Method, device, equipment and storage medium for intelligently pushing environmental monitoring data

Abstract

The present invention relates to the field of big data, and discloses a method, device, equipment and storage medium for intelligently pushing environmental monitoring data, which can intelligently judge target environmental monitoring and return target environmental monitoring data. The method of the present invention includes: obtaining the longitude and latitude data of the terminal, and generating a Hilbert curve graph corresponding to the longitude and latitude data of the terminal; performing a quadtree nearest common ancestor analysis on the Hilbert curve graph to obtain candidate environmental monitoring points; calculating the distance between the terminal and the candidate environmental monitoring points to obtain multiple distance values; obtaining the candidate environmental monitoring point corresponding to the minimum distance value among the multiple distance values ​​to obtain the target environmental monitoring point; obtaining operation data, performing feature extraction on the operation data to obtain data features, and constructing a decision tree through the data features; performing predictive analysis on the operation data through the decision tree to obtain the environmental monitoring point to be pushed, and pushing the environmental monitoring data corresponding to the environmental monitoring point to be pushed to the terminal.

Description

Technical Field

[0001] The present invention relates to the field of intelligent recommendation, and in particular to a method, device, equipment and storage medium for intelligently pushing environmental monitoring data. Background Art

[0002] With the development of science and technology and economy, the threat posed by environmental pollution to human living environment has become increasingly severe. Common environmental pollution includes: greenhouse effect caused by excessive carbon dioxide emissions; acid rain caused by excessive sulfur dioxide emissions; water quality deterioration, water function damage and soil structure damage caused by indiscriminate sewage discharge. It is urgent to control the emission of pollution sources.

[0003] At present, the pollutant emissions of each monitoring point are mainly monitored through the environmental monitoring system, and the monitoring data corresponding to each monitoring point is obtained. The target monitoring location is selected on the client application of the environmental monitoring system, and the server obtains and returns the target monitoring data corresponding to the target monitoring location of the client. The disadvantage of this method is that it is necessary to reselect the target environmental monitoring point closest to the location and perform multiple manual operations to obtain the target environmental monitoring data each time, and the target environmental monitoring data cannot be automatically returned. Summary of the invention

[0004] The present invention provides a method, device, equipment and storage medium for intelligently pushing environmental monitoring data, aiming to intelligently determine target environmental monitoring points and return target environmental monitoring data.

[0005] A first aspect of an embodiment of the present invention provides a method for intelligently pushing environmental monitoring data, including:

[0006] Acquire the longitude and latitude data of the terminal, and generate a Hilbert curve graph corresponding to the longitude and latitude data of the terminal;

[0007] Performing a quadtree least common ancestor analysis on the Hilbert curve graph to obtain candidate environmental monitoring points in a preset area of ​​the Hilbert curve graph and candidate environmental monitoring points in four subnode areas of the preset area;

[0008] Calculating the distance between the terminal and the candidate environmental monitoring point to obtain multiple distance values;

[0009] Obtain a candidate environmental monitoring point corresponding to the minimum distance value among the multiple distance values ​​to obtain a target environmental monitoring point;

[0010] Acquire operation data of the terminal based on historical environmental monitoring points and the target environmental monitoring points, perform feature extraction on the operation data to obtain data features, and construct a decision tree through the data features, wherein the historical environmental monitoring points are used to indicate environmental monitoring points acquired by the terminal before the target environmental monitoring points;

[0011] The operation data is predicted and analyzed through the decision tree to obtain the environmental monitoring points to be pushed, the environmental monitoring data corresponding to the environmental monitoring points to be pushed are acquired, and the environmental monitoring data corresponding to the environmental monitoring points to be pushed are pushed to the terminal.

[0012] Optionally, in a first implementation manner of the first aspect of the embodiment of the present invention, the acquiring the longitude and latitude data of the terminal and generating a Hilbert curve graph corresponding to the longitude and latitude data of the terminal includes:

[0013] Obtain the terminal's latitude and longitude data through the global positioning system;

[0014] Performing spherical coordinate conversion processing on the latitude and longitude data of the terminal to obtain first coordinate data;

[0015] Convert the first coordinate data into plane coordinate data to obtain second coordinate data;

[0016] Performing spherical projection hexagon processing on the second coordinate data based on the Fuller projection algorithm, and performing range correction processing on the second coordinate data after the spherical projection hexagon processing based on the modified Bessel function to obtain third coordinate data;

[0017] Mapping the third coordinate data to a square corresponding to the preset area level to obtain coordinate axis point data;

[0018] A Hilbert curve graph corresponding to the longitude and latitude data of the terminal is generated according to the coordinate axis point data.

[0019] Optionally, in a second implementation manner of the first aspect of the embodiment of the present invention, performing a quadtree nearest common ancestor analysis on the Hilbert curve graph to obtain candidate environmental monitoring points in a preset area of ​​the Hilbert curve graph and candidate environmental monitoring points in four subnode areas of the preset area includes:

[0020] Based on a preset quadtree nearest common ancestor algorithm, four sub-node regions of the preset region of the Hilbert curve graph are obtained;

[0021] Detecting whether there are environmental monitoring points in the preset area and the four sub-node areas;

[0022] If there are environmental monitoring points in the preset area and / or the four sub-node areas, the detected environmental monitoring points are used as candidate environmental monitoring points.

[0023] Optionally, in a third implementation manner of the first aspect of the embodiment of the present invention, after the step of detecting whether there are environmental monitoring points in the preset area and the four sub-node areas, the method for intelligently pushing environmental monitoring data further includes:

[0024] If there is no environmental monitoring point in the preset area and the four child node areas, searching for the parent area of ​​the preset area in the Hilbert curve graph;

[0025] Based on the preset quadtree nearest common ancestor algorithm, four target child node regions corresponding to the parent region are obtained;

[0026] Detect whether there is an environmental monitoring point in the parent area and four target child node areas corresponding to the parent area;

[0027] If there are environmental monitoring points in the parent area and the four target child node areas corresponding to the parent area, the detected environmental monitoring points are used as candidate environmental monitoring points.

[0028] Optionally, in a fourth implementation manner of the first aspect of the embodiment of the present invention, calculating the distance between the terminal and the candidate monitoring point to obtain multiple distance values ​​includes:

[0029] Convert the longitude and latitude data of the terminal into global positioning system coordinate cLatLng data to obtain the coordinate data of the terminal;

[0030] Acquire the longitude and latitude data of the candidate environmental monitoring point, convert the longitude and latitude data of the candidate environmental monitoring point into candidate global positioning system coordinate mLatLng data, and obtain the coordinate data of the candidate environmental monitoring point;

[0031] The coordinate data of the terminal and the coordinate data of the candidate environmental monitoring points are calculated by a preset formula to obtain a plurality of distance values ​​between the terminal and the candidate environmental monitoring points.

[0032] Optionally, in a fifth implementation manner of the first aspect of the embodiment of the present invention, extracting features from the operation data to obtain data features, and constructing a decision tree through the data features includes:

[0033] Extracting features from the operation data using a principal component analysis algorithm to obtain data features;

[0034] Calculating the information gain of each of the data features to obtain multiple information gain values;

[0035] Obtaining candidate data features corresponding to the multiple information gain values, and sorting the candidate data features corresponding to the multiple information gain values ​​in descending order of value to obtain sorted target data features;

[0036] The sorted target data features are used as nodes, the categories of the sorted target data features and the preset judgment conditions corresponding to the categories are obtained, and the preset judgment conditions are used as branch conditions to construct a decision tree.

