Forest fire rescue path complementation optimization method and device

Abstract

The invention provides a method and a device for complementing and optimizing a forest fire rescue path. The method comprises the following steps: acquiring remote sensing image data of a target area; performing road information extraction on the remote sensing image data to obtain initial road data; acquiring action track data of the user in the target area; and correcting the initial road data based on the action track data to obtain target road data. By implementing the method disclosed by the invention, the accuracy and reliability of the obtained target road data can be effectively improved in combination with the remote sensing image data and the action track data of the user in the target area.

Description

Technical Field

[0001] The present invention relates to the technical field of forest fire prevention and extinguishing command linkage, and in particular to a method and device for forest fire rescue path completion and optimization. Background Art

[0002] At present, the command and dispatch of forest fire emergency rescue is still at the strategic direction guidance level, unable to provide accurate and detailed route guidance for front-line firefighting teams. The lack of detailed road network data in forest areas has led to obvious deficiencies in forest path planning, and the problem of being unable to ensure that rescue equipment and teams can accurately and quickly reach the location closest to the fire source has gradually shown its drawbacks in forest command and rescue scenarios, seriously restricting the response speed in wartime.

[0003] In the existing technology, there are two main modes for extracting basic road networks: one is to rely on satellite image data, but the trees in the forest area are seriously blocked and the extraction effect is difficult to achieve the expected result; the other is to analyze the personnel trajectory data of operators or mobile phone manufacturers, but the forest area is sparsely populated, and the traditional urban road extraction algorithm is not applicable to the forest area, and the effect is also poor. Although the basic algorithm of path planning is relatively mature, its application effect in forest fire prevention and control scenarios is still not ideal in the context of the lack of forest road networks. Summary of the invention

[0004] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0005] To this end, the purpose of the present invention is to propose a method, device, computer equipment and storage medium for forest fire rescue path completion optimization, which can effectively improve the accuracy and reliability of the obtained target road data by combining remote sensing image data and user's movement trajectory data in the target area.

[0006] To achieve the above-mentioned purpose, the method for optimizing forest fire rescue path completion proposed in the first aspect of the present disclosure includes:

[0007] Acquire remote sensing image data of the target area;

[0008] Extracting road information from the remote sensing image data to obtain initial road data;

[0009] Acquire the user's movement trajectory data in the target area;

[0010] The initial road data is corrected based on the action trajectory data to obtain target road data.

[0011] To achieve the above-mentioned purpose, the device for forest fire rescue path completion and optimization proposed in the second aspect of the present disclosure includes:

[0012] The first acquisition module is used to acquire remote sensing image data of the target area;

[0013] An information extraction module, used for extracting road information from the remote sensing image data to obtain initial road data;

[0014] A second acquisition module is used to acquire the movement trajectory data of the user in the target area;

[0015] The correction module is used to correct the initial road data based on the action trajectory data to obtain target road data.

[0016] The computer device proposed in the third aspect embodiment of the present disclosure includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for optimizing the completion of forest fire rescue paths proposed in the first aspect embodiment of the present disclosure is implemented.

[0017] The fourth aspect embodiment of the present disclosure proposes a non-temporary computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method for forest fire rescue path completion and optimization proposed in the first aspect embodiment of the present disclosure.

[0018] The fifth aspect embodiment of the present disclosure proposes a computer program product. When the instructions in the computer program product are executed by a processor, the method for forest fire rescue path completion and optimization proposed in the first aspect embodiment of the present disclosure is executed.

[0019] The method, device, computer equipment and storage medium provided by the present disclosure for forest fire rescue path completion optimization obtain remote sensing image data of the target area; extract road information from the remote sensing image data to obtain initial road data; obtain the user's action trajectory data in the target area; and correct the initial road data based on the action trajectory data to obtain the target road data. Thus, the accuracy and reliability of the obtained target road data can be effectively improved by combining the remote sensing image data and the user's action trajectory data in the target area.

