontal flux and the carbon storage amount of the target water area according to the corrected second environmental information.
In this embodiment, parameters such as temperature, salinity, dissolved oxygen concentration, PH value, turbidity and the like may all affect carbon circulation, so that a correction coefficient may be calculated through data obtained from the first environmental information, and the calculation may be implemented by designing a statistical model or a machine learning model, and training according to historical data. If the correction factor may depend on a plurality of the above parameters, there may be complex nonlinear relationships between the parameters, in which case multiple linear regression, decision trees, neural networks, etc. may be used. The DOC concentration value may then be corrected using the calculated correction factor to correct the second environmental information, illustratively by calculating the product of the DOC concentration and the correction parameter.
In the embodiment, the influence of the physical parameters of the water body on the carbon circulation is considered, and errors caused by the influence of the physical parameters on the carbon circulation process are reduced by correcting the parameters, so that the accuracy of carbon dynamic monitoring is improved.
The application also provides a robot, referring to fig. 4, the robot is an underwater robot, and has a waterproof function and a wireless communication function, the wireless communication function can be realized through a Bluetooth technology, the robot comprises a machine body, a joint, a propeller, a water quality sensor (sensor module), a water sample collector (water sample collection module), a sediment collector (sediment collection module) and the like, and is provided with more than four external transmission and power supply interfaces, and the carrying capacity is more than 100kg. Wherein, sensor module includes mounting panel, protective housing and a plurality of sensor, and each sensor is fixed in the mounting panel, and the inner chamber of protective housing is located to mounting panel and sensor, and sensor module passes through the front side outer wall connection of joint and fuselage, and the protective housing has waterproof and withstand voltage function, and each sensor is fixed on above-mentioned mounting panel, and convenient integration and maintenance, sensor module link to each other with the robot main part through a waterproof joint. The water sample collection module comprises a multichannel collector, the multichannel collector is a rotary multichannel collector, a sampling channel is selected through rotation, the volume is reduced, the sampling efficiency is improved, each channel of the multichannel collector comprises a spring driving device and a valve, the spring driving device is used for controlling the opening and closing of the valve, the water sample collection module is connected with the side outer wall of the machine body through a connector and is fixed to the machine body through a telescopic bracket, and the water sample collection module has a telescopic function. The sediment collection module comprises a sediment collector and a packer, wherein the inner cavity of the packer is provided with a plurality of pipe columns, sediment samples can be collected in water areas with different depths, one end of the sediment collector is of a conical structure, the conical structure is of a catfish mouth-like structure, sediment is convenient to collect, the other end of the sediment collector is axially connected with the packer, the packer automatically packs the collected sediment, and the sediment collection module is connected with the outer wall of the bottom side of the machine body through a connector and is fixed to the machine body through a telescopic bracket and has a telescopic function. Each module is connected with the robot main body through a waterproof joint, so that data transmission and power supply are realized. The telescoping supports for the sampler and sediment collector may be made of high strength lightweight materials to reduce weight and increase stability. The robot reduces the volume of the robot through compact layout and optimal design, thereby reducing the resistance. The modules of the robot provided by the embodiment are designed to realize the acquisition of specific data (first environmental information and second environmental information) required by the carbon dynamic monitoring method, wherein a plurality of sensors of the sensor module can acquire indexes such as temperature, salinity, dissolved oxygen concentration, pH value, turbidity and the like of a water body of each grid unit in real time, a water sample acquisition module can acquire a water sample, the indexes such as dissolved organic carbon concentration, granular organic carbon concentration, dissolved inorganic carbon concentration, total carbon concentration and the like of the grid unit can be acquired through offline analysis of the water sample, a sediment acquisition module can acquire a sediment sample, and the indexes such as sediment organic carbon content and sediment carbon deposition rate of the grid unit can be acquired through offline analysis of the sediment sample.