[0037] Optionally, in a sixth implementation manner of the first aspect of the embodiment of the present invention, after predicting and analyzing the operation data through the decision tree to obtain the environmental monitoring points to be pushed, obtaining the environmental monitoring data corresponding to the environmental monitoring points to be pushed, and pushing the environmental monitoring data corresponding to the environmental monitoring points to be pushed to the terminal, the method for intelligently pushing environmental monitoring data further includes:

[0038] Identify environmental monitoring points within a preset range to obtain early warning analysis environmental monitoring points, wherein the preset range is used to indicate a range centered on the environmental monitoring point to be pushed;

[0039] Analyze the real-time environmental monitoring data of the early warning analysis environmental monitoring point by presetting early warning conditions to obtain real-time early warning information corresponding to the early warning analysis environmental monitoring point;

[0040] The real-time warning information is pushed to the terminal.

[0041] The second aspect of the embodiment of the present invention provides a device for intelligently pushing environmental monitoring data, which has the function of implementing the method for intelligently pushing environmental monitoring data provided by the first aspect above. The function can be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions, and the unit can be software and / or hardware.

[0042] The device for intelligently pushing environmental monitoring data includes:

[0043] A generating module, used to obtain the longitude and latitude data of the terminal, and generate a Hilbert curve graph corresponding to the longitude and latitude data of the terminal;

[0044] A first analysis module is used to perform a quadtree least common ancestor analysis on the Hilbert curve graph to obtain candidate environmental monitoring points in a preset area of ​​the Hilbert curve graph and candidate environmental monitoring points in four subnode areas of the preset area;

[0045] A calculation module, used to calculate the distance between the terminal and the candidate environmental monitoring point to obtain multiple distance values;

[0046] An acquisition module, used to acquire a candidate environmental monitoring point corresponding to a minimum distance value among the multiple distance values, and obtain a target environmental monitoring point;

[0047] A construction module, used to obtain operation data of the terminal based on historical environmental monitoring points and the target environmental monitoring points, perform feature extraction on the operation data to obtain data features, and construct a decision tree through the data features, wherein the historical environmental monitoring points are used to indicate environmental monitoring points obtained by the terminal before the target monitoring points;

[0048] The second analysis module is used to perform predictive analysis on the operation data through the decision tree, obtain the environmental monitoring points to be pushed, obtain the environmental monitoring data corresponding to the environmental monitoring points to be pushed, and push the environmental monitoring data corresponding to the environmental monitoring points to be pushed to the terminal.

[0049] Optionally, in a first implementation manner of the second aspect of the embodiment of the present invention, the generating module is specifically used to:

[0050] Obtain the terminal's latitude and longitude data through the global positioning system;

[0051] Performing spherical coordinate conversion processing on the latitude and longitude data of the terminal to obtain first coordinate data;

[0052] Convert the first coordinate data into plane coordinate data to obtain second coordinate data;

[0053] Performing spherical projection hexagon processing on the second coordinate data based on the Fuller projection algorithm, and performing range correction processing on the second coordinate data after the spherical projection hexagon processing based on the modified Bessel function to obtain third coordinate data;

[0054] Mapping the third coordinate data to a square corresponding to the preset area level to obtain coordinate axis point data;

[0055] A Hilbert curve graph corresponding to the longitude and latitude data of the terminal is generated according to the coordinate axis point data.

[0056] Optionally, in a second implementation of the second aspect of the embodiment of the present invention, the first analysis module is specifically used to:

[0057] Based on a preset quadtree nearest common ancestor algorithm, four sub-node regions of the preset region of the Hilbert curve graph are obtained;

[0058] Detecting whether there are environmental monitoring points in the preset area and the four sub-node areas;

[0059] If there are environmental monitoring points in the preset area and / or the four sub-node areas, the detected environmental monitoring points are used as candidate environmental monitoring points.

[0060] Optionally, in a third implementation manner of the second aspect of the embodiment of the present invention, after the first analysis module detects whether there are environmental monitoring points in the preset area and the four sub-node areas, it is further configured to:

[0061] If there is no environmental monitoring point in the preset area and the four child node areas, searching for the parent area of ​​the preset area in the Hilbert curve graph;

[0062] Based on the preset quadtree nearest common ancestor algorithm, four target child node regions corresponding to the parent region are obtained;

[0063] Detect whether there is an environmental monitoring point in the parent area and four target child node areas corresponding to the parent area;

[0064] If there are environmental monitoring points in the parent area and the four target child node areas corresponding to the parent area, the detected environmental monitoring points are used as candidate environmental monitoring points.

[0065] Optionally, in a fourth implementation manner of the second aspect of the embodiment of the present invention, the calculation module is specifically used to:

[0066] Convert the longitude and latitude data of the terminal into global positioning system coordinate cLatLng data to obtain the coordinate data of the terminal;

[0067] Acquire the longitude and latitude data of the candidate environmental monitoring point, convert the longitude and latitude data of the candidate environmental monitoring point into candidate global positioning system coordinate mLatLng data, and obtain the coordinate data of the candidate environmental monitoring point;

[0068] The coordinate data of the terminal and the coordinate data of the candidate environmental monitoring points are calculated by a preset formula to obtain a plurality of distance values ​​between the terminal and the candidate environmental monitoring points.

[0069] Optionally, in a fifth implementation of the second aspect of the embodiment of the present invention, the building module is specifically used to:

[0070] Extracting features from the operation data using a principal component analysis algorithm to obtain data features;

[0071] Calculating the information gain of each of the data features to obtain multiple information gain values;

[0072] Obtaining candidate data features corresponding to the multiple information gain values, and sorting the candidate data features corresponding to the multiple information gain values ​​in descending order of value to obtain sorted target data features;

[0073] The sorted target data features are used as nodes, the categories of the sorted target data features and the preset judgment conditions corresponding to the categories are obtained, and the preset judgment conditions are used as branch conditions to construct a decision tree.

[0074] Optionally, in a sixth implementation manner of the second aspect of the embodiment of the present invention, the device for intelligently pushing environmental monitoring data further includes:

[0075] A module for obtaining the environment monitoring point to be pushed is used to identify the environment monitoring point within a preset range to obtain the early warning analysis environment monitoring point, wherein the preset range is used to indicate the range centered on the environment monitoring point to be pushed;

[0076] A third analysis module is used to analyze the real-time environmental monitoring data of the early warning analysis environmental monitoring point through preset early warning conditions to obtain real-time early warning information corresponding to the early warning analysis environmental monitoring point;

[0077] The push module is used to push the real-time warning information to the terminal.

[0078] A third aspect of an embodiment of the present invention provides a device for intelligently pushing environmental monitoring data, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for intelligently pushing environmental monitoring data described in any of the above-mentioned embodiments is implemented.

[0079] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium, comprising computer-readable instructions. When the computer-readable instructions are executed on a computer, the computer executes the method for intelligently pushing environmental monitoring data as described in any of the above embodiments.

