CN116668949B - Distributed hydrologic sensing method and equipment system based on Beidou satellite communication - Google Patents
Distributed hydrologic sensing method and equipment system based on Beidou satellite communication Download PDFInfo
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- 238000004891 communication Methods 0.000 title claims abstract description 66
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000005070 sampling Methods 0.000 claims abstract description 236
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 233
- 238000012544 monitoring process Methods 0.000 claims abstract description 91
- 238000012216 screening Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 description 16
- 238000004364 calculation method Methods 0.000 description 10
- 239000013589 supplement Substances 0.000 description 7
- 238000010586 diagram Methods 0.000 description 5
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 229910052760 oxygen Inorganic materials 0.000 description 2
- 239000001301 oxygen Substances 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000000586 desensitisation Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 239000012634 fragment Substances 0.000 description 1
- ZZUFCTLCJUWOSV-UHFFFAOYSA-N furosemide Chemical compound C1=C(Cl)C(S(=O)(=O)N)=CC(C(O)=O)=C1NCC1=CC=CO1 ZZUFCTLCJUWOSV-UHFFFAOYSA-N 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000001556 precipitation Methods 0.000 description 1
- 239000000047 product Substances 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C13/00—Surveying specially adapted to open water, e.g. sea, lake, river or canal
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/18—Water
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q9/00—Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/90—Services for handling of emergency or hazardous situations, e.g. earthquake and tsunami warning systems [ETWS]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/04—Wireless resource allocation
- H04W72/044—Wireless resource allocation based on the type of the allocated resource
- H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/02—Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
- H04W84/04—Large scale networks; Deep hierarchical networks
- H04W84/06—Airborne or Satellite Networks
Abstract
The application discloses a distributed hydrologic sensing method and equipment system based on Beidou satellite communication, and relates to the technical field of water conservancy surveys. Calculating the hydrologic critical state of each position in a monitoring area of the current time period according to the position of a sampling station of the current time period and corresponding hydrologic data under a plurality of data types; obtaining the information critical state of each sampling station in the current time period according to the hydrological critical state of each position in the monitoring area in the current time period and the position of each sampling station; the available communication resources of the next time period are distributed according to the information critical state of each sampling station of the current time period, and the number of times that each sampling station of the next time period uploads water level data in unit time is obtained to be used as the reporting frequency; and the next time period reporting frequency is returned to the corresponding sampling station. The application takes the timeliness and comprehensiveness of hydrologic data sampling into consideration under the condition of limited satellite communication resources.
Description
Technical Field
The application belongs to the technical field of water conservancy surveys, and particularly relates to a distributed hydrologic sensing method and equipment system based on Beidou satellite communication.
Background
Hydrologic sensing methods and devices are key technologies for water resource management, climate change research and natural disaster prediction. These devices are typically deployed in the surface and body of water to collect and transmit hydrographic environmental parameters such as water level, flow rate, dissolved oxygen, pH, etc. However, most of traditional hydrologic sensing systems are based on wired communication, so that the traditional hydrologic sensing systems are difficult to install and maintain, have high system delay and poor real-time performance, are greatly influenced by terrain environment, and limit the monitoring range.
Compared with other navigation positioning satellites, the Beidou satellite, in particular the Beidou satellite III, has the function of data communication, and is favorable for positioning a sampling station and transmitting hydrological data. However, since satellite communication resources are effective, cooperative control over the reporting frequency of the hydrologic data of each sampling station is required.
Disclosure of Invention
The application aims to provide a distributed hydrologic sensing method and equipment system based on Beidou satellite communication.
In order to solve the technical problems, the application is realized by the following technical scheme:
the application discloses a distributed hydrologic sensing method based on Beidou satellite communication, which comprises the following steps of,
acquiring the position of each sampling station in a monitoring area;
acquiring available communication resources in the next time period;
acquiring hydrological data of a plurality of sampling stations in a current period under a plurality of data types;
according to the position of the sampling station in the current period and corresponding hydrological data under a plurality of data types, calculating to obtain hydrological critical states of all positions in the monitoring area in the current period;
obtaining the information critical state of each sampling station in the current time period according to the hydrological critical state of each position in the monitoring area in the current time period and the position of each sampling station;
the available communication resources of the next time period are distributed according to the information critical state of each sampling station of the current time period, and the number of times of uploading water level data in unit time of each sampling station of the next time period is obtained to be used as the reporting frequency;
and transmitting the reporting frequency of the next time period back to the corresponding sampling station.
The application also discloses a distributed hydrologic sensing method based on Beidou satellite communication, which comprises the steps of,
uploading hydrologic data of the current period under a plurality of data types to a server;
receiving the message frequency;
and uploading the hydrological data of the next time period under a plurality of data types to a server according to the reporting frequency in the next time period.
The application also discloses a distributed hydrologic sensing method based on Beidou satellite communication, which comprises the steps of,
uploading hydrological data of the sampling station under a plurality of data types in the current period to a server through a satellite communication terminal;
acquiring the position of each sampling station in a monitoring area;
acquiring available communication resources in the next time period;
acquiring hydrological data of a plurality of sampling stations in a current period under a plurality of data types;
according to the position of the sampling station in the current period and corresponding hydrological data under a plurality of data types, calculating to obtain hydrological critical states of all positions in the monitoring area in the current period;
obtaining the information critical state of each sampling station in the current time period according to the hydrological critical state of each position in the monitoring area in the current time period and the position of each sampling station;
the available communication resources of the next time period are distributed according to the information critical state of each sampling station of the current time period, and the number of times of uploading water level data in unit time of each sampling station of the next time period is obtained to be used as the reporting frequency;
the reporting frequency of the next time period is returned to the corresponding sampling station;
receiving the message frequency;
and uploading the hydrological data of the next time period under a plurality of data types to a server according to the reporting frequency in the next time period.