The robot provided by the embodiment realizes the acquisition of the first environmental information and the second environmental information of the grid unit, so that data support is provided for the quantitative calculation of the dynamic carbon monitoring, the automatic acquisition is realized through the robot, the labor and time cost are saved, and the acquisition efficiency is improved.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a water carbon dynamic monitoring device in a hardware running environment according to an embodiment of the present application.
As shown in fig. 5, the water carbon dynamic monitoring device may be a server or a terminal device, and may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) Memory or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Those skilled in the art will appreciate that the configuration shown in fig. 5 does not constitute a limitation of the aquatic carbon dynamics monitoring apparatus and may include more or fewer components than shown, or may combine certain components, or may be a different arrangement of components.
As shown in fig. 5, an operating system, a data storage module, a network communication module, a user interface module, and a water carbon dynamic monitoring program may be included in the memory 1005 as one type of storage medium.
In the water carbon dynamic monitoring device shown in fig. 5, the network interface 1004 is mainly used for data communication with other devices; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the water area carbon dynamic monitoring device of the present application may be disposed in the water area carbon dynamic monitoring device, and the water area carbon dynamic monitoring device invokes the water area carbon dynamic monitoring program stored in the memory 1005 through the processor 1001, and executes the water area carbon dynamic monitoring method provided by the embodiment of the present application.
The present embodiment also provides a computer storage medium having computer readable program instructions stored thereon for performing the method of dynamically monitoring carbon in a body of water of the above embodiments.
The computer storage medium provided by the embodiment of the application can be, for example, a USB flash disk, but is not limited to an electric, magnetic, optical, electromagnetic, infrared system, system or device, or any combination of the above. More specific examples of the computer storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this embodiment, a computer storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The computer storage medium may be included in the aquatic carbon dynamics monitoring apparatus or may exist alone without being assembled into the aquatic carbon dynamics monitoring apparatus.
The computer storage medium carries one or more programs which, when executed by the aquatic carbon dynamics monitoring device, cause the aquatic carbon dynamics monitoring device to: dividing a target water area into grid cells, wherein the grid cells are three-dimensional grid cells;
adjusting each of the grid cells based on first environmental information of the grid cell, wherein the first environmental information includes one or more indicators strongly related to carbon circulation of the target water area; and quantitatively calculating the carbon dynamic quantity of the target water area based on the adjusted second environmental information of each grid unit, wherein the second environmental information at least comprises dissolved organic carbon concentration, granular organic carbon concentration, dissolved inorganic carbon concentration, total carbon concentration, sediment organic carbon content and sediment carbon deposition rate, and the first environmental information and the second environmental information are obtained through robot sampling.
Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present disclosure may be implemented in software or hardware. Wherein the name of the module does not constitute a limitation of the unit itself in some cases.
The computer storage medium provided by the application stores the computer readable program instructions for executing the water area carbon dynamic monitoring method, so that the technical problem of improving the accuracy of the water area carbon dynamic monitoring is solved. Compared with the prior art, the beneficial effects of the computer storage medium provided by the embodiment of the application are the same as those of the earphone box control method provided by the embodiment, and are not repeated here.

Claims (10)

1. The method for dynamically monitoring the carbon in the water area is characterized by comprising the following steps of:
dividing a target water area into grid cells, wherein the grid cells are three-dimensional grid cells;
adjusting each of the grid cells based on first environmental information of the grid cell, wherein the first environmental information includes one or more indicators strongly related to carbon circulation of the target water area;
and quantitatively calculating the carbon dynamic quantity of the target water area based on the adjusted second environmental information of each grid unit, wherein the second environmental information at least comprises dissolved organic carbon concentration, granular organic carbon concentration, dissolved inorganic carbon concentration, total carbon concentration, sediment organic carbon content and sediment carbon deposition rate, and the first environmental information and the second environmental information are obtained through robot sampling.
2. A method of dynamically monitoring carbon in a body of water as recited in claim 1, wherein said step of adjusting said grid cells based on first environmental information of each of said grid cells comprises:
calculating a first weight of each grid cell according to the first environment information;
and adjusting the grid unit according to the first weight.