[0020] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and / or additional aspects and advantages of the present disclosure will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0022] Figure 1 It is a flowchart of a method for completing and optimizing a forest fire rescue path proposed in an embodiment of the present disclosure;

[0023] Figure 2 is a flow chart of a method for completing and optimizing a forest fire rescue path proposed in another embodiment of the present disclosure;

[0024] Figure 3 is a schematic diagram of trajectory data proposed according to the present disclosure;

[0025] Figure 4 It is a schematic diagram of buffer zone analysis and point screening proposed in the present disclosure;

[0026] Figure 5 It is a schematic diagram of the orientation division and edge point determination proposed in the present disclosure;

[0027] Figure 6 is a schematic diagram of edge plotting proposed according to the present disclosure;

[0028] Figure 7 is a schematic diagram of road extraction according to the present disclosure;

[0029] Figure 8 is a schematic diagram of road fusion proposed according to the present disclosure;

[0030] Fig. 9 It is a structural schematic diagram of a device for completing and optimizing a forest fire rescue path proposed in an embodiment of the present disclosure;

[0031] Fig.10 A block diagram of an exemplary computer device suitable for implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0032] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present disclosure, and are not to be construed as limitations of the present disclosure. On the contrary, the embodiments of the present disclosure include all changes, modifications, and equivalents that fall within the spirit and connotation of the appended claims.

[0033] Figure 1 It is a flowchart of a method for forest fire rescue path completion and optimization proposed in an embodiment of the present disclosure.

[0034] Among them, it should be noted that the executor of the method for completing and optimizing forest fire rescue paths in this embodiment is a device for completing and optimizing forest fire rescue paths, which can be implemented by software and / or hardware. The device can be configured in a computer device, and the computer device can include but is not limited to a terminal, a server, etc. For example, the terminal can be a mobile phone, a handheld computer, etc.

[0035] like Figure 1 As shown, the method for optimizing the forest fire rescue path completion includes:

[0036] S101: Acquire remote sensing image data of a target area.

[0037] The target area may refer to the area to be optimized for forest fire rescue path completion in the embodiment of the present disclosure.

[0038] Among them, remote sensing image data refers to the image data of the target area collected based on remote sensing technology.

[0039] In the disclosed embodiment, when remote sensing image data of a target area is acquired, reliable data support can be provided for the subsequent determination of initial road data.

[0040] S102: Extracting road information from the remote sensing image data to obtain initial road data.

[0041] The initial road data refers to the data used to describe road-related information determined by extracting road information from the sensing image data in the implementation of the present disclosure.

[0042] For example, in the embodiment of the present disclosure, when extracting road information from remote sensing image data to obtain initial road data, the relevant features of the remote sensing image data (such as texture features, color features, etc.) can be analyzed to obtain road vector data, and then converted into raster data as the initial road data.

[0043] S103: Obtaining the user's movement trajectory data in the target area.

[0044] The action trajectory data refers to the trajectory data of the user in the target area, and the action trajectory data may include multiple positioning information of the user.

[0045] In the embodiment of the present disclosure, when obtaining the user's movement trajectory data in the target area, the user can configure a positioning device (such as a mobile phone) while moving in the target area, and then feedback positioning information based on a preset time interval, and generate the movement trajectory data based on the positioning information.

[0046] S104: Correcting the initial road data based on the action trajectory data to obtain target road data.

[0047] The target road data refers to the road data obtained by correcting the initial road data based on the action trajectory data.

[0048] For example, in the embodiment of the present disclosure, when the initial road data is corrected based on the motion trajectory data to obtain the target road data, the motion trajectory data and the initial road data can be input into a pre-trained machine learning model to obtain the corresponding target road data. Alternatively, the initial road data can be corrected based on the motion trajectory data by a third-party device to obtain the target road data, and there is no limitation to this.

[0049] Optionally, in some embodiments, after the initial road data is corrected based on the action trajectory data to obtain the target road data, the new action trajectory data of the user in the target area can also be obtained; the target road data is updated and adjusted based on the new action trajectory data. In this way, the target road data can be updated in real time to ensure the reliability and real-time performance of the target road data.

[0050] It is understandable that forest roads have greater uncertainty and may change with changes in natural conditions or the influence of human factors. Therefore, in the embodiments of the present disclosure, new movement trajectory data of the user in the target area can be obtained; the target road data can be updated and adjusted based on the new movement trajectory data.