The application also discloses a distributed hydrologic sensing equipment system based on Beidou satellite communication, which comprises,
the satellite communication end is used for transmitting data between the service end and the sampling station;
acquiring the position of the sampling station and sending the position to the server;
the sampling station is used for uploading hydrological data of the sampling station under a plurality of data types in the current period to the server through the satellite communication terminal;
the server side is used for acquiring the position of each sampling station in the monitoring area;
acquiring available communication resources in the next time period;
acquiring hydrological data of a plurality of sampling stations in a current period under a plurality of data types;
according to the position of the sampling station in the current period and corresponding hydrological data under a plurality of data types, calculating to obtain hydrological critical states of all positions in the monitoring area in the current period;
obtaining the information critical state of each sampling station in the current time period according to the hydrological critical state of each position in the monitoring area in the current time period and the position of each sampling station;
the available communication resources of the next time period are distributed according to the information critical state of each sampling station of the current time period, and the number of times of uploading water level data in unit time of each sampling station of the next time period is obtained to be used as the reporting frequency;
the reporting frequency of the next time period is returned to the corresponding sampling station;
the sampling station is also used for receiving the message frequency;
and uploading the hydrological data of the next time period under a plurality of data types to a server according to the reporting frequency in the next time period.
According to the application, the water service data in the monitoring area is summarized and analyzed, so that the timeliness and the comprehensiveness of hydrologic data sampling are considered under the condition of limited satellite communication resources. First, the position of each sampling station in the monitoring area is obtained, and the available communication resources in the next time period are obtained. And then acquiring hydrological data of a plurality of sampling stations in the current period under a plurality of data types, and calculating according to the positions of the sampling stations and the hydrological data to obtain the hydrological critical state of each position in the monitoring area in the current period. The information critical state for each sampling station for the current time period is then obtained based on the hydrologic critical state and the location of each sampling station. And allocating available communication resources in the next time period according to the information critical state, and determining the number of times of uploading water level data in unit time by each sampling station in the next time period as the reporting frequency. And finally, the reporting frequency of the next time period is returned to the corresponding sampling station for controlling the reporting frequency of the corresponding sampling station. The application comprehensively considers timeliness and comprehensiveness of data sampling, and effectively manages and transmits water service data.
Of course, it is not necessary for any one product to practice the application to achieve all of the advantages set forth above at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of functional units and information flow of an embodiment of a distributed hydrologic sensing device system based on Beidou satellite communication according to the present application;
FIG. 2 is a schematic flow chart of steps of an embodiment of a distributed hydrologic sensing method based on Beidou satellite communication according to the present application;
FIG. 3 is a flowchart illustrating the step S4 according to an embodiment of the present application;
FIG. 4 is a flowchart illustrating the step S42 according to an embodiment of the present application;
FIG. 5 is a flowchart illustrating the step S43 according to an embodiment of the present application;
FIG. 6 is a flowchart illustrating the step S5 according to an embodiment of the present application;
FIG. 7 is a flowchart illustrating the step S52 according to an embodiment of the present application;
FIG. 8 is a flowchart illustrating the step S6 according to an embodiment of the present application;
in the drawings, the list of components represented by the various numbers is as follows:
1-satellite communication end, 2-service end, 3-sampling station.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In order to cooperatively control the reporting frequency of the water service data uploaded by the distributed sampling stations, the application provides the following scheme.
Referring to fig. 1 to 2, the present application provides a distributed hydrologic sensing device system based on beidou satellite communication, where the division may include a satellite communication terminal 1, a server terminal 2 and a sampling station 3 for communication positioning from a hardware deployment. The satellite communication terminal 1 is used for transmitting data between the service terminal and the sampling station, and is limited by the Beidou No. three satellite used by the functional scheme.
In a specific implementation process, the satellite communication terminal 1 may first perform step S00 to obtain the position of the sampling station 3 and send the position to the server 2. And then the sampling station 3 executes the step S01 to upload the hydrological data of the sampling station under a plurality of data types in the current period to the server through the satellite communication terminal. Such as water level, flow rate, dissolved oxygen, turbidity, pH, etc. Of these, the most important is the water level information, and of course, the turbidity can be more indicative of the water crisis for mountain areas.
Then, the server 2 may perform step S1 to obtain the position of each sampling station in the monitoring area, and then may perform step S2 to obtain the available communication resources in the next period. Step S3 may then be performed to obtain the hydrologic data for the plurality of sampling stations for the current time period under several data types. Step S4 may be performed to calculate the hydrologic critical state of each position in the monitoring area of the current time period according to the position of the sampling station of the current time period and the corresponding hydrologic data under several data types. Step S5 may be performed to obtain the information critical state of each sampling station in the current period according to the hydrologic critical state of each location in the monitoring area in the current period and the location of each sampling station. And then, step S6 can be executed to allocate available communication resources in the next time period according to the information critical state of each sampling station in the current time period, so as to obtain the number of times of uploading water level data in unit time by each sampling station in the next time period as the reporting frequency. Step S7 may be performed to transmit the next time period reporting frequency back to the corresponding sampling station 3 through the satellite communication terminal 1.
Finally, the sampling station 3 may perform step S02 to receive the reporting frequency, and perform step S03 to upload the hydrological data of the several data types in the next period to the server according to the reporting frequency.
In the implementation process, the position of the sampling station of the monitoring area and the communication resource of the next period are firstly obtained, the hydrologic data of different data types of the multi-sampling station of the current period are obtained, and then the hydrologic critical state of each position of the monitoring area is obtained through calculation according to the position and the data. Based on the hydrologic critical state and the sampling station positions, the information critical state of each sampling station in the current period is obtained, and the number of times of uploading water level data in unit time of each sampling station is further determined to be used as the reporting frequency. And finally, returning the report frequency of the next period to the control frequency of the sampling station. The scheme comprehensively considers timeliness and comprehensiveness of data sampling, and effectively manages and transmits water service data.