[0080] Compared with the prior art, in the technical solution provided by the embodiment of the present invention, the latitude and longitude data of the terminal are obtained, and a Hilbert curve graph corresponding to the latitude and longitude data of the terminal is generated; a quadtree nearest common ancestor analysis is performed on the Hilbert curve graph to obtain candidate environmental monitoring points; the distance between the terminal and the candidate environmental monitoring points is calculated to obtain multiple distance values; the candidate environmental monitoring point corresponding to the minimum distance value among the multiple distance values ​​is obtained to obtain the target environmental monitoring point; operation data is obtained, feature extraction is performed on the operation data to obtain data features, and a decision tree is constructed through the data features; the operation data is predicted and analyzed through the decision tree to obtain the environmental monitoring points to be pushed, and the environmental monitoring data corresponding to the environmental monitoring points to be pushed are pushed to the terminal. The embodiment of the present invention adopts a spatial point indexing algorithm to obtain the best environmental monitoring point, performs location search according to regional coverage, improves retrieval efficiency, records the operation trajectory of the terminal and summarizes the historical data feature information of the operation trajectory, and then adopts a decision tree model to analyze and infer the best environmental monitoring point data that the terminal needs to obtain next time, realizes intelligent push, avoids the process operation of the terminal outputting the selection interface of the best environmental monitoring point, reduces the interaction between the terminal and the server, improves the user experience and the work efficiency of the terminal and the server, and can intelligently judge the target environmental monitoring point and return the target environmental monitoring data. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 A schematic diagram of an embodiment of a method for intelligently pushing environmental monitoring data in an embodiment of the present invention;

[0082] Figure 2 A schematic diagram of another embodiment of the method for intelligently pushing environmental monitoring data in an embodiment of the present invention;

[0083] Figure 3 A schematic diagram of an embodiment of a device for intelligently pushing environmental monitoring data in an embodiment of the present invention;

[0084] Figure 4 It is a schematic diagram of another embodiment of the device for intelligently pushing environmental monitoring data in an embodiment of the present invention;

[0085] Figure 5 This is a schematic diagram of an embodiment of a device for intelligently pushing environmental monitoring data in an embodiment of the present invention. DETAILED DESCRIPTION

[0086] The embodiment of the present invention provides a method, device, equipment and storage medium for intelligently pushing environmental monitoring data, which is used to use a spatial point indexing algorithm to obtain the best environmental monitoring point, perform location search according to regional coverage, improve retrieval efficiency, and record the operation trajectory of the terminal and summarize the historical data feature information of the operation trajectory, and then use a decision tree model to analyze and infer the best environmental monitoring data that the terminal needs to obtain next time, so as to realize intelligent push, avoid the process operation of the terminal outputting the selection interface of the best environmental monitoring point, reduce the interaction between the terminal and the server, improve the user experience and the work efficiency of the terminal and the server, and can intelligently judge the target environmental monitoring point and return the target environmental monitoring data.

[0087] In order to enable those skilled in the art to better understand the solutions of the present invention, the embodiments of the present invention will be described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0088] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described here can be implemented in an order other than the content illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. The division of modules that appear in the present invention is only a logical division. There may be other division methods when implemented in actual applications, such as multiple modules can be combined or integrated in another system, or some features can be ignored or not executed.

[0089] See also Figure 1 , a flowchart of a method for intelligently pushing environmental monitoring data provided by an embodiment of the present invention, the following is an example of an intelligent method for pushing environmental monitoring data provided by the present invention, the method is executed by a computer device, the computer device can be a server or a terminal, the present invention does not limit the type of the execution subject, specifically including:

[0090] 101. Obtain the longitude and latitude data of the terminal, and generate a Hilbert curve graph corresponding to the longitude and latitude data of the terminal.

[0091] Click on the relevant application on the terminal to trigger the positioning instruction. The terminal starts the global positioning system or other positioning tools according to the positioning instruction to obtain the longitude and latitude of the terminal's location (that is, the longitude and latitude data of the terminal). The terminal sends the longitude and latitude data to the server. The server receives the longitude and latitude data of the terminal and converts the longitude and latitude data of the terminal into global positioning system (GPS) coordinates s2LatLng through a preset multi-dimensional space point index S2 algorithm. The conversion of coordinates s2LatLn is implemented as follows: S2LatLng s2LatLng=S2LatLng.fromDegrees(lat, lng). According to the coordinates s2LatLng, the area identification code Cell ID data corresponding to the longitude and latitude data of the terminal is obtained. The acquisition of the Cell ID data corresponding to the longitude and latitude data of the terminal can be implemented in the following way: CellId cId=S2CellId.fromLatLng(s2LatLng).

[0092] By using the multi-dimensional spatial point indexing algorithm of Google S2, the location search is performed according to the area coverage to improve the retrieval efficiency.

[0093] Furthermore, the above step 101 may include: acquiring the latitude and longitude data of the terminal through a global positioning system; performing spherical coordinate conversion processing on the latitude and longitude data of the terminal to obtain first coordinate data; converting the first coordinate data into plane coordinate data to obtain second coordinate data; performing spherical projection hexagon processing on the second coordinate data based on a Fuller projection algorithm, and performing range correction processing on the second coordinate data after spherical projection hexagon processing based on a modified Bessel function to obtain third coordinate data; mapping the third coordinate data to a square corresponding to a preset area level to obtain coordinate axis point data; and generating a Hilbert curve graph corresponding to the latitude and longitude data of the terminal according to the coordinate axis point data.

[0094] The preset multi-dimensional space point index S2 algorithm is used to perform spherical coordinate conversion processing, plane coordinate data conversion, and mapping to the square corresponding to the preset area level on the longitude and latitude data of the terminal. The preset area level is the level corresponding to the preset area in the Hilbert curve diagram. The spherical hexagonal projection of the second coordinate data is realized by adopting the Fuller projection algorithm; since the hexagon has fewer corners, its six sides can also connect with other hexagons, and the more hexagons, the better, the finer the granularity, the closer to the sphere, and the accuracy of the spherical projection correction can be improved. Therefore, the second coordinate data is processed by spherical projection hexagon, and the preset multi-dimensional space point index S2 algorithm is optimized by performing spherical projection hexagon processing on the second coordinate data based on the Fuller projection algorithm. The second coordinate data after the spherical hexagonal projection is corrected by the modified Bessel function to ensure that the correction operation efficiency is improved without affecting the computing performance.

[0095] 102. Perform a quadtree least common ancestor analysis on the Hilbert curve graph to obtain candidate environmental monitoring points in a preset area of ​​the Hilbert curve graph and candidate environmental monitoring points in four subnode areas of the preset area.

[0096] The server retrieves the candidate environmental monitoring points in the same area Cell as the terminal and the four candidate environmental monitoring points in the four child node areas Cell in the quadtree of the same area Cell as the terminal through the quadtree nearest common ancestor algorithm in the preset multi-dimensional space point index S2 algorithm, and obtains the candidate environmental monitoring points, and the number of candidate environmental monitoring points includes one or more. This can be achieved in the following way: S2LatLngs 2LatLng=S2LatLng.fromDegrees(lat, lng); CellId cId=S2CellId.fromLatLng(s2LatLng).parent(level); S2CellId[]output=NewS2CellId[4]; cId.getEdgeNeighbors(output). The quadtree nearest common ancestor algorithm is used to conveniently, quickly and accurately obtain the candidate environmental monitoring points. Among them, the number of candidate environmental monitoring points includes one or more.

