CN112597264A - River patrol trajectory concentration degree analysis method based on space-time big data - Google Patents
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Abstract
The invention discloses a river patrol track concentration degree analysis method based on space-time big data. It includes the following steps, S1: setting a patrol validity intelligent judgment rule and parameters thereof on a time dimension and a space dimension; s2: preprocessing river patrol data of each time according to a preset validity judgment rule, filtering invalid river patrol records, and extracting valid river patrol data; s3: on the basis of effective river patrol data, gridding river patrol tracks associated with each river, and calculating river patrol times in each grid; s4: calculating the patrolling river concentration of each river by using a variance method; s5: and calculating the comprehensive patrolling river concentration of all rivers in the region by using a geographical concentration index method. The method solves the problem that the river patrol concentration analysis is distorted because the factors such as the examination period, the river patrol space-time distribution characteristics, the river patrol effectiveness and the like are not considered in the current river patrol concentration analysis; the method has the advantage of ensuring the reasonability of the calculation result of the concentration of the mass river patrol tracks.
Description
Technical Field
The invention relates to the technical field of big data analysis, relates to a space-time big data feature mining technology, and more particularly relates to a river patrol track concentration degree analysis method based on space-time big data, which can be applied to intelligent estuary construction and can assist managers in judging whether a river patrol concentration area is reasonable or not and guiding the managers to perform subsequent river patrol work deployment arrangement.
Background
In recent years, river growth control work is vigorously promoted in cities and counties of various provinces, a large amount of river growth patrol data are collected in the period, however, how to use a space-time big data analysis method to judge whether the river patrol concentration degree is reasonable is an urgent need of managers at all levels in river growth refinement and intelligent management;
at present, the analysis of the concentration of the patrolling river mainly comprises the analysis of two dimensions of time and space, the times of patrolling the river in time periods of year, quarter, month, week and the like are counted in the time dimension, and the times of patrolling the river in administrative divisions of province, city, county, village and the like are counted in the space dimension. However, in the time dimension, the influence of the examination period is not considered, and in the space dimension, the influence of factors such as river patrol track effectiveness, river length quantity, river distribution density and the like in the administrative division is not considered. For example, the more river reach in an administrative district, the more river length, and the more times of natural river patrol; compared with the places where the river distribution is sparse, the places where the river distribution is more dense have higher concentration degree naturally; and ineffective river patrol such as too short river patrol time, too short river patrol mileage, too far river patrol track from the riverway shoreline and the like exists. If the factors are ignored, the scientificity of the river patrol concentration calculation is greatly influenced, so that whether the river patrol concentration is reasonable or not cannot be effectively judged, and the river patrol concentration cannot be used for guiding the river patrol working deployment arrangement;
therefore, it is necessary to develop a river patrol trajectory concentration analysis method which ensures reasonableness.
Disclosure of Invention
The invention aims to provide a river patrol track concentration analysis method based on space-time big data, which ensures the reasonability of a calculation result of a mass river patrol track concentration; the problem of the concentration analysis distortion of the patrolling river caused by the fact that factors such as an examination period, the spatial and temporal distribution characteristics of the patrolling river, the effectiveness of the patrolling river and the like are not considered in the current concentration analysis of the patrolling river is solved.
In order to achieve the purpose, the technical scheme of the invention is as follows: a river patrol trajectory concentration degree analysis method based on space-time big data is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
s1: setting a patrol validity intelligent judgment rule and parameters thereof on a time dimension and a space dimension;
s2: preprocessing river patrol data of each time according to a preset validity judgment rule, filtering invalid river patrol records, and extracting valid river patrol data;
s3: on the basis of effective river patrol data, gridding river patrol tracks associated with each river, and calculating river patrol times in each grid;
s4: calculating the patrolling river concentration of each river by using a variance method;
s5: and calculating the comprehensive patrolling river concentration of all rivers in the region by using a geographical concentration index method.