To supplement the above-described implementation procedures of step S1 to step S7, source codes of part of the functional modules are provided, and a comparison explanation is made in the annotation section. In order to meet the data safety requirements of related laws and regulations on water conservancy facilities, desensitization treatment is carried out on partial data which does not influence the implementation of a scheme, and the following is carried out. This code essentially provides a process for obtaining data, processing the data, and then distributing and reporting the data.
#include <iostream>
#include <vector>
#include <map>
Data structure defining sampling station
struct SamplingStation {
int id;
double latitude;
double longitude;
std::map<std::string, double> hydrologicalData;
};
Data structure for defining hydrologic critical state
struct HydrologicalEmergencyStatus {
int stationId;
double statusValue;
};
Data structure of// definition communication resource
struct CommunicationResource {
int stationId;
int availableResources;
};
int main() {
Position of each sampling station in a monitoring area is/is acquired
Where the sampling station data has been obtained from a data source
std::vector<SamplingStation> samplingStations = {/*...*/};
Obtaining/obtaining available communication resources for a next time period
Where the communication resource data has been obtained from a data source
std::vector<CommunicationResource> nextPeriodCommResources = {/*...*/};
Obtaining/acquiring hydrological data of a plurality of sampling stations of a current period under a number of data types
for (auto& station : samplingStations) {
Obtaining hydrological data from a data source and populating it with station
}
Calculating to obtain the hydrologic critical state of each position in the monitoring area of the current time period according to the position of the sampling station of the current time period and the corresponding hydrologic data under a plurality of data types
std::vector<HydrologicalEmergencyStatus> emergencyStatuses;
for (auto& station : samplingStations) {
Calculating the hydrologic critical state from the position of the station.hydrologic data and station
HydrologicalEmergencyStatus status;
status.stationId = station.id;
Status value requires assignment according to the actual calculation method
emergencyStatuses.push_back(status);
}
Obtaining information critical state of each sampling station in current period according to hydrologic critical state of each position in monitoring area in current period
std::map<int, double> stationInformationEmergencyStatus;
for (auto& emergencyStatus : emergencyStatuses) {
Calculating information critical state of each sampling station
double infoEmergencyStatus = /*...*/;
stationInformationEmergencyStatus[emergencyStatus.stationId] = infoEmergencyStatus;
}
The available communication resources of the next time period are distributed according to the information critical state of each sampling station of the current time period, so that the number of times of uploading water level data in unit time of each sampling station of the next time period is obtained as the reporting frequency
std::map<int, int> nextPeriodReportingFrequency;
for (auto& commResource : nextPeriodCommResources) {
Allocating communication resources based on information critical status
int frequency = /*...*/;
nextPeriodReportingFrequency[commResource.stationId] = frequency;
}
The next time period report frequency is returned to the corresponding sampling station
This part will be related to a specific data backhaul scheme, where only a simplified example of an output to the console is provided
for (auto& item : nextPeriodReportingFrequency) {
std::cout << "Station ID: " << item.first << ", Next Period Reporting Frequency: " << item.second << std::endl;
}
return 0;
}
Referring to fig. 3, in a severe rainfall state, due to uneven distribution of the rain accumulation clouds, rainfall degrees at different positions in the monitoring area are different, so that water level increase degrees at different positions are different, and the difference of the hydrologic critical states at all positions in the monitoring area is reflected. In order to perform quantization calculation on this, step S4 may be performed in the specific implementation process, where step S41 obtains, according to the position of the sampling station in the current period and the corresponding hydrological data under several data types, a water level increase value of the position of the sampling station in a unit time as a water level increase rate corresponding to the position of each sampling station in the current period. Step S42 may be executed to obtain the areas of different water service areas in the monitoring area of the current period and the corresponding comprehensive water level increasing rate according to the water level increasing rate corresponding to the position of each sampling station in the current period. Step S43 can be executed to obtain the water critical state of the different water service areas in the monitoring area of the current period according to the areas of the different water service areas in the monitoring area of the current period and the corresponding comprehensive water level increasing rate. Finally, step S44 may be executed to obtain the hydrologic critical state of each position in the monitoring area according to the hydrologic critical states of each position in the monitoring area belonging to different water service areas and different water service areas in the monitoring area in the current period.
In order to supplement the implementation process of the steps, source codes of partial functional modules are provided, and the explanation is compared in the annotating part. In this code, the hydrologic critical state of each location is stored in a map, where the key is the ID of the sampling station and the value is a structure containing the hydrologic critical state. The corresponding hydrologic critical state can be looked up by the ID of the sampling station.