[0097] Furthermore, the above step 102 may include: obtaining four sub-node areas of the preset area of ​​the Hilbert curve diagram based on the preset quadtree nearest common ancestor algorithm; detecting whether there are environmental monitoring points in the preset area and the four sub-node areas; if there are environmental monitoring points in the preset area and / or the four sub-node areas, then using the detected environmental monitoring points as candidate environmental monitoring points.

[0098] For example: use the findnode() function to find out whether the node to be queried exists. If the node to be queried exists, use the LCA() function to find the latest common ancestor. Obtain two area identification CellIDs (encodings of the terminal signal coverage area) of two nodes on the same branch of the quadtree, perform XOR on the two CellIDs, and obtain location information represented by different binary bits; determine whether there is an ancestral relationship between the candidate nodes corresponding to the location information represented by different binary bits; if there is an ancestral relationship, obtain different positions of the highest bit on the left of the candidate node, and determine whether the msbPos corresponding to different positions of the highest bit on the left of the candidate node conform to the preset rules; if so, output the level value of the most recent common ancestor, and obtain the position and direction of the preset area Cell in the preset area hierarchy according to the level value of the most recent common ancestor; obtain the four child node area Cells of the preset area Cell according to the position and direction of the preset area Cell in the preset area hierarchy, detect whether there are environmental monitoring points in the preset area Cell and the four child node area Cells, and if there are environmental monitoring points in the preset area Cell and / or the four child node area Cells, obtain the environmental monitoring points of the preset area Cell and / or the four child node area Cells to obtain candidate environmental monitoring points.

[0099] Furthermore, after the above-mentioned detection of whether there are environmental monitoring points in the preset area and the four child node areas, it can include: if there are no environmental monitoring points in the preset area and the four child node areas, searching the parent area of ​​the preset area in the Hilbert curve graph; obtaining the four target child node areas corresponding to the parent area based on the preset quadtree nearest common ancestor algorithm; detecting whether there are environmental monitoring points in the parent area and the four target child node areas corresponding to the parent area; if there are environmental monitoring points in the parent area and the four target child node areas corresponding to the parent area, taking the detected environmental monitoring points as candidate environmental monitoring points.

[0100] The server can identify environmental monitoring points by identifying environmental monitoring sensors. The server identifies whether there are environmental monitoring sensors in the preset area or in the four sub-node areas. When no environmental monitoring sensors are detected in the preset area Cell and the four sub-node area Cells, the parent area Cell can be used to obtain the four target sub-node area Cells corresponding to the parent area Cell according to the parent area Cell, and detect whether there are environmental monitoring points in the parent area and the four target sub-node areas corresponding to the parent area, thereby obtaining candidate environmental monitoring points. The parent area Cell can be obtained by changing the level to level-1.

[0101] 103. Calculate the distance between the terminal and the candidate environmental monitoring point to obtain multiple distance values.

[0102] After obtaining the candidate environmental monitoring points, the server obtains the longitude and latitude corresponding to each of the candidate environmental monitoring points, converts the longitude and latitude of the candidate environmental monitoring points into a one-dimensional array or a two-dimensional array, and converts the longitude and latitude data of the terminal into a one-dimensional array or a two-dimensional array, and obtains multiple distance values ​​(one distance between a candidate monitoring point and the terminal corresponds to one distance value) by calculating the one-dimensional array (or two-dimensional array) of the terminal and the one-dimensional array (or two-dimensional array) of the candidate monitoring points, that is, the distance between the location of the candidate environmental monitoring point and the location of the terminal. The distance value is further calculated so that the target environmental monitoring point can be accurately obtained later.

[0103] Furthermore, the above-mentioned step 103 may include: converting the longitude and latitude data of the terminal into global positioning system coordinate cLatLng data to obtain the coordinate data of the terminal; obtaining the longitude and latitude data of the candidate environmental monitoring point, converting the longitude and latitude data of the candidate environmental monitoring point into multiple candidate global positioning system coordinate mLatLng data to obtain the coordinate data of the candidate environmental monitoring point; calculating the coordinate data of the terminal and the coordinate data of the candidate environmental monitoring point by a preset formula to obtain multiple distance values ​​between the terminal and the candidate environmental monitoring point.

[0104] The server obtains the longitude and latitude corresponding to each candidate environmental monitoring point, converts the longitude and latitude corresponding to each candidate environmental monitoring point into GPS coordinates mLatLng, and obtains multiple second coordinates, which are implemented as follows: S2LatLng mLatLng=S2LatLng.fromDegrees(lat, lng), converts the longitude and latitude of the terminal into GPS coordinates cLatLng, and obtains the first coordinate, which is implemented as follows: S2LatLng cLatLng=S2LatLng.fromDegrees(lat, lng), and calculates the distance values ​​between the first coordinate and the multiple second coordinates respectively through the preset formula, and obtains multiple distance values ​​between the location of the terminal and the location of the candidate environmental monitoring point. Among them, the preset formula can be the Haversine formula: R is the radius of the earth, and The Haversine formula is used to quickly and accurately calculate the straight-line distance between two longitudes and latitudes (ie, the terminal and the candidate environmental monitoring point).

[0105] 104. Obtain a candidate environmental monitoring point corresponding to a minimum distance value among multiple distance values ​​to obtain a target environmental monitoring point.

[0106] The server compares the sizes of multiple distance values, takes the candidate environmental monitoring point with the smallest distance value as the target environmental monitoring point, obtains the environmental monitoring data of the target environmental monitoring point, and analyzes the environmental type in the environmental monitoring data, such as sewage discharge, and calls the corresponding environmental analysis system to analyze the environmental monitoring data and generate a visual chart or analysis report. The visual chart or analysis report is returned to the terminal as the current environmental monitoring data currently returned, and the current environmental monitoring data of the target environmental monitoring point obtained by this operation is displayed on the terminal. The current environmental monitoring data is obtained so that the environmental monitoring data of the target environmental monitoring point obtained this time can be presented on the terminal.

[0107] 105. Obtain operation data of the terminal based on historical environmental monitoring points and target environmental monitoring points, perform feature extraction on the operation data to obtain data features, and construct a decision tree through the data features. The historical environmental monitoring points are used to indicate the environmental monitoring points obtained by the terminal before the target environmental monitoring points.