[0051] In this embodiment, remote sensing image data of the target area is obtained; road information is extracted from the remote sensing image data to obtain initial road data; the user's movement trajectory data in the target area is obtained; and the initial road data is corrected based on the movement trajectory data to obtain target road data. Thus, the accuracy and reliability of the obtained target road data can be effectively improved by combining the remote sensing image data and the user's movement trajectory data in the target area.

[0052] Figure 2 It is a flowchart of a method for optimizing forest fire rescue path completion proposed in another embodiment of the present disclosure.

[0053] like Figure 2 As shown, the method for optimizing the forest fire rescue path completion includes:

[0054] S201: Acquire remote sensing image data of the target area.

[0055] S202: Extracting road information from remote sensing image data to obtain initial road data.

[0056] S203: Acquire the user's motion trajectory data in the target area, wherein the motion trajectory data includes a plurality of candidate trajectory points.

[0057] The description of S201 - S203 can be specifically referred to the above embodiment, which will not be repeated here.

[0058] S204: Screening multiple candidate trajectory points to determine a target trajectory point.

[0059] The target trajectory point refers to a trajectory point determined after screening multiple candidate trajectory points.

[0060] It is understandable that since there may be individual users moving in non-road areas, there may be trajectory points in the non-road range among the multiple candidate trajectory points. Therefore, in the embodiment of the present disclosure, multiple candidate trajectory points can be screened to determine the target trajectory points, so as to effectively reduce the impact brought by abnormal point data.

[0061] In the embodiment of the present disclosure, when screening multiple candidate trajectory points to determine the target trajectory point, the multiple candidate trajectory points can be screened based on a method combining numbers and shapes to determine the target trajectory point, or the multiple candidate trajectory points can be screened based on any other possible method to determine the target trajectory point, and there is no limitation on this.

[0062] Optionally, in some embodiments, when screening multiple candidate trajectory points to determine the target trajectory point, a buffer corresponding to the candidate trajectory point may be determined; if the number of candidate trajectory points contained in the buffer is greater than a first threshold, the corresponding candidate trajectory point is used as the target trajectory point. Thus, the target trajectory point can be accurately and quickly determined from multiple candidate trajectory points based on the number of candidate trajectory points contained in the buffer.

[0063] The buffer zone may refer to an area divided for a candidate trajectory point, for example, a circular area with the candidate trajectory point as the center, or any other possible area, which is not limited.

[0064] The first threshold refers to the threshold value configured in advance for the number of candidate trajectory points contained in the buffer in the implementation of the present disclosure, which can be used to determine whether the candidate trajectory point can be used as the target trajectory point. Its specific value can be flexibly adjusted according to the application scenario, for example, it can be 2 or 3, etc., and there is no restriction on this.

[0065] In the disclosed embodiment, when a plurality of candidate trajectory points are screened to determine a target trajectory point, the influence brought by abnormal point data can be effectively reduced, thereby ensuring the practicality of the obtained target trajectory point.

[0066] S205: Determine a boundary trajectory point located at a road boundary among the plurality of target trajectory points.

[0067] The boundary track point may refer to a track point located at the boundary of a road.

[0068] In the embodiment of the present disclosure, when determining the boundary trajectory point located at the road boundary among multiple target trajectory points, the boundary trajectory point located at the road boundary among the multiple target trajectory points can be determined based on a method combining numbers and shapes, or, the multiple target trajectory points can be input into a pre-trained machine learning model to obtain the corresponding boundary trajectory points, and there is no limitation on this.

[0069] Optionally, in some embodiments, when determining a boundary trajectory point located at a road boundary among multiple target trajectory points, the number of interval points included in the target trajectory point corresponding to different azimuth intervals may be determined; the number of interval points is substituted into a preset formula to calculate the edge degree corresponding to the target trajectory point, wherein the edge degree is used to describe the possibility that the target trajectory point is a boundary trajectory point; and the boundary trajectory point is determined from the multiple target trajectory points according to the edge degree. Thus, the corresponding edge degree can be accurately calculated based on the number of interval points included in the target trajectory point corresponding to different azimuth intervals, and then the boundary trajectory point can be determined from the multiple target trajectory points in combination with the edge degree threshold.