In the above technical solution, in S1, the time dimension includes two determination rules: firstly, the duration of a single river patrol is more than 5 min; when the duration of one river patrol is less than or equal to 5min, judging that the river patrol is invalid;
secondly, the total times of river patrol in the previous 3 days of a single river patrol are less than 3 times; when the total times of river patrol in the previous 3 days of a single river patrol is more than or equal to 3 times, judging that the river patrol is invalid;
the spatial dimension includes two judgment rules: firstly, the mileage of a single river patrol is more than 100 meters; when the mileage of one river patrol is less than or equal to 100 meters, judging that the river patrol is invalid;
secondly, at least 20% of track points of a single river patrol fall within 2km of the river course shoreline; when less than 20% of track points of a single river patrol fall within 2 kilometers of the river course shoreline, the river patrol is judged to be invalid.
In the above technical solution, in S3, the river patrol frequency in each grid is calculated after gridding the river patrol track associated with each river, and the specific steps are as follows:
s3.1: grouping all river patrol tracks according to rivers related to the river patrol, namely one river corresponds to a plurality of river patrol records;
s3.2: uniformly dividing grids with fixed width and height according to the minimum external rectangle of the effective river patrol track associated with each river;
s3.3: circularly calculating the intersection of each grid and all effective river patrol tracks by a space intersection analysis method of the grids and the river patrol tracks to count the river patrol times in each grid;
the space intersection analysis method of the grid and the patrol river track comprises the following steps:
when the grid is intersected with the effective river patrol track, the patrol frequency in the grid is increased by one; when the grid and the effective river patrol track are not intersected, the patrol frequency in the grid is unchanged;
s3.4: and removing the grids with the river patrol frequency of 0.
In the above technical solution, in step S4, the patrol concentration of each river is calculated using the following variance formula (1); the larger the variance is, the more scattered the patrol river is; the smaller the variance is, the more concentrated the river patrol is;
in equation (1): sjThe variance of the jth river is obtained, and n is the grid number related to the jth river; a isiThe number of effective river-patrolling tracks falling in the ith grid is obtained; x is the number ofiAnd yiLongitude and latitude coordinates of the ith grid center point; x is the number ofj ’And yj ’The weighted center point longitude and latitude coordinates of all grids of the jth river are calculated in a specific manner as shown in the following formula (2):
in equation (2): n is the number of grids associated with the jth river; a isiThe number of effective river-patrolling tracks falling in the ith grid is obtained; x is the number ofiAnd yiAnd the longitude and latitude coordinates of the central point of the ith grid.
In the above technical solution, in S5, the patrol concentration ratios of all rivers in an area are calculated using the following geographical concentration ratio index calculation formula (3); the larger the index is, the more concentrated the patrol river is; the smaller the index is, the more scattered the patrol river is;
in equation (3): g is river patrol concentration index in the region, SjThe variance of the jth river is shown, and m is the number of rivers in the region.
Compared with the prior art, the invention has the following beneficial effects:
(1) rationality; whether the single river or one area patrol track is centralized or scattered is analyzed by the method; according to the method, when the concentration of the patrolling river is calculated, the influence of invalid patrolling river records is filtered, and the variance method and the geographical concentration index method are adopted on the calculation model, so that the dispersion of the patrolling river track can be more sensitively reflected, and the calculation result can correctly reflect the real concentration of the patrolling river;
(2) the practicability is high; the river patrol concentration degree is decomposed from the region dimension to the river dimension, the river patrol concentration degree of a single river can be reflected, the river patrol concentration degree in a region can also be reflected, and the method has important significance for assisting management personnel in judging whether the river patrol concentration region is reasonable or not and guiding the river patrol working deployment arrangement;
(3) according to the invention, through four effectiveness judgment rules preset in S1, the influence of invalid river patrol records on concentration calculation can be filtered; the intersection of the grids and the river patrol tracks is adopted to count the river patrol times in each small area to serve as the weight of variance calculation; the method adopts a variance formula and a geographical concentration index formula which are commonly used for calculating the concentration to quantify the concentration; the river reach system relates to 5-level river reach of county and county in province, more than 25 thousands of people exist in the country, and the village requires river patrol once per week, so that annual patrol records in the country are more than 1000 thousands, and annual patrol records in the province are more than 50 thousands; the river length system is implemented from 2017, the cumulative river patrol records of each province are over 100 thousands, and are increased year by year, the method can be applied to analysis of concentration of river patrol tracks of various river lengths, and the data amount processed by the method is in a large scale.