[0108] The server detects the time period for acquiring the target environmental monitoring point, retrieves and filters all environmental monitoring points stored in the terminal according to the time period to obtain an initial environmental monitoring point, deletes the initial environmental monitoring point from all environmental monitoring points to obtain a historical environmental monitoring point, and reads the historical environmental monitoring point data of the historical environmental monitoring point and the target environmental monitoring point data of the target monitoring point, the historical environmental monitoring point data is the log information of operations such as acquisition, detection and analysis of the historical environmental monitoring point in the terminal, and the distance information between the historical environmental monitoring point and the terminal, the target environmental monitoring point data is the log information of operations such as acquisition, detection and analysis of the target environmental monitoring point in the terminal, and the distance information between the target environmental monitoring point and the terminal, the historical environmental monitoring point data and the target environmental monitoring point data are operation data of the terminal based on the historical environmental monitoring point and the target environmental monitoring point, feature extraction is performed on the operation data through a linear discriminant analysis algorithm or a singular value decomposition algorithm or other feature extraction algorithm to obtain data features, the data features include but are not limited to usage frequency and geographic location information. The first layer of the decision tree is created based on whether the geographic location information in the data features is in the same preset area as the terminal location; the second layer of the decision tree is constructed based on whether the usage frequency is greater than the preset threshold; the third layer of the decision tree is constructed based on whether the distance value between the geographic location information in the data features and the terminal location is the minimum, and a decision tree is obtained. By constructing a decision tree, historical environmental monitoring points and target environmental monitoring points can be clearly and quickly classified and predicted.

[0109] 106. Perform predictive analysis on the operation data through a decision tree to obtain the environmental monitoring points to be pushed, obtain the environmental monitoring data corresponding to the environmental monitoring points to be pushed, and push the environmental monitoring data corresponding to the environmental monitoring points to be pushed to the terminal.

[0110] The server analyzes and predicts the operation data through the created decision tree, and obtains the environmental monitoring point corresponding to the user's usage habits or the most likely to be consulted, and the operation data and environmental monitoring data corresponding to the environmental monitoring point. When the user opens the terminal at the current terminal location, the terminal immediately displays the environmental monitoring point to be pushed closest to the current terminal location and the environmental monitoring data corresponding to the environmental monitoring point to be pushed. There is no need to obtain the longitude and latitude data of the current terminal location, and then calculate Cellid and nearby Cellid based on the longitude and latitude data to obtain the environmental monitoring point closest to the current terminal location. It can be directly obtained from the operation data corresponding to the environmental monitoring point to be pushed obtained by analyzing and predicting the decision tree stored in the database. Record the operation trajectory of the terminal, summarize the historical data feature information, and then use the decision tree model to analyze and infer the best environmental monitoring point and the environmental monitoring data corresponding to the best environmental monitoring point that the terminal needs to obtain next time, so as to achieve intelligent push, avoid the process operation of the terminal application outputting the selection interface of the environmental monitoring point location to the user, reduce the interaction between the terminal and the server, and improve the user experience and the work efficiency of the terminal and the server.

[0111] Furthermore, after the above step 106, it can include: identifying environmental monitoring points within a preset range to obtain early warning analysis environmental monitoring points, the preset range is used to indicate the range centered on the environmental monitoring point to be pushed; analyzing the real-time environmental monitoring data of the early warning analysis environmental monitoring point through preset early warning conditions to obtain real-time early warning information corresponding to the early warning analysis environmental monitoring point; and pushing the real-time early warning information to the terminal.

[0112] The preset range is a circular range with a certain radius centered on the environmental monitoring point to be pushed, or an elliptical range centered on the environmental monitoring point to be pushed, or a regular polygonal range centered on the environmental monitoring point to be pushed; the preset warning condition can be a threshold range of indicators corresponding to each environmental monitoring type. The server analyzes the real-time environmental monitoring data of the environmental monitoring points (i.e., early warning analysis environmental monitoring points) within the preset range and pushes the real-time early warning monitoring data and the early warning analysis environmental monitoring points corresponding to the real-time early warning monitoring data (i.e., real-time early warning information, including real-time early warning monitoring data and early warning analysis environmental monitoring points corresponding to the real-time early warning monitoring data) to the terminal, so as to timely know the abnormalities of the early warning analysis environmental monitoring points and the association and influence of the early warning analysis environmental monitoring points on the real-time environmental monitoring data.

[0113] The embodiment of the present invention obtains the best environmental monitoring point by adopting a spatial point indexing algorithm, performs location search according to regional coverage, improves retrieval efficiency, records the operation trajectory of the terminal and summarizes the historical data feature information of the operation trajectory, and then adopts a decision tree model to analyze and infer the best environmental monitoring data that the terminal needs to obtain next time, realizes intelligent push, avoids the process operation of the terminal outputting the selection interface of the best environmental monitoring point, reduces the interaction between the terminal and the server, improves the user experience and the work efficiency of the terminal and the server, and can intelligently judge the target environmental monitoring point and return the target environmental monitoring data.

[0114] See also Figure 2 Another embodiment of the method for intelligently pushing environmental monitoring data in the embodiment of the present invention includes:

[0115] 201. Obtain the longitude and latitude data of the terminal, and generate a Hilbert curve graph corresponding to the longitude and latitude data of the terminal.

[0116] 202. Perform a quadtree least common ancestor analysis on the Hilbert curve graph to obtain candidate environmental monitoring points in a preset area of ​​the Hilbert curve graph and candidate environmental monitoring points in four subnode areas of the preset area.

[0117] 203. Calculate the distance between the terminal and the candidate environmental monitoring point to obtain multiple distance values.

[0118] 204. Obtain a candidate environmental monitoring point corresponding to a minimum distance value among multiple distance values ​​to obtain a target environmental monitoring point.

[0119] In the embodiment of the present invention, the methods of 201 to 204 may refer to 101 to 104 and will not be described in detail here.

[0120] 205. Obtain operation data of the terminal based on historical environmental monitoring points and target environmental monitoring points, where the historical environmental monitoring points are used to indicate environmental monitoring points acquired by the terminal before the target environmental monitoring points.

[0121] The server obtains all environmental monitoring points stored in the terminal and the operation log information corresponding to all environmental monitoring points, identifies the time period in which the target monitoring point is obtained and operated, obtains the start time of the time period and the time information of the time point before the start time, traverses the operation log information according to the time information or retrieves the hash table constructed by the operation log information through the hash value, obtains the historical environmental monitoring point corresponding to the time information and the historical environmental monitoring point data of the historical environmental monitoring point, and obtains the target environmental monitoring point data of the target environmental monitoring point, the historical environmental monitoring point data and the target environmental monitoring point data are the operation data of the terminal based on the historical environmental monitoring point and the target environmental monitoring point, and the operation data includes the operation log information and the basic information and distance information of the historical environmental monitoring point and the target environmental monitoring point. By obtaining the operation data, it is convenient to analyze the operation data later to obtain the environmental monitoring point to be pushed and the environmental monitoring data corresponding to the environmental monitoring point to be pushed.

[0122] 206. The principal component analysis algorithm is used to extract the features of the operation data to obtain data features.

[0123] The server extracts features from the operation data by combining the principal component analysis algorithm's features of making the data set easier to use, reducing the algorithm's computational overhead, removing noise, making the results easy to understand, and having no parameter restrictions, so as to improve the operational efficiency and accuracy of feature extraction. The data features include, but are not limited to, the operation frequency and the location information of the environmental monitoring points.

[0124] 207. Calculate the information gain of each data feature to obtain multiple information gain values.

[0125] After the server obtains the data features, it calculates the information gain value of each data feature using the information gain C4.5 algorithm. The size of the information gain value represents the speed at which the data feature develops toward a subset with higher purity. Purity is used to indicate the degree of uncertainty in the subset obtained by dividing the data features.