3. A method of dynamically monitoring carbon in a body of water as recited in claim 2, wherein said step of calculating a first weight for each of said grid cells based on said first environmental information comprises:
calculating the information entropy of each index in the target water area according to the first environmental information of all the grid units;
calculating a second weight corresponding to each index according to the information entropy of each index;
and calculating the first weight of the grid unit according to the first environment information of the grid unit and the second weight corresponding to each index.
4. A method of dynamically monitoring carbon in a body of water as recited in claim 2, wherein said step of adjusting said grid cells in accordance with said first weight comprises:
sorting all the first weights, and screening from the sorted first weights according to preset conditions to obtain a first weight threshold and a second weight threshold, wherein the first weight threshold is used for dividing the grid cells, the second weight threshold is used for merging the grid cells, and the first weight threshold is larger than the second weight threshold;
If the first weight of the grid cell is greater than the first weight threshold, dividing the grid cell into a plurality of new grid cells;
if the first weight of the grid cell is smaller than the second weight threshold, the grid cell is integrated into a target grid cell, wherein the target grid cell is adjacent to the grid cell;
calculating the number of the grid cells, and if the number is greater than a preset number threshold, executing: the step of adjusting the grid cells according to the first weights.
5. A method of dynamically monitoring carbon in a body of water as recited in claim 4, wherein after the step of calculating the number of grid cells, the method further comprises:
traversing the adjusted grid cells if the number is less than or equal to a preset number threshold, and executing if the first weight of at least one grid cell is greater than the first weight threshold or less than the second weight threshold: the step of adjusting the grid cells according to the first weights.
6. A method of dynamically monitoring carbon in a body of water as recited in claim 1, wherein said step of quantitatively calculating the carbon dynamic quantity of the target body of water based on the adjusted second environmental information of each of the grid cells comprises:
Calculating the carbon vertical flux, the carbon horizontal flux and the carbon storage amount of the target water area according to the second environmental information;
calculating the carbon flux of the target water area according to the carbon vertical flux and the carbon horizontal flux;
and calculating the carbon dynamic quantity of the target water area according to the carbon flux and the carbon storage quantity.
7. A method of dynamically monitoring carbon in a body of water as claimed in any one of claims 1 to 6 wherein the first environmental information includes at least one or more of temperature, salinity, dissolved oxygen concentration, ph, turbidity, dissolved organic carbon concentration, particulate organic carbon concentration, dissolved inorganic carbon concentration, total carbon concentration, sediment organic carbon content and sediment carbon deposition rate.
8. A robot is characterized by comprising a body, a joint, a sensor module, a water sample acquisition module and a sediment acquisition module, wherein,
the sensor module, the water sample acquisition module and the sediment acquisition module are used for acquiring first environmental information and second environmental information;
the sensor module comprises a mounting plate, a protective shell and a plurality of sensors, each sensor is fixed on the mounting plate, the mounting plate and the sensors are arranged in an inner cavity of the protective shell, and the sensor module is connected with the front outer wall of the machine body through the connector;
The water sample collection module comprises a multichannel collector, each channel of the multichannel collector comprises a spring driving device and a valve, the spring driving device is used for controlling the opening and closing of the valve, and the water sample collection module is connected with the side outer wall of the machine body through the connector;
the sediment collection module comprises a sediment collector and a packaging device, a plurality of tubular columns are arranged in an inner cavity of the packaging device, one end of the sediment collector is of a conical structure, the other end of the sediment collector is axially connected with the packaging device, and the sediment collection module is connected with the outer wall of the bottom side of the machine body through a connector.
9. A water carbon dynamic monitoring device, the device comprising: a memory, a processor and a water carbon dynamic monitoring program stored on the memory and operable on the processor, the water carbon dynamic monitoring program being configured to implement the steps of the water carbon dynamic monitoring method of any one of claims 1 to 7.
10. A computer storage medium having stored thereon a water carbon dynamic monitoring program which when executed by a processor implements the steps of the water carbon dynamic monitoring method of any one of claims 1 to 7.
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