struct SamplingStation {
int id;
double latitude;
double longitude;
std::map<std::string, double> hydrologicalData;
double waterLevelGrowthRate;// rate of water level increase
};
Data structure defining water service area
struct HydroArea {
double area;
double totalWaterLevelGrowthRate;
double emergencyStatus;
};
Data structure for defining hydrologic critical state
struct HydrologicalEmergencyStatus {
int stationId;
double statusValue;
};
int main() {
Position of each sampling station in a monitoring area is/is acquired
std::vector<SamplingStation> samplingStations = {/*...*/};
Obtaining the water level increment value of the position of the sampling station in unit time according to the position of the sampling station in the current period and the corresponding hydrological data under a plurality of data types, and taking the water level increment value as the water level increment rate corresponding to the position of each sampling station in the current period
for (auto& station : samplingStations) {
The water level increasing rate is calculated according to the actual calculation method and assigned to the station
// station.waterLevelGrowthRate = /*...*/;
}
Obtaining the areas of different water service areas in the monitoring area of the current period and the corresponding comprehensive water level increasing rate according to the water level increasing rate corresponding to the position of each sampling station in the current period
std::vector<HydroArea> hydroAreas = {/*...*/};
for (auto& area : hydroAreas) {
The part is required to calculate the comprehensive water level increasing rate according to the actual calculation method and assign the comprehensive water level increasing rate to the area
// area.totalWaterLevelGrowthRate = /*...*/;
}
Obtaining the hydrologic crisis state of different water service areas in the monitoring area of the current period according to the areas of the different water service areas in the monitoring area of the current period and the corresponding comprehensive water level increase rate
for (auto& area : hydroAreas) {
This part requires calculation of the hydrologic crisis state according to the actual calculation method and assignment to area
/(e.g.:
// area.emergencyStatus = /*...*/;
}
obtaining the hydrologic critical state of each position in the monitoring area according to the hydrologic critical states of each position in the monitoring area belonging to different water service areas and different water service areas in the monitoring area in the current period
std::map<int, HydrologicalEmergencyStatus> hydrologicalEmergencyStatuses;
for (auto& station : samplingStations) {
The part is required to obtain the hydrologic critical state of each position according to the actual attribution relation and the calculation method and assign the hydrologic EmerrgencyStatus
/(e.g.:
// HydrologicalEmergencyStatus status;
// status.stationId = station.id;
// status.statusValue = /*...*/;
// hydrologicalEmergencyStatuses[station.id] = status;
}
return 0;
}
referring to fig. 4, since there is no large difference in precipitation between adjacent positions, the areas with similar water level increase rates can be combined. In view of this, in the process of processing the implementation of the step S42, the server may first execute the step S421 to select, from the plurality of water level increasing rates corresponding to all the sampling stations, a plurality of water level increasing rates with the previous numerical ranks as the marked water level increasing rates. Step S422 may then be performed to calculate the difference between each mark water level increase rate and the other water level increase rates, respectively. Step S423 may then be performed to incorporate each of the other water level increase rates into the same water service data set based on the difference between the mark water level increase rate and the other water level increase rate. Step S424 may then be performed to calculate a median of each water service data set as an updated mark water level increase rate. Step S425 may next be performed to update the water service data set according to the updated marked water level increase rate. Step S426 may then be performed to determine whether the updated water service data set has changed. If not, step S422 to step S426 may be performed continuously to update the mark water level increase rate and the water service data sets, and if yes, step S427 may be performed continuously to use the area of the sampling station corresponding to the water service data set in each water service data set as the area of the corresponding water service area. Finally, step S428 may be performed to take the median, average or mode of the water level increase rate in each water service data set as the water level integrated increase rate of the corresponding water service area.
In order to supplement the implementation process of the steps, source codes of partial functional modules are provided, and the explanation is compared in the annotating part. The basic idea of the above code is to use an iterative process to continuously update the mark water level growth rate and the water service data sets until these data sets are no longer changing. Then, the median (or average or mode) of the water level increase rate in each data set is calculated as the water level integrated increase rate of the corresponding water service area. In this process, the areas of the areas where the sampling stations are located are accumulated to obtain the areas of the corresponding water service areas.
struct SamplingStation {
int id;
double latitude;
double longitude;
double waterLevelGrowthRate;// rate of water level increase
double area;// area of the region where the sampling station is located
};
Data structure defining water service area
struct HydroArea {
std: vector < double > waterLevelGrowthRates;// set of water level growth rates
double area;// area of water area
double totalWaterLevelGrowthRate/Water level comprehensive increasing rate
};
int main() {
Position and water level increase rate of each sampling station in the acquisition monitoring area
std::vector<SamplingStation> samplingStations = {/*...*/};
Selecting a plurality of values with the previous ranking from a plurality of water level increasing rates corresponding to all sampling stations as marked water level increasing rates
std = {// this requires selection according to actual requirements
Creation of water service area sets
std::vector<HydroArea> hydroAreas(markedWaterLevelGrowthRates.size());
bool dataGroupsChanged;
do {
dataGroupsChanged = false;
Data of old water area is/are emptied
for (auto& area : hydroAreas) {
area.waterLevelGrowthRates.clear();
area.area = 0.0;
}
Incorporating each of the other water level increment rates into the same water service data set based on the marked water level increment rate
for (const auto& station : samplingStations) {
double minDifference = std::numeric_limits<double>::max();
size_t closestMarkedRateIndex = 0;
for (size_t i = 0; i < markedWaterLevelGrowthRates.size(); i++) {
double difference = std::abs(markedWaterLevelGrowthRates[i] - station.waterLevelGrowthRate);
if (difference < minDifference) {
minDifference = difference;
closestMarkedRateIndex = i;
}
}
hydroAreas[closestMarkedRateIndex].waterLevelGrowthRates.push_back(station.waterLevelGrowthRate);
hydroAreas[closestMarkedRateIndex].area += station.area;
}
Obtaining the median of each water service data set as the updated mark water level increasing rate by means of the calculation and checking whether the water service data set changes
for (size_t i = 0; i < hydroAreas.size(); i++) {
std::sort(hydroAreas[i].waterLevelGrowthRates.begin(), hydroAreas[i].waterLevelGrowthRates.end());
double newMarkedRate = hydroAreas[i].waterLevelGrowthRates[hydroAreas[i].waterLevelGrowthRates.size() / 2];
if (newMarkedRate != markedWaterLevelGrowthRates[i]) {
markedWaterLevelGrowthRates[i] = newMarkedRate;
dataGroupsChanged = true;
}
Calculating the median, average or mode of the water level increase rate in each water service data set as the comprehensive water level increase rate of the corresponding water service area
The median is taken as an example here:
hydroAreas[i].totalWaterLevelGrowthRate = newMarkedRate;
}
} while (dataGroupsChanged);
return 0;
}
referring to fig. 5, in order to upload water traffic data of water traffic critical areas at high frequency, it is necessary to quantitatively calculate water traffic critical states of different water traffic areas. Specifically, the water condition of the water service area is critical as the comprehensive water level increasing rate is higher, and the water condition of the water service area is critical as the water service area is larger. In view of this, in the implementation process of step S43, step S431 may be performed first to obtain the ratio of the areas of different water service areas in the monitoring area of the current time period according to the areas of different water service areas in the monitoring area of the current time period. Step S432 may be performed next, where the area proportionality coefficient between different water service areas is used as the hydrologic critical state proportionality coefficient between different water service areas in the current period according to the ratio of the areas between different water service areas in the monitoring area in the current period. And finally, step S433 can be executed to adjust the corresponding comprehensive water level increase rate according to the proportional coefficient of the water critical state in different water service areas in the current period to obtain the weighted comprehensive water level increase rate of each water service area as the water critical state of different water service areas in the monitoring area in the current period.