Drawings
FIG. 1 is a flow chart of a river patrol trajectory concentration analysis method based on space-time big data;
FIG. 2 is a flow chart of the method for gridding the river-patrol trajectory of S3 in FIG. 1 according to the present invention;
FIG. 3 is a schematic view of gridding the tour record of a single river at S3 in FIG. 1 according to the present invention;
fig. 4 is a patrol river trajectory distribution diagram of a dragon river basin in york county of Chongqing, Toyodu, 2020, used in an embodiment of the present invention;
fig. 5 is a diagram of the grading effect after gridding the route of the patrol of the dragon river used in the embodiment of the present invention.
S1, S2, S3, S4 and S5 in fig. 1 respectively represent step S1, step S2, step S3, step S4 and step S5 of the patrol trajectory concentration analysis method based on spatiotemporal big data;
s3.1, S3.2, S3.3, S3.4 in fig. 2 respectively represent step S3.1, step S3.2, step S3.3, step S3.4 of the method for calculating the number of times of the cruise in each grid;
fig. 3 is a schematic diagram of a second judgment rule for space dimension (the river patrol track cannot be too far away from the river course shoreline, that is, at least 20% of track points of a single river patrol should fall within 2km of the river course shoreline) and gridding; in fig. 3, the dotted line is an effective range line 2km away from the river course shoreline, the river patrol track inside the dotted line is effective, and the river patrol track outside the dotted line is ineffective; the number of times of river patrol falling in each grid can be counted through the horizontal and vertical grids shown in fig. 3, and the number of times of river patrol can identify the river patrol intensity in each small grid range;
fig. 4 is a graph showing experimental data of the present invention, after step S2 is executed, distinguishing an effective river patrol track from an ineffective river patrol track, wherein a solid line represents the ineffective river patrol track, and a dotted line represents the effective river patrol track;
fig. 5 shows that after the experimental data of the present invention is processed in step S3, the number of times of river patrol in each small grid is calculated, the density is divided into three levels according to the number of times of river patrol, and a darker filling color of the grid indicates a larger number of times of river patrol.
Detailed Description
The embodiments of the present invention will be described in detail with reference to the accompanying drawings, which are not intended to limit the present invention, but are merely exemplary. While the advantages of the invention will be clear and readily understood by the description.
The analysis method of the invention adopts: firstly, 4 rules of space and time dimensions are comprehensively adopted to judge the effectiveness of the patrolling river, the invalid patrolling river is eliminated, and the concentration of the patrolling river is calculated, so that the calculation deviation is avoided; analyzing the intersection of each geographical small grid and the effective river patrol track by adopting a spatial analysis method, and if the geographical small grids are intersected, adding one to the patrol frequency in the grids to quantitatively calculate the river patrol intensity; quantifying the concentration of a river and a patrolling river in a region by using the variance and the geographic concentration index;
the three points are applied to analysis of the concentration of the river-lake long patrol track, and the dispersion of the river-patrol track can be more sensitively reflected, so that the calculation result can correctly reflect the real river-patrol concentration, and the reasonability of the calculation result of the mass river-patrol track concentration is ensured;
according to the space intersection analysis method in S3.3, a plurality of GIS professional libraries are introduced during calculation, and codes are compiled for calculation; the spatial intersection analysis method is a known technology in the field, and a mature open source algorithm library can be integrated during encoding.