[0126] 208. Obtain candidate data features corresponding to multiple information gain values, and sort the candidate data features corresponding to the multiple information gain values ​​in descending order of value to obtain sorted target data features.

[0127] Since the size of the information gain value represents the speed at which the data feature develops toward a subset with higher purity, the server sorts the candidate data features in order of information gain value from large to small, and can quickly and accurately analyze the sorted target data features to obtain qualified environmental monitoring points to be pushed.

[0128] 209. Take the sorted target data features as nodes, obtain the categories of the sorted target data features and the preset judgment conditions corresponding to the categories, and take the preset judgment conditions as branch conditions to construct a decision tree.

[0129] The server uses the first sorted target data feature as the root node, and establishes a child node by the features corresponding to the category of the sorted target data feature of the root node and the preset judgment conditions corresponding to the category, and then establishes a child node by the features corresponding to the category of the sorted target data feature of the child node and the preset judgment conditions corresponding to the category, and so on, until the target data feature meets the preset conditions, and the preset conditions are that the information gain of the sorted target data feature is less than or equal to the preset value, or if the information gain of the sorted target data feature is greater than the preset value, the last sorted target data feature in the sorted target data features is used as the last child node to obtain a decision tree. By constructing a decision tree to analyze and infer the best monitoring point data for the next terminal, intelligent push is realized. For example: after sorting the information gain, the target data features are whether the terminal is in the same area Cell, whether the usage frequency is greater than the preset threshold, and whether the distance to the terminal is the closest. Then, whether the terminal is in the same area Cell is used as the root node, whether the usage frequency is greater than the preset threshold is used as the child node of the second layer, and whether the distance to the terminal is the closest is used as the child node of the third layer. Yes or no is used as the branch condition to obtain the initial decision tree, and the initial decision tree is pruned, continuous value processed, and missing value processed to obtain the final decision tree.

[0130] 210. Predictively analyze the operation data through a decision tree to obtain the environmental monitoring points to be pushed, obtain the environmental monitoring data corresponding to the environmental monitoring points to be pushed, and push the environmental monitoring data corresponding to the environmental monitoring points to be pushed to the terminal.

[0131] In the embodiment of the present invention, the method of 210 may refer to 106 and will not be described in detail here.

[0132] The embodiment of the present invention obtains sorted target data features by sorting candidate data features from large to small according to information gain values, and constructs a decision tree based on the sorted target data features. This can not only intelligently determine target environmental monitoring points and return target environmental monitoring data, but also improve operational efficiency and quickly and effectively perform classification and prediction.

[0133] The above describes the method for intelligently pushing environmental monitoring data in the embodiment of the present invention. The following describes the device for intelligently pushing environmental monitoring data in the embodiment of the present invention. Figure 3 An embodiment of the device for intelligently pushing environmental monitoring data in an embodiment of the present invention includes:

[0134] A generating module 301 is used to obtain the longitude and latitude data of the terminal and generate a Hilbert curve graph corresponding to the longitude and latitude data of the terminal;

[0135] The first analysis module 302 is used to perform a quadtree least common ancestor analysis on the Hilbert curve graph to obtain candidate environmental monitoring points in a preset area of ​​the Hilbert curve graph and candidate environmental monitoring points in four subnode areas of the preset area;

[0136] A calculation module 303 is used to calculate the distance between the terminal and the candidate environmental monitoring point to obtain multiple distance values;

[0137] An acquisition module 304 is used to acquire a candidate environmental monitoring point corresponding to a minimum distance value among multiple distance values ​​to obtain a target environmental monitoring point;

[0138] A construction module 305 is used to obtain operation data of the terminal based on historical environmental monitoring points and target environmental monitoring points, extract features from the operation data to obtain data features, and construct a decision tree based on the data features, wherein the historical environmental monitoring points are used to indicate environmental monitoring points obtained by the terminal before the target environmental monitoring points;

[0139] The second analysis module 306 is used to perform predictive analysis on the operation data through a decision tree, obtain the environmental monitoring points to be pushed, obtain the environmental monitoring data corresponding to the environmental monitoring points to be pushed, and push the environmental monitoring data corresponding to the environmental monitoring points to be pushed to the terminal.

[0140] The functional implementation of each module in the above-mentioned device for intelligently pushing environmental monitoring data corresponds to the steps in the above-mentioned method embodiment for intelligently pushing environmental monitoring data, and its functions and implementation processes will not be repeated here one by one.

[0141] The embodiment of the present invention improves retrieval efficiency by performing location search based on regional coverage, records the operation trajectory of the terminal and summarizes the historical data feature information of the operation trajectory, and then uses a decision tree model to analyze and infer the optimal environmental monitoring data that the terminal needs to obtain next time, thereby realizing intelligent push, avoiding the process operation of the terminal outputting the selection interface of the optimal environmental monitoring point, reducing the interaction between the terminal and the server, improving the user experience and the work efficiency of the terminal and the server, and being able to intelligently determine the target environmental monitoring point and return the target environmental monitoring data.

[0142] See also Figure 4 Another embodiment of the device for intelligently pushing environmental monitoring data in the embodiment of the present invention includes:

[0143] A generating module 301 is used to obtain the longitude and latitude data of the terminal and generate a Hilbert curve graph corresponding to the longitude and latitude data of the terminal;

[0144] The first analysis module 302 is used to perform a quadtree least common ancestor analysis on the Hilbert curve graph to obtain candidate environmental monitoring points in a preset area of ​​the Hilbert curve graph and candidate environmental monitoring points in four sub-node areas of the preset area;

[0145] A calculation module 303 is used to calculate the distance between the terminal and the candidate environmental monitoring point to obtain multiple distance values;

[0146] An acquisition module 304 is used to acquire a candidate environmental monitoring point corresponding to a minimum distance value among multiple distance values ​​to obtain a target environmental monitoring point;

[0147] The operation data acquisition module 3051 is used to acquire the operation data of the terminal based on the historical environment monitoring points and the target environment monitoring points, where the historical environment monitoring points are used to indicate the environment monitoring points acquired by the terminal before the target environment monitoring points;

[0148] Extraction module 3052, used for extracting features from the operation data through principal component analysis algorithm to obtain data features;

[0149] An information gain calculation module 3053 is used to calculate the information gain of each data feature to obtain multiple information gain values;

[0150] A sorting module 3054 is used to obtain candidate data features corresponding to multiple information gain values, and sort the candidate data features corresponding to the multiple information gain values ​​in descending order to obtain sorted target data features;

[0151] A creation module 3055 is used to use the sorted target data features as nodes, obtain the categories of the sorted target data features and the preset judgment conditions corresponding to the categories, and use the preset judgment conditions as branch conditions to construct a decision tree;

[0152] The second analysis module 306 is used to perform predictive analysis on the operation data through a decision tree, obtain the environmental monitoring points to be pushed, obtain the environmental monitoring data corresponding to the environmental monitoring points to be pushed, and push the environmental monitoring data corresponding to the environmental monitoring points to be pushed to the terminal.