In order to supplement the implementation process of the steps, source codes of partial functional modules are provided, and the explanation is compared in the annotating part. This code first calculates the total area of all water service areas in the monitored area. Then, for each water area, it divides the area of that area by the total area to give an area ratio. This ratio is used as a scaling factor for the hydrologic critical state for adjusting the overall rate of increase of the water level for each zone. The end result is the hydrologic critical state of each water service area.
Data structure defining hydrologic crisis state
struct HydrologicalEmergencyStatus {
int hydroAreaId;// Water service area ID
double statusValue,// hydrologic critical state value
};
int main() {
Information about each water area is obtained (see the previous code)
std::vector<HydroArea> hydroAreas = {/*...*/};
Calculation of the total area
double totalArea = 0.0;
for (const auto& area : hydroAreas) {
totalArea += area.area;
}
Calculating and storing hydrologic crisis states
std::vector<HydrologicalEmergencyStatus> hydroEmergencyStatuses;
for (size_t i = 0; i < hydroAreas.size(); i++) {
HydrologicalEmergencyStatus status;
status.hydroAreaId = i;
The ratio of/(to area) is the area proportionality coefficient, i.e. the hydrologic critical state proportionality coefficient
double areaRatio = hydroAreas[i].area / totalArea;
Adjusting the comprehensive water level increasing rate to obtain a hydrologic critical state value
status.statusValue = hydroAreas[i].totalWaterLevelGrowthRate × areaRatio;
hydroEmergencyStatuses.push_back(status);
}
Output of the hydrologic crisis status of each water service area
for (const auto& status : hydroEmergencyStatuses) {
std::cout << "Hydro Area ID: " << status.hydroAreaId
<< ", Emergency Status Value: " << status.statusValue << std::endl;
}
return 0;
}
Referring to fig. 6, since the number of sampling stations 1 is different in different water service areas, the greater the number of sampling stations 1 per unit area, the lower the importance of a single sampling station 1. In view of this, in the implementation process of step S5, step S51 may be performed first to obtain the weighted water level integrated growth rate of each sampling station in the current period according to the weighted water level integrated growth rates of different water service areas in the monitoring area in the current period. Step S52 may then be performed to derive an importance adjustment coefficient between each sampling station based on the location of each sampling station within the monitored area. Finally, step S53 may be executed to perform weighted adjustment on the weighted water level integrated growth rate of each sampling station according to the importance adjustment coefficient between each sampling station, so as to obtain the weighted water level integrated adjustment growth rate of each sampling station in the current period as the corresponding information critical state.
In order to supplement the implementation process of the steps, source codes of partial functional modules are provided, and the explanation is compared in the annotating part. This piece of code needs to rely on the structural definition and data of the hydrographic emergencystatus and samplingstatus generated in the previous code. Firstly, acquiring the hydrologic critical state of the water service area where each sampling station is located, then carrying out weighted adjustment on the hydrologic critical state according to the importance adjustment coefficient, and finally obtaining the information critical state of each sampling station as a result.
Data structure for defining information critical state
struct InformationEmergencyStatus {
int stationId;// sampling station ID
double statusValue,// information critical status value
};
int main() {
The information of the hydrologic critical state of each water service area and the information of each sampling station are obtained
std::vector<HydrologicalEmergencyStatus> hydroEmergencyStatuses = {/*...*/};
std::vector<SamplingStation> samplingStations = {/*...*/};
The adjustment coefficient of the importance is known
std::vector<double> importanceAdjustmentFactors = {/*...*/};
Calculating and storing information critical states
std::vector<InformationEmergencyStatus> infoEmergencyStatuses;
for (size_t i = 0; i < samplingStations.size(); i++) {
InformationEmergencyStatus status;
status.stationId = samplingStations[i].stationId;
Obtaining weighted water level integrated growth rate (i.e. water critical state value of water service area where the weighted water level integrated growth rate is located) of current sampling station
status.statusValue = hydroEmergencyStatuses[samplingStations[i].hydroAreaId].statusValue;
Weighted adjustment according to importance adjustment coefficient
status.statusValue ×= importanceAdjustmentFactors[i];
infoEmergencyStatuses.push_back(status);
}
Output of information critical state for each sampling station
for (const auto& status : infoEmergencyStatuses) {
std::cout << "Sampling Station ID: " << status.stationId
<< ", Information Emergency Status Value: " << status.statusValue << std::endl;
}
return 0;
}
Referring to fig. 7, in order to quantitatively calculate the importance of each sampling station 1, step S52 may be implemented by first performing step S521 to obtain an average value of the distances between any two sampling stations as the screening distance according to the position of each sampling station in the monitored area. Step S522 may next be performed to calculate, for each sampling station, the number of other sampling stations within the screening distance as the number of adjacencies of the sampling station. Step S523 may be performed next to acquire the inverse of the number of adjacencies per sampling station as an importance parameter per sampling station. Finally, step S524 may be performed to calculate a ratio of the importance parameters between each sampling station as an importance adjustment coefficient between each sampling station.