With reference to the accompanying drawings: a river patrol track concentration degree analysis method based on space-time big data comprises the following steps,
s1: setting a patrol validity intelligent judgment rule and parameters thereof on a time dimension and a space dimension;
s2: preprocessing river patrol data of each time according to a preset validity judgment rule, filtering invalid river patrol records, and extracting valid river patrol data;
s3: on the basis of effective river patrol data, gridding river patrol tracks associated with each river, and calculating river patrol times in each grid;
s4: calculating the patrolling river concentration of each river by using a variance method;
s5: calculating the comprehensive patrolling river concentration of all rivers in the area by using a geographical concentration index method (as shown in figure 1); the method comprehensively considers multiple factors of the patrol river in time and space dimensions, provides a scientific and effective analysis means for the patrol river concentration calculation, and can effectively ensure the reasonability of the calculation result of the mass patrol river trajectory concentration.
Further, in S1, the time dimension includes two determination rules:
the first judgment rule of the time dimension is: the river patrol duration of each time cannot be too short, a certain duration needs to be exceeded, namely the river patrol duration of each time needs to be longer than 5min, otherwise, the river patrol is judged to be invalid, and the river patrol concentration calculation is not involved;
the second judgment rule of the time dimension is: the river patrol frequency in each river length assessment period cannot be too dense, namely the total river patrol frequency in the previous 3 days of a single river patrol is less than 3 times; otherwise, judging that the river patrol is invalid, and not participating in the calculation of the river patrol concentration;
if the village river is required to patrol 4 times per month and uniformly patrol once per week, and if the patrol is concentrated in a small time range for several times, the follow-up patrol can be judged to be invalid;
the spatial dimension also includes two decision rules:
the first rule for determining the spatial dimension is: the river patrol mileage can not be too short every time, and needs to exceed a certain mileage, namely the river patrol mileage for one time is more than 100 meters; otherwise, judging that the river patrol is invalid, and not participating in the calculation of the river patrol concentration;
the second rule for determining the spatial dimension is: the river patrol track cannot be too far away from the river course shoreline, namely at least 20% of track points of a single river patrol fall within 2 kilometers of the river course shoreline, otherwise, the river patrol is judged to be invalid and does not participate in the calculation of the river patrol concentration;
and eliminating the influence of invalid patrolling on the concentration calculation of patrolling by the four preset validity judgment conditions.
Further, in S3, the river patrol frequency in each grid is calculated after gridding the river patrol track associated with each river (as shown in fig. 2), and the specific steps are as follows:
s3.1: grouping all river patrol tracks according to rivers related to the river patrol, namely one river corresponds to a plurality of river patrol records;
s3.2: as shown in fig. 3, grids with fixed width and height are uniformly divided according to the minimum circumscribed rectangle of the effective river patrol track associated with each river;
s3.3: circularly calculating the intersection of each grid and all effective river patrol tracks by a space intersection analysis method of the grids and the river patrol tracks to count the river patrol times in each grid;
the space intersection analysis method of the grid and the patrol river track comprises the following steps:
when the grid is intersected with the effective river patrol track, the patrol frequency in the grid is increased by one; when the grid and the effective river patrol track are not intersected, the patrol frequency in the grid is unchanged;
s3.4: removing the grids with the river patrol frequency of 0;
and quantifying the concentration of the patrolling river in each area by adopting the intersection of the small grids and the patrolling river tracks.
Further, in step S4, the patrol concentration ratio of each river is calculated using the following variance formula (1); the larger the variance is, the more scattered the patrol river is; the smaller the variance is, the more concentrated the river patrol is;
in equation (1): sjThe variance of the jth river is obtained, and n is the grid number related to the jth river; a isiThe number of effective river-patrolling tracks falling in the ith grid is obtained; x is the number ofiAnd yiLongitude and latitude coordinates of the ith grid center point; x is the number ofj ’And yj ’The weighted center point longitude and latitude coordinates of all grids of the jth river are calculated in a specific manner as shown in the following formula (2):
in equation (2): n is the number of grids associated with the jth river; a isiThe number of effective river-patrolling tracks falling in the ith grid is obtained; x is the number ofiAnd yiLongitude and latitude coordinates of the ith grid center point;
the river patrol concentration of a single river is quantified by using a variance formula.