[0153] Optionally, the generating module 301 is specifically used for:

[0154] Acquire the longitude and latitude data of the terminal through the global positioning system; perform spherical coordinate conversion processing on the longitude and latitude data of the terminal to obtain first coordinate data;

[0155] Convert the first coordinate data into plane coordinate data to obtain second coordinate data;

[0156] Performing spherical projection hexagon processing on the second coordinate data based on the Fuller projection algorithm, and performing range correction processing on the second coordinate data after spherical projection hexagon processing based on the modified Bessel function to obtain third coordinate data;

[0157] Mapping the third coordinate data to the square corresponding to the preset area level to obtain the coordinate axis point data;

[0158] Generate a Hilbert curve corresponding to the longitude and latitude data of the terminal according to the coordinate axis point data.

[0159] Optionally, the first analysis module 302 is specifically used for:

[0160] Based on the preset quadtree nearest common ancestor algorithm, four sub-node areas of the preset area of ​​the Hilbert curve graph are obtained;

[0161] Detect whether there are environmental monitoring points in the preset area and the four sub-node areas;

[0162] If there are environmental monitoring points in the preset area and / or the four sub-node areas, the detected environmental monitoring points are used as candidate environmental monitoring points.

[0163] Optionally, after the first analysis module 302 detects whether there are environmental monitoring points in the preset area and the four sub-node areas, it is further configured to:

[0164] If there are no environmental monitoring points in the preset area and the four child node areas, then find the parent area of ​​the preset area in the Hilbert curve graph;

[0165] Based on the preset quadtree nearest common ancestor algorithm, the four target child node areas corresponding to the parent area are obtained;

[0166] Detect whether there are environmental monitoring points in the parent area and the four target sub-node areas corresponding to the parent area;

[0167] If there are environmental monitoring points in the parent area and the four target sub-node areas corresponding to the parent area, the detected environmental monitoring points are used as candidate environmental monitoring points.

[0168] Optionally, the calculation module 303 is specifically used for:

[0169] Convert the longitude and latitude data of the terminal into the global positioning system coordinate cLatLng data to obtain the coordinate data of the terminal;

[0170] Acquire the longitude and latitude data of the candidate environmental monitoring point, convert the longitude and latitude data of the candidate environmental monitoring point into candidate global positioning system coordinate mLatLng data, and obtain the coordinate data of the candidate environmental monitoring point;

[0171] The coordinate data of the terminal and the coordinate data of the candidate environmental monitoring points are calculated by a preset formula to obtain multiple distance values ​​between the terminal and the candidate environmental monitoring points.

[0172] Optionally, the device for intelligently pushing environmental monitoring data also includes:

[0173] The module for obtaining the environment monitoring points to be pushed is used to identify the environment monitoring points within a preset range to obtain the early warning analysis environment monitoring points, and the preset range is used to indicate the range centered on the environment monitoring points to be pushed;

[0174] The third analysis module is used to analyze the real-time environmental monitoring data of the early warning analysis environmental monitoring point through the preset early warning conditions to obtain the real-time early warning information corresponding to the early warning analysis environmental monitoring point;

[0175] The push module is used to push real-time warning information to the terminal.

[0176] The functional implementation of each module in the above-mentioned device for intelligently pushing environmental monitoring data corresponds to the steps in the above-mentioned method embodiment for intelligently pushing environmental monitoring data, and its functions and implementation processes will not be repeated here one by one.

[0177] The embodiment of the present invention improves retrieval efficiency by performing location search based on regional coverage, records the operation trajectory of the terminal and summarizes the historical data feature information of the operation trajectory, and then uses a decision tree model to analyze and infer the optimal environmental monitoring data that the terminal needs to obtain next time, thereby realizing intelligent push, avoiding the process operation of the terminal outputting the selection interface of the optimal environmental monitoring point, reducing the interaction between the terminal and the server, improving the user experience and the work efficiency of the terminal and the server, and being able to intelligently determine the target environmental monitoring point and return the target environmental monitoring data.

[0178] above Figure 3 to Figure 4 The apparatus for intelligently pushing environmental monitoring data in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The device for intelligently pushing environmental monitoring data in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0179] Figure 5It is a structural diagram of a device for intelligently pushing environmental monitoring data provided by an embodiment of the present invention. The device 500 for intelligently pushing environmental monitoring data may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 501 (for example, one or more processors) and a memory 509, and one or more storage media 508 (for example, one or more massive storage devices) for storing application programs 507 or data 506. Among them, the memory 509 and the storage medium 508 can be short-term storage or permanent storage. The program stored in the storage medium 508 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the sign-in management device. Furthermore, the processor 501 can be configured to communicate with the storage medium 508 to execute a series of instruction operations in the storage medium 508 on the device 500 for intelligently pushing environmental monitoring data.

[0180] The device 500 for intelligently pushing environmental monitoring data may also include one or more power supplies 502, one or more wired or wireless network interfaces 503, one or more input and output interfaces 504, and / or one or more operating systems 505, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 5 The device structure for intelligently pushing environmental monitoring data shown in the figure does not constitute a limitation on the device for intelligently pushing environmental monitoring data, and may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently. The processor 501 can execute the functions of the generation module 301, the first analysis module 302, the calculation module 303, the acquisition module 304, the construction module 305, the operation data acquisition module 3051, the extraction module 3052, the information gain calculation module 3053, the sorting module 3054, the construction module 3055 and the second analysis module 306 in the above-mentioned embodiment.

[0181] Combine the following Figure 5 A detailed introduction to the various components of the equipment that intelligently pushes environmental monitoring data:

[0182] Processor 501 is the control center of the device for intelligently pushing environmental monitoring data, and can be processed according to the method for intelligently pushing environmental monitoring data. Processor 501 uses various interfaces and lines to connect the various parts of the entire device for intelligently pushing environmental monitoring data, and executes various functions and processes data of the device for intelligently pushing environmental monitoring data by running or executing software programs and / or modules stored in memory 509, and calling data stored in memory 509, thereby realizing the functions of intelligently judging target environmental monitoring and returning target environmental monitoring data. Storage medium 508 and memory 509 are both carriers for storing data. In the embodiment of the present invention, storage medium 508 can refer to an internal memory with a small storage capacity but a fast speed, and memory 509 can be an external memory with a large storage capacity but a slow storage speed.

[0183] The memory 509 can be used to store software programs and modules. The processor 501 executes various functional applications and data processing of the device 500 for intelligently pushing environmental monitoring data by running the software programs and modules stored in the memory 509. The memory 509 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (obtaining the latitude and longitude data of the terminal, etc.); the data storage area may store data created according to the use of the sign-in management device (performing a quadtree to find the nearest common ancestor analysis on the Hilbert curve graph, obtaining candidate environmental monitoring points, etc.). In addition, the memory 509 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices. The method program for intelligently pushing environmental monitoring data provided in the embodiment of the present invention and the received data stream are stored in the memory, and when needed, the processor 501 calls from the memory 509.