In order to supplement the implementation process of the steps, source codes of partial functional modules are provided, and the explanation is compared in the annotating part.
double calculateDistance(const SamplingStation& a, const SamplingStation& b) {
Calculating the Euclidean distance between two sampling stations
double xDiff = a.position.x - b.position.x;
double yDiff = a.position.y - b.position.y;
return sqrt(xDiff × xDiff + yDiff × yDiff);
}
int main() {
Information about each sampling station is obtained
std::vector<SamplingStation> stations = {/*...*/};
Calculating the distance between any two sampling stations and calculating the average value
double totalDistance = 0.0;
int numDistances = 0;
for (size_t i = 0; i < stations.size(); i++) {
for (size_t j = i+1; j < stations.size(); j++) {
totalDistance += calculateDistance(stations[i], stations[j]);
numDistances++;
}
}
double averageDistance = totalDistance / numDistances;
Calculating the number of adjacencies per sampling station
std::vector<int> adjacencyCounts(stations.size(), 0);
for (size_t i = 0; i < stations.size(); i++) {
for (size_t j = 0; j < stations.size(); j++) {
if (i != j && calculateDistance(stations[i], stations[j]) <= averageDistance) {
adjacencyCounts[i]++;
}
}
}
Obtaining/acquiring importance parameters for each sampling station
std::vector<double> importanceParameters(stations.size());
for (size_t i = 0; i < stations.size(); i++) {
importanceParameters[i] = 1.0 / adjacencyCounts[i];
}
Output of importance parameters (i.e. their importance adjustment coefficients) for each sampling station
for (size_t i = 0; i < stations.size(); i++) {
std::cout << "Station ID: " << stations[i].stationId
<< ", Importance Adjustment Factor: " << importanceParameters[i] << std::endl;
}
return 0;
}
This piece of code needs to rely on the samplingstate data structure, which has been defined in the previous code fragment. First, an average value of distances between any two sampling stations is calculated as a screening distance. Then, for each sampling station, the number of other sampling stations within the screening distance is calculated as the number of adjacencies of that station. The inverse of the number of adjacencies per sampling station is obtained as an importance parameter for each sampling station.
Referring to fig. 8, in order to adjust and allocate limited available communication resources of satellites to achieve the technical effect of taking both timeliness and comprehensiveness of hydrological data sampling into consideration, step S6 may be executed first to obtain the total available report times of all sampling stations in the monitoring area of the next time period according to the available communication resources of the next time period according to step S61 in the implementation process. Step S62 can be executed to allocate the total available flood number of all sampling stations in the monitoring area of the next period according to the ratio between the weighted water level comprehensive adjustment growth rates of each sampling station of the current period, so as to obtain the available flood number of each sampling station of the next period. Finally, step S63 may be executed to obtain the reporting frequency of each sampling station in the next period according to the available reporting times of each sampling station in the next period.
In order to supplement the implementation process of the steps, source codes of partial functional modules are provided, and the explanation is compared in the annotating part. This code relies on the samplingstate data structure defined previously and samplingstate contains double importanceFactor member variables to represent the information critical state (i.e., the weighted water level integrated adjustment growth rate) of each sampling station and int reportFrequency to represent the reporting frequency of each sampling station for the next period. First, the sum of the weighted water level comprehensive adjustment growth rate of each sampling station in the current period is calculated. And then, comprehensively adjusting the ratio between the growth rates according to the weighted water level of each sampling station, and distributing the available flood number of the next time period to obtain the available flood number of each sampling station of the next time period, wherein the available flood number is represented by a reportFrequency. And finally, outputting the reporting frequency of each sampling station in the next period by the code.
int main() {
Information about each sampling station, including their importance parameters, has been obtained
std::vector<SamplingStation> stations = {/*...*/};
The available communication resources for the next time period
int totalReportTimes = 1000;
Calculating the sum of the weighted water level integrated adjustment growth rate for each sampling station for the current period
double totalImportance = 0.0;
for (const auto& station : stations) {
totalImportance += station.importanceFactor;
}
The available number of flood times for next time period is/are distributed
for (auto& station : stations) {
station.reportFrequency = totalReportTimes × (station.importanceFactor / totalImportance);
}
Output of the reporting frequency of each sampling station for the next period
for (const auto& station : stations) {
std::cout << "Station ID: " << station.stationId
<< ", Report Frequency: " << station.reportFrequency << std::endl;
}
return 0;
}
In summary, the method and the device ensure timeliness and comprehensiveness of hydrologic data sampling under the condition of limited satellite communication resources by comprehensively analyzing the water service data in the monitoring area. In the implementation process, the position of each sampling station in the monitoring area and the available communication resources in the next time period are firstly obtained. And collecting the hydrologic data of the sampling stations in different data types in the current period, and calculating by using the positions of the sampling stations and the hydrologic data to determine the hydrologic critical state of each position in the monitoring area. And then acquiring the information critical state of each sampling station in the current period according to the hydrologic critical state and the position of each sampling station. And based on the information critical state, the available communication resources in the next time period are allocated to determine the number of times of uploading water level data in unit time by each sampling station, namely the reporting frequency. And finally, transmitting the reporting frequency of the next time period to the corresponding sampling station so as to control the reporting frequency. The timeliness and the comprehensiveness of data sampling are comprehensively considered so as to effectively manage and transmit water service data.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, 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 instructions, which comprises one or more executable instructions for implementing the specified logical function(s). 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 hardware, such as circuits or ASICs (application specific integrated circuits, application Specific Integrated Circuit), which perform the corresponding functions or acts, or combinations of hardware and software, such as firmware, etc.
Although the application is described herein in connection with various embodiments, other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed application, from a study of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the "a" or "an" does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.