Further, in S5, the patrol concentration of all rivers in the area is calculated using the following geographical concentration index calculation formula (3); the larger the index is, the more concentrated the patrol river is; the smaller the index is, the more scattered the patrol river is;
in equation (3): g is river patrol concentration index in the region, SjThe variance of the jth river is shown, and m is the number of rivers in the region;
quantifying the concentration of the patrolling river in one area by adopting a geographic index method; the concentration is divided into three levels according to the number of times of river patrol, and the problem of calculation of the river patrol concentration under different geographic scales is solved through three levels of quantization (as shown in figure 5).
Examples
The invention is explained in detail by taking the embodiment of the invention as an example, and has the guiding function for analyzing the concentration of the river patrol track in other areas.
A flowchart of an embodiment of a method for analyzing concentration of a patrol river trajectory based on space-time big data adopted in this embodiment is shown in fig. 1 and fig. 2, in this embodiment, patrol river data of a dragon river basin in Chongqing Toyowa prefecture is used as example data (shown in fig. 4), and is 2765 patrol river trajectory distribution diagrams in a dragon river basin in Chongqing Toyowa prefecture in 2020, a dotted line is an effective patrol river, and a solid line is an ineffective patrol river.
The embodiment of the method for analyzing the concentration of the river patrol track in a certain area based on space-time big data comprises the following specific steps:
s1, setting judgment parameters of 4 validity judgment rules, which are respectively:
firstly, the time for effectively patrolling the river is more than 5 minutes;
secondly, the number of effective patrol of the river is less than 3 in the first 3 days;
the effective river patrol mileage is over 100 meters;
fourthly, 20% of the river patrol track of the effective river patrol is within 2 kilometers of the river course shoreline;
s2, reading all patrol records of a dragon river basin of Toyodo county at Chongqing 2020 from a Hechang system, totaling 2765 patrol records, and calculating according to 4 preset validity judgment rules to obtain: 624 river patrol records are invalid, and 2141 river patrol records are valid;
s3, on the basis of effective river patrol data, gridding river patrol tracks associated with each river, and calculating river patrol times in each grid, wherein the specific steps are as follows:
s3.1, grouping all patrolling tracks according to rivers related to patrols, namely one river corresponds to a plurality of patrolling records, wherein 761 effective patrolling of the dragon river, 102 effective patrolling of the eagle river, 392 effective patrolling of the marmot river, 337 effective patrolling of the married river, 310 effective patrolling of the dragon river and 239 effective patrolling of the Dongjia river;
s3.2, uniformly dividing grids with fixed widths according to the minimum circumscribed rectangle of the effective river patrol track associated with each river, taking the dragon river as an example, the minimum circumscribed rectangle is (107.721639, 29.747651, 108.045639 and 29.915651), and the length and the width of each grid are both 0.002 degrees, namely about 200 meters, so that the grids can be uniformly divided into 83 x 162=13446 grids;
s3.3: circularly calculating the intersection of each grid and all effective river patrol tracks by a space intersection analysis method of the grids and the river patrol tracks to count the river patrol times in each grid;
the space intersection analysis method of the grid and the patrol river track comprises the following steps:
when the grid is intersected with the effective river patrol track, the patrol frequency in the grid is increased by one; when the grid and the effective river patrol track are not intersected, the patrol frequency in the grid is unchanged;
s3.4, removing the grids with the river patrol frequency of 0, taking the dragon river as an example, after removal, remaining 1353 grids have values, and the grading effect after meshing is shown in figure 5, wherein deeper colors indicate higher concentration;
s4, calculating the river patrol concentration ratio of each river by using a variance formula; calculating to obtain: the dragon-river variance value is 0.009377304264898788, the eagle-river variance value is 0.0004546770456277842, the crosse-river variance value is 0.0028801627494366823, the covered-married-couple-river variance value is 0.0017587730516899274, the dragon-river variance value is 0.003932220708302711, and the Dongfanghe-river variance value is 0.0024805831750711526; the results show that the eagle river is most concentrated and the dragon river is most dispersed in the river patrolling process;
s5, calculating by using a geographical concentration index calculation formula to obtain the comprehensive patrolling concentration of 52.7% of all rivers in the dragon river domain of Chongqing Toudo county in 2020.