[0070] The azimuth interval refers to the area divided according to different azimuths of the target trajectory point. For example, it can be divided into four areas according to the southeast, northwest, and northeast directions, or any other possible division method can be used to determine the azimuth interval, which is not limited.

[0071] The number of interval points may be used to describe the number of other target trajectory points within the above-mentioned azimuth interval.

[0072] The preset formula refers to a formula pre-configured in the embodiment of the present disclosure for evaluating the probability that the target trajectory point is at the edge of the road. The specific logic and form of the formula can be flexibly adjusted according to the application scenario, and there is no limitation on this.

[0073] In the embodiment of the present disclosure, when determining a boundary trajectory point from a plurality of target trajectory points according to the edge degree, an edge degree threshold may be preset, and then the target trajectory points having an edge degree greater than the edge degree threshold are taken as boundary trajectory points.

[0074] S206: Determine reference road data based on the boundary trajectory points.

[0075] The reference road data refers to the road data determined based on the boundary track points in the embodiment of the present disclosure, which can be used to indicate the direction, starting point, turning point, etc. of the road.

[0076] Optionally, in some embodiments, when determining reference road data based on boundary track points, the road width information may be determined based on the boundary track points; multiple reference road sections may be determined based on the road width information and a preset width difference threshold; the first starting point and the first ending point of the reference road section may be determined; and the reference road data may be determined based on the first starting point and the first ending point of different reference road sections. In this way, the road sections may be accurately and quickly divided in combination with the width change of the road, thereby ensuring the clarity of the indication of the obtained reference road data.

[0077] The road width information may be used to indicate the width of the road at different locations.

[0078] The preset width difference threshold refers to a threshold value configured in advance for the width difference at different positions of the road, which can be used to determine whether the road needs to be segmented.

[0079] The reference road section refers to the road section determined after segmenting the road based on the road width information and the preset width difference threshold in the embodiment of the present disclosure.

[0080] For example, in the embodiment of the present disclosure, when determining multiple reference road sections based on road width information and a preset width difference threshold, the starting point of the entire road can be used as the first starting point, and then the width values ​​of different positions along the road are determined. When it is found that the difference between the width value of a certain position and the width value of the starting point is greater than or equal to the preset width difference threshold, the position is used as the new starting point, and the above operation is repeated along the road until the end of the road.

[0081] S207: Correcting the initial road data based on the reference road data to obtain target road data.

[0082] That is, in the embodiment of the present disclosure, after the reference road data is determined based on the boundary trajectory points, the initial road data can be corrected based on the reference road data to obtain the target road data.

[0083] In the embodiment of the present disclosure, when the initial road data is corrected based on the reference road data to obtain the target road data, the reference road data and the initial road data can be input into a pre-trained machine learning model to obtain the corresponding target road data. Alternatively, the initial road data can be corrected based on the reference road data based on a method combining numbers and shapes to obtain the target road data, and there is no limitation to this.

[0084] Optionally, in some embodiments, the initial road data includes a second starting point and a second ending point corresponding to a plurality of initial road segments; when the initial road data is corrected based on the reference road data to obtain the target road data, the following steps may be performed: determining the angle between the reference road segment and the initial road segment; determining a first distance between the first starting point and the second starting point, and a second distance between the first ending point and the second ending point; if the reference road segment and the initial road segment meet a preset condition, then determining the angle between the second starting point and the second ending point based on the preset weight information, the first starting point and the first ending point; The preset conditions include: the angle value is less than or equal to the preset angle threshold, the first distance is less than or equal to the preset distance threshold, and the second threshold is less than or equal to the preset distance threshold; if the reference road section and the initial road section do not meet the preset conditions, then determine the starting road section among the multiple initial road sections, determine the target road section among the multiple reference road sections, and connect the second starting point position corresponding to the starting road section with the first starting point position corresponding to the target road section, wherein the first ending point position of the target road section is the point position among the first ending points of the multiple reference road sections that is closest to the second starting point position of the starting road section. In this way, the initial road data can be quickly and accurately corrected based on the reference road data to obtain the target road data, thereby effectively improving the reliability of the obtained target road data.

[0085] Among them, the angle value can be used to indicate the similarity of the directions of two roads.

[0086] In the embodiment of the present disclosure, when determining the angle value between the reference road segment and the initial road segment, the angle value between the reference road segment and the initial road segment may be determined by determining an initial road segment that is closest to the reference road segment in the spatial dimension.

[0087] The first distance may be used to describe the distance between the reference segment and the corresponding starting point of the initial segment, while the second distance may be used to describe the distance between the reference segment and the corresponding ending point of the initial segment.

[0088] The preset weight information may be used to indicate the weights of the reference road segment and the initial road segment during the fusion process.

[0089] For example, in the embodiment of the present disclosure, when the second starting point and the second ending point are corrected based on the preset weight information, the first starting point and the first ending point, the first starting point and the second starting point can be weightedly summed based on the preset weight information to obtain the fused starting point; and the first ending point and the second ending point can be weightedly summed based on the preset weight information to obtain the fused ending point.

[0090] The starting section refers to a section at the starting position among a plurality of connected initial sections, and this section is closer to the reference section than the initial section at the end.

[0091] In the embodiment of the present disclosure, when determining a target section among multiple reference sections, the end point closest to the starting point of the starting section among the multiple reference sections can be found based on the shortest path algorithm or the nearest neighbor search algorithm, and then the reference section corresponding to the end point is used as the target section.

[0092] That is, in the disclosed embodiment, the action trajectory data includes multiple candidate trajectory points. After obtaining the action trajectory data of the user in the target area, the multiple candidate trajectory points can be screened to determine the target trajectory point; the boundary trajectory point located at the road boundary among the multiple target trajectory points can be determined; based on the boundary trajectory point, the reference road data can be determined; based on the reference road data, the initial road data can be corrected to obtain the target road data. In this way, the reliability of the road correction process can be effectively improved to ensure that the obtained target road data clearly indicates the actual road conditions.

[0093] In this embodiment, a target trajectory point is determined by screening multiple candidate trajectory points; a boundary trajectory point located at a road boundary among the multiple target trajectory points is determined; reference road data is determined based on the boundary trajectory point; and the initial road data is corrected based on the reference road data to obtain the target road data. In this way, the reliability of the road correction process can be effectively improved to ensure that the obtained target road data clearly indicates the actual road conditions.

[0094] In summary of the above embodiments, the forest fire rescue path completion optimization method proposed in the present disclosure aims to optimize the forest fire emergency rescue route by combining the trajectory data of daily forest fire inspections and fire point verifications, as well as the basic road network data extracted based on remote sensing images, to achieve the construction of forest road network data. This method not only utilizes the actual trajectory information during daily inspections and fire point verifications, but also integrates the basic road network data artificially obtained by remote sensing imaging technology, thereby achieving the comprehensive construction and precise improvement of forest road network data.

[0095] The main steps include:

[0096] 1. Construction of forest road network

[0097] 1.1 Road extraction based on remote sensing images

[0098] The forest roads are blocked by trees, which makes AI intelligent analysis challenging and limited in accuracy. Therefore, manual analysis can be used to analyze the trunk roads in the forest area. In remote sensing images, trees usually appear granular, while the road surface may appear grayish white. Therefore, the editing tools of professional software such as ArcGIS can be used to manually outline and collect road information through visual interpretation methods, and convert it into real road vector data. Subsequently, attribute values ​​are assigned to the vector data to distinguish the road from the background. Finally, the vector-to-raster conversion tool is used to convert it into raster data.

[0099] 1.2 Road correction based on daily inspection and fire point verification trajectory

[0100] Road data extracted from remote sensing image data can provide basic path planning during daily inspections and fire point verification tasks, and collect the action trajectory point data of task executors. Forest roads are corrected based on the action trajectory point data. The specific data processing process is as follows.

[0101] Road data extracted from remote sensing image data provides basic path planning for daily inspections and fire point verification tasks. When performing these tasks, the action trajectory point data of the task performers are also collected, such as Figure 3 As shown, Figure 3 This is a schematic diagram of trajectory data proposed in the present disclosure. In order to further improve the accuracy of path planning, these action trajectory point data are used to correct the forest