The foregoing description of embodiments of the application has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the improvement of technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (9)
1. A distributed hydrologic sensing method based on Beidou satellite communication is characterized by comprising the following steps of,
acquiring the position of each sampling station in a monitoring area;
acquiring available communication resources in the next time period;
acquiring hydrological data of a plurality of sampling stations in a current period under a plurality of data types;
according to the position of the sampling station in the current period and corresponding hydrological data under a plurality of data types, calculating to obtain hydrological critical states of all positions in the monitoring area in the current period;
obtaining the information critical state of each sampling station in the current time period according to the hydrological critical state of each position in the monitoring area in the current time period and the position of each sampling station;
the available communication resources in the next time period are distributed according to the information critical state of each sampling station in the current time period, so that the number of times of uploading water level data in unit time by each sampling station in the next time period is obtained as the reporting frequency;
the reporting frequency of the next time period is returned to the corresponding sampling station;
the step of calculating the hydrologic critical state of each position in the monitoring area in the current time period according to the position of the sampling station in the current time period and the corresponding hydrologic data under a plurality of data types comprises the following steps of,
obtaining a water level increase value of the position of the sampling station in unit time according to the position of the sampling station in the current period and corresponding hydrological data under a plurality of data types, wherein the water level increase value is used as a water level increase rate corresponding to the position of each sampling station in the current period;
obtaining the areas of different water service areas in the monitoring area of the current period and the corresponding comprehensive water level increasing rate according to the water level increasing rate corresponding to the position of each sampling station in the current period;
obtaining the hydrologic crisis state of different water service areas in the monitoring area of the current period according to the areas of the different water service areas in the monitoring area of the current period and the corresponding comprehensive water level increase rate;
and obtaining the hydrologic critical state of each position in the monitoring area according to the hydrologic critical states of each position in the monitoring area belonging to different water service areas and different water service areas in the monitoring area in the current period.
2. The method of claim 1, wherein the server performs the step of obtaining the areas of different water service areas in the monitoring area of the current period and the corresponding comprehensive water level increasing rate according to the water level increasing rate corresponding to the position of each sampling station in the current period,
selecting a plurality of water level increasing rates with the previous numerical value row from a plurality of water level increasing rates corresponding to all the sampling stations as marked water level increasing rates;
respectively calculating to obtain the difference value of the water level increasing rate of each mark and other water level increasing rates;
the marked water level increasing rate with the minimum difference value of each other water level increasing rate is included into the same water service data set according to the difference value of the marked water level increasing rate and the other water level increasing rates;
calculating and obtaining the median of each water service data set as the updated mark water level increase rate;
updating the water service data set according to the updated marked water level increase rate;
judging whether the updated water service data set changes or not;
if not, continuously updating the marked water level increase rate and the water service data set;
if yes, taking the area of the area where the sampling station corresponding to the water level increase rate in each water service data set is located as the area corresponding to the water service area;
and taking the median, average or mode of the water level increase rate in each water service data set as the comprehensive water level increase rate corresponding to the water service area.
3. The method of claim 1, wherein the step of obtaining the water critical status of the different water service areas in the current time period monitoring area according to the areas of the different water service areas in the current time period monitoring area and the corresponding comprehensive water level increase rate comprises,
obtaining the ratio of the areas of different water service areas in the monitoring area of the current time period according to the areas of the different water service areas in the monitoring area of the current time period;
taking the area proportionality coefficient between different water service areas as the hydrologic critical state proportionality coefficient between different water service areas in the current time period according to the ratio of the areas between the different water service areas in the monitoring area of the current time period;
and adjusting the corresponding comprehensive water level increase rate according to the proportional coefficients of the hydrologic critical states of different water service areas in the current period to obtain the weighted comprehensive water level increase rate of each water service area as the hydrologic critical state of the different water service areas in the monitoring area of the current period.
4. The method of claim 3, wherein the step of deriving the information critical state for each of the sampling stations for the current time period based on the hydrographic critical state for each location within the monitored area for the current time period and the location of each sampling station comprises,
obtaining the weighted water level comprehensive increasing rate of each sampling station in the current period according to the weighted water level comprehensive increasing rates of different water service areas in the monitoring area of the current period;
obtaining an importance adjustment coefficient between each sampling station according to the position of each sampling station in the monitoring area;
and carrying out weighted adjustment on the weighted water level comprehensive increasing rate of each sampling station according to the importance adjusting coefficient among each sampling station to obtain the weighted water level comprehensive adjusting increasing rate of each sampling station in the current period as a corresponding information critical state.
5. The method of claim 4, wherein said step of deriving an importance adjustment factor between each of said sampling stations based on the location of each of said sampling stations within a monitored area comprises,
obtaining an average value of distances between any two sampling stations as a screening distance according to the position of each sampling station in a monitoring area;
calculating, for each of the sampling stations, the number of other of the sampling stations within the screening distance as the number of adjacencies of the sampling station;
obtaining the reciprocal of the adjacent number of each sampling station as an importance parameter of each sampling station;
and calculating and acquiring the ratio of the importance parameters between each sampling station as an importance adjustment coefficient between each sampling station.
6. The method of claim 4, wherein the step of allocating the communication resources available for the next period according to the information critical state of each sampling station for the current period to obtain the number of times of uploading water level data per unit time for each sampling station for the next period as the reporting frequency comprises,
obtaining total available flood number of all sampling stations in the monitoring area of the next time period according to the available communication resources of the next time period;
the total available flood number of all sampling stations in the monitoring area of the next time period is distributed according to the ratio between the weighted water level comprehensive adjustment growth rates of each sampling station in the current time period, so that the available flood number of each sampling station in the next time period is obtained;
and obtaining the reporting frequency of each sampling station in the next time period according to the available reporting times of each sampling station in the next time period.
7. A distributed hydrologic sensing method based on Beidou satellite communication is characterized by comprising the following steps of,
uploading hydrologic data of the current period under a plurality of data types to a server;
receiving a message frequency in a distributed hydrologic sensing method based on Beidou satellite communication according to any one of claims 1 to 6;
and uploading the hydrological data of the next time period under a plurality of data types to a server according to the reporting frequency in the next time period.
8. A distributed hydrologic sensing method based on Beidou satellite communication is characterized by comprising the following steps of,
uploading hydrological data of the sampling station under a plurality of data types in the current period to a server through a satellite communication terminal;
acquiring the position of each sampling station in a monitoring area;
acquiring available communication resources in the next time period;
acquiring hydrological data of a plurality of sampling stations in a current period under a plurality of data types;
according to the position of the sampling station in the current period and corresponding hydrological data under a plurality of data types, calculating to obtain hydrological critical states of all positions in the monitoring area in the current period;
obtaining the information critical state of each sampling station in the current time period according to the hydrological critical state of each position in the monitoring area in the current time period and the position of each sampling station;
the available communication resources of the next time period are distributed according to the information critical state of each sampling station of the current time period, and the number of times of uploading water level data in unit time of each sampling station of the next time period is obtained to be used as the reporting frequency;
the reporting frequency of the next time period is returned to the corresponding sampling station;
receiving the message frequency;
uploading hydrological data of a next time period under a plurality of data types to a server according to the reporting frequency in the next time period;
the step of calculating the hydrologic critical state of each position in the monitoring area in the current time period according to the position of the sampling station in the current time period and the corresponding hydrologic data under a plurality of data types comprises the following steps of,
obtaining a water level increase value of the position of the sampling station in unit time according to the position of the sampling station in the current period and corresponding hydrological data under a plurality of data types, wherein the water level increase value is used as a water level increase rate corresponding to the position of each sampling station in the current period;
obtaining the areas of different water service areas in the monitoring area of the current period and the corresponding comprehensive water level increasing rate according to the water level increasing rate corresponding to the position of each sampling station in the current period;
obtaining the hydrologic crisis state of different water service areas in the monitoring area of the current period according to the areas of the different water service areas in the monitoring area of the current period and the corresponding comprehensive water level increase rate;
and obtaining the hydrologic critical state of each position in the monitoring area according to the hydrologic critical states of each position in the monitoring area belonging to different water service areas and different water service areas in the monitoring area in the current period.
9. A distributed hydrologic sensing equipment system based on Beidou satellite communication is characterized by comprising,
the satellite communication end is used for transmitting data between the service end and the sampling station;
acquiring the position of the sampling station and sending the position to the server;
the sampling station is used for uploading hydrological data of the sampling station under a plurality of data types in the current period to the server through the satellite communication terminal;
the server side is used for acquiring the position of each sampling station in the monitoring area;
acquiring available communication resources in the next time period;
acquiring hydrological data of a plurality of sampling stations in a current period under a plurality of data types;
according to the position of the sampling station in the current period and corresponding hydrological data under a plurality of data types, calculating to obtain hydrological critical states of all positions in the monitoring area in the current period;
obtaining the information critical state of each sampling station in the current time period according to the hydrological critical state of each position in the monitoring area in the current time period and the position of each sampling station;
the available communication resources of the next time period are distributed according to the information critical state of each sampling station of the current time period, and the number of times of uploading water level data in unit time of each sampling station of the next time period is obtained to be used as the reporting frequency;
the reporting frequency of the next time period is returned to the corresponding sampling station;
the sampling station is also used for receiving the message frequency;
uploading hydrological data of a next time period under a plurality of data types to a server according to the reporting frequency in the next time period;
the step of calculating the hydrologic critical state of each position in the monitoring area in the current time period according to the position of the sampling station in the current time period and the corresponding hydrologic data under a plurality of data types comprises the following steps of,
obtaining a water level increase value of the position of the sampling station in unit time according to the position of the sampling station in the current period and corresponding hydrological data under a plurality of data types, wherein the water level increase value is used as a water level increase rate corresponding to the position of each sampling station in the current period;
obtaining the areas of different water service areas in the monitoring area of the current period and the corresponding comprehensive water level increasing rate according to the water level increasing rate corresponding to the position of each sampling station in the current period;
obtaining the hydrologic crisis state of different water service areas in the monitoring area of the current period according to the areas of the different water service areas in the monitoring area of the current period and the corresponding comprehensive water level increase rate;
and obtaining the hydrologic critical state of each position in the monitoring area according to the hydrologic critical states of each position in the monitoring area belonging to different water service areas and different water service areas in the monitoring area in the current period.
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| CN203116807U (en) * | 2013-04-08 | 2013-08-07 | 天津虹炎科技有限公司 | Water level observation system based on Beidou satellite |
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| CN115802415A (en) * | 2023-02-02 | 2023-03-14 | 易迅通科技有限公司 | Hydrology monitoring system based on big dipper communication technology |
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| US7626508B2 (en) * | 2002-03-05 | 2009-12-01 | Aeromesh Corporation | Monitoring system and method |
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|---|---|---|---|---|
| CN203116807U (en) * | 2013-04-08 | 2013-08-07 | 天津虹炎科技有限公司 | Water level observation system based on Beidou satellite |
| CN106657326A (en) * | 2016-12-20 | 2017-05-10 | 南京南瑞集团公司 | Integrated long-distance water level recorder based on compass communication and compass message encoding method |
| CN112378461A (en) * | 2021-01-18 | 2021-02-19 | 长江空间信息技术工程有限公司(武汉) | River channel flow monitoring method based on Beidou satellite III and related system thereof |
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| CN115802415A (en) * | 2023-02-02 | 2023-03-14 | 易迅通科技有限公司 | Hydrology monitoring system based on big dipper communication technology |
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