In this embodiment, 2765 patrol records of the dragon river basin in york county of Chongqing Toyodu in 2020 are used for calculating the patrol concentration, a patrol trajectory distribution diagram is shown in fig. 4, it can be seen from fig. 4 that the dragon river patrol trajectories are distributed uniformly, the double eagle river patrol trajectories are distributed at one end of the river, the distribution is concentrated, and the fit with the final calculation result is better.
And (4) conclusion: the embodiment adopts a river patrol track concentration analysis method based on space-time big data, can solve the problem that the analysis of the river patrol concentration is distorted because the factors such as the examination period, the river patrol space-time distribution characteristics, the river patrol effectiveness and the like are not considered in the current analysis of the river patrol concentration, provides a scientific and effective analysis means for the calculation of the river patrol concentration, and can effectively ensure the reasonability of the calculation result of the mass river patrol track concentration.
Other parts not described belong to the prior art.
Claims (5)
1. A river patrol trajectory concentration degree analysis method based on space-time big data is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
s1: setting a patrol validity intelligent judgment rule and parameters thereof on a time dimension and a space dimension;
s2: preprocessing river patrol data of each time according to a preset validity judgment rule, filtering invalid river patrol records, and extracting valid river patrol data;
s3: on the basis of effective river patrol data, gridding river patrol tracks associated with each river, and calculating river patrol times in each grid;
s4: calculating the patrolling river concentration of each river by using a variance method;
s5: and calculating the comprehensive patrolling river concentration of all rivers in the region by using a geographical concentration index method.
2. The patrolling river trajectory concentration degree analysis method based on space-time big data according to claim 1, characterized in that: in S1, the time dimension includes two determination rules: firstly, the duration of a single river patrol is more than 5 min; when the duration of one river patrol is less than or equal to 5min, judging that the river patrol is invalid;
secondly, the total times of river patrol in the previous 3 days of a single river patrol are less than 3 times; when the total times of river patrol in the previous 3 days of a single river patrol is more than or equal to 3 times, judging that the river patrol is invalid;
the spatial dimension includes two judgment rules: firstly, the mileage of a single river patrol is more than 100 meters; when the mileage of one river patrol is less than or equal to 100 meters, judging that the river patrol is invalid;
secondly, at least 20% of track points of a single river patrol fall within 2km of the river course shoreline; when less than 20% of track points of a single river patrol fall within 2 kilometers of the river course shoreline, the river patrol is judged to be invalid.
3. The patrolling river trajectory concentration degree analysis method based on space-time big data according to claim 2, characterized in that: in S3, calculating the number of times of river patrol in each grid after gridding the river patrol track associated with each river, the specific steps are as follows:
s3.1: grouping all river patrol tracks according to rivers related to the river patrol, namely one river corresponds to a plurality of river patrol records;
s3.2: uniformly dividing grids with fixed width and height according to the minimum external rectangle of the effective river patrol track associated with each river;
s3.3: circularly calculating the intersection of each grid and all effective river patrol tracks by a space intersection analysis method of the grids and the river patrol tracks to count the river patrol times in each grid;
the space intersection analysis method of the grid and the patrol river track comprises the following steps:
when the grid is intersected with the effective river patrol track, the patrol frequency in the grid is increased by one; when the grid and the effective river patrol track are not intersected, the patrol frequency in the grid is unchanged;
s3.4: and removing the grids with the river patrol frequency of 0.
4. The patrolling river trajectory concentration degree analysis method based on space-time big data according to claim 3, characterized in that: in step S4, the patrol concentration ratio of each river is calculated using the following variance formula (1); the larger the variance is, the more scattered the patrol river is; the smaller the variance is, the more concentrated the river patrol is;
in equation (1): sjThe variance of the jth river is obtained, and n is the grid number related to the jth river; a isiThe number of effective river-patrolling tracks falling in the ith grid is obtained; x is the number ofiAnd yiLongitude and latitude coordinates of the ith grid center point; x is the number ofj ’And yj ’The weighted center point longitude and latitude coordinates of all grids of the jth river are calculated in a specific manner as shown in the following formula (2):
in equation (2): n is the number of grids associated with the jth river; a isiThe number of effective river-patrolling tracks falling in the ith grid is obtained; x is the number ofiAnd yiAnd the longitude and latitude coordinates of the central point of the ith grid.
5. The patrolling river trajectory concentration degree analysis method based on space-time big data according to claim 4, characterized in that: at S5, calculating the patrol concentration of all rivers in the area using the following geographical concentration index calculation formula (3); the larger the index is, the more concentrated the patrol river is; the smaller the index is, the more scattered the patrol river is;
in equation (3): g is river patrol concentration index in the region, SjThe variance of the jth river is shown, and m is the number of rivers in the region.
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| CN115689129A (en) * | 2023-01-03 | 2023-02-03 | 长江信达软件技术（武汉）有限责任公司 | Method and device for realizing river patrol effectiveness judgment and calculation based on time-space analysis |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103218672A (en) * | 2013-03-24 | 2013-07-24 | 吉林大学 | Taxi cruising action analytical method based on global position system (GPS) data grid statistic |
| US8972161B1 (en) * | 2005-11-17 | 2015-03-03 | Invent.Ly, Llc | Power management systems and devices |
| CN108932344A (en) * | 2018-07-26 | 2018-12-04 | 青海中水数易信息科技有限责任公司 | A kind of patrolling river system system and patrol river method based on mobile terminal |
| CN109583633A (en) * | 2018-11-16 | 2019-04-05 | 浩鲸云计算科技股份有限公司 | A kind of method of public transport and the route shift adjustment of Public Transport Transfer trip |
| CN111125290A (en) * | 2019-12-24 | 2020-05-08 | 广州华微明天软件技术有限公司 | Intelligent river patrol method and device based on river growth system and storage medium |
-
2021
- 2021-03-03 CN CN202110234363.1A patent/CN112597264B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8972161B1 (en) * | 2005-11-17 | 2015-03-03 | Invent.Ly, Llc | Power management systems and devices |
| CN103218672A (en) * | 2013-03-24 | 2013-07-24 | 吉林大学 | Taxi cruising action analytical method based on global position system (GPS) data grid statistic |
| CN108932344A (en) * | 2018-07-26 | 2018-12-04 | 青海中水数易信息科技有限责任公司 | A kind of patrolling river system system and patrol river method based on mobile terminal |
| CN109583633A (en) * | 2018-11-16 | 2019-04-05 | 浩鲸云计算科技股份有限公司 | A kind of method of public transport and the route shift adjustment of Public Transport Transfer trip |
| CN111125290A (en) * | 2019-12-24 | 2020-05-08 | 广州华微明天软件技术有限公司 | Intelligent river patrol method and device based on river growth system and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 张慧: ""河长制综合管理系统APP的设计与实现"", 《灌溉排水学报》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115689129A (en) * | 2023-01-03 | 2023-02-03 | 长江信达软件技术（武汉）有限责任公司 | Method and device for realizing river patrol effectiveness judgment and calculation based on time-space analysis |
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