[0184] When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, twisted pair) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integrations. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., an optical disk), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0185] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0186] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0187] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0188] In addition, each functional unit in the embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0189] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0190] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligently pushing environmental monitoring data, characterized in that: include: Obtain the terminal's latitude and longitude data through the global positioning system; Performing spherical coordinate conversion processing on the latitude and longitude data of the terminal to obtain first coordinate data; Convert the first coordinate data into plane coordinate data to obtain second coordinate data; Performing spherical projection hexagon processing on the second coordinate data based on the Fuller projection algorithm, and performing range correction processing on the second coordinate data after the spherical projection hexagon processing based on the modified Bessel function to obtain third coordinate data; Mapping the third coordinate data to a square corresponding to the preset area level to obtain coordinate axis point data; Generate a Hilbert curve graph corresponding to the longitude and latitude data of the terminal according to the coordinate axis point data; Performing a quadtree least common ancestor analysis on the Hilbert curve graph to obtain candidate environmental monitoring points in a preset area of ​​the Hilbert curve graph and candidate environmental monitoring points in four subnode areas of the preset area; Calculating the distance between the terminal and the candidate environmental monitoring point to obtain multiple distance values; Obtain a candidate environmental monitoring point corresponding to the minimum distance value among the multiple distance values ​​to obtain a target environmental monitoring point; Acquire operation data of the terminal based on historical environmental monitoring points and the target environmental monitoring points, perform feature extraction on the operation data to obtain data features, and construct a decision tree through the data features, wherein the historical environmental monitoring points are used to indicate environmental monitoring points acquired by the terminal before the target environmental monitoring points; The operation data is predicted and analyzed through the decision tree to obtain the environmental monitoring points to be pushed, the environmental monitoring data corresponding to the environmental monitoring points to be pushed are acquired, and the environmental monitoring data corresponding to the environmental monitoring points to be pushed are pushed to the terminal.

2. The method for intelligently pushing environmental monitoring data according to claim 1, characterized in that: The performing of quadtree least common ancestor analysis on the Hilbert curve graph to obtain candidate environmental monitoring points in a preset area of ​​the Hilbert curve graph and candidate environmental monitoring points in four subnode areas of the preset area includes: Based on a preset quadtree nearest common ancestor algorithm, four sub-node regions of the preset region of the Hilbert curve graph are obtained; Detecting whether there are environmental monitoring points in the preset area and the four sub-node areas; If there are environmental monitoring points in the preset area and / or the four sub-node areas, the detected environmental monitoring points are used as candidate environmental monitoring points.

3. The method for intelligently pushing environmental monitoring data according to claim 2, characterized in that: After the step of detecting whether there are environmental monitoring points in the preset area and the four sub-node areas, the method for intelligently pushing environmental monitoring data further includes: If there is no environmental monitoring point in the preset area and the four child node areas, searching for the parent area of ​​the preset area in the Hilbert curve graph; Based on the preset quadtree nearest common ancestor algorithm, four target child node regions corresponding to the parent region are obtained; Detect whether there is an environmental monitoring point in the parent area and four target child node areas corresponding to the parent area; If there are environmental monitoring points in the parent area and the four target child node areas corresponding to the parent area, the detected environmental monitoring points are used as candidate environmental monitoring points.

4. The method for intelligently pushing environmental monitoring data according to claim 1, characterized in that: The calculating the distance between the terminal and the candidate environmental monitoring point to obtain a plurality of distance values ​​includes: Convert the longitude and latitude data of the terminal into global positioning system coordinate cLatLng data to obtain the coordinate data of the terminal; Acquire the longitude and latitude data of the candidate environmental monitoring point, convert the longitude and latitude data of the candidate environmental monitoring point into candidate global positioning system coordinate mLatLng data, and obtain the coordinate data of the candidate environmental monitoring point; The coordinate data of the terminal and the coordinate data of the candidate environmental monitoring points are calculated by a preset formula to obtain a plurality of distance values ​​between the terminal and the candidate environmental monitoring points.

5. The method for intelligently pushing environmental monitoring data according to claim 1, characterized in that: The extracting features of the operation data to obtain data features and constructing a decision tree through the data features includes: Extracting features from the operation data using a principal component analysis algorithm to obtain data features; Calculating the information gain of each of the data features to obtain multiple information gain values; Obtaining candidate data features corresponding to the multiple information gain values, and sorting the candidate data features corresponding to the multiple information gain values ​​in descending order of value to obtain sorted target data features; The sorted target data features are used as nodes, the categories of the sorted target data features and the preset judgment conditions corresponding to the categories are obtained, and the preset judgment conditions are used as branch conditions to construct a decision tree.

6. The method for intelligently pushing environmental monitoring data according to any one of claims 1 to 5, characterized in that: After predicting and analyzing the operation data through the decision tree to obtain the environmental monitoring points to be pushed, acquiring the environmental monitoring data corresponding to the environmental monitoring points to be pushed, and pushing the environmental monitoring data corresponding to the environmental monitoring points to be pushed to the terminal, the method for intelligently pushing environmental monitoring data also includes: Identify environmental monitoring points within a preset range to obtain early warning analysis environmental monitoring points, wherein the preset range is used to indicate a range centered on the environmental monitoring point to be pushed; Analyze the real-time environmental monitoring data of the early warning analysis environmental monitoring point by presetting early warning conditions to obtain real-time early warning information corresponding to the early warning analysis environmental monitoring point; The real-time warning information is pushed to the terminal.

7. A device for intelligently pushing environmental monitoring data, characterized in that: The device for intelligently pushing environmental monitoring data includes: A generation module, used for acquiring the latitude and longitude data of the terminal through a global positioning system; performing spherical coordinate conversion processing on the latitude and longitude data of the terminal to obtain first coordinate data; converting the first coordinate data into plane coordinate data to obtain second coordinate data; performing spherical projection hexagon processing on the second coordinate data based on a Fuller projection algorithm, and performing range correction processing on the second coordinate data after spherical projection hexagon processing based on a modified Bessel function to obtain third coordinate data; mapping the third coordinate data to a square corresponding to a preset area level to obtain coordinate axis point data; and generating a Hilbert curve corresponding to the latitude and longitude data of the terminal according to the coordinate axis point data; A first analysis module is used to perform a quadtree least common ancestor analysis on the Hilbert curve graph to obtain candidate environmental monitoring points in a preset area of ​​the Hilbert curve graph and candidate environmental monitoring points in four subnode areas of the preset area; A calculation module, used to calculate the distance between the terminal and the candidate environmental monitoring point to obtain multiple distance values; An acquisition module, used to acquire a candidate environmental monitoring point corresponding to a minimum distance value among the multiple distance values, and obtain a target environmental monitoring point; A construction module, used to obtain operation data of the terminal based on historical environmental monitoring points and the target environmental monitoring points, perform feature extraction on the operation data to obtain data features, and construct a decision tree through the data features, wherein the historical environmental monitoring points are used to indicate environmental monitoring points obtained by the terminal before the target environmental monitoring points; The second analysis module is used to perform predictive analysis on the operation data through the decision tree, obtain the environmental monitoring points to be pushed, obtain the environmental monitoring data corresponding to the environmental monitoring points to be pushed, and push the environmental monitoring data corresponding to the environmental monitoring points to be pushed to the terminal.

8. A device for intelligently pushing environmental monitoring data, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for intelligently pushing environmental monitoring data as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The method comprises instructions, and when the computer-readable instructions are executed on a computer, the computer executes the method for intelligently pushing environmental monitoring data as described in any one of claims 1 to 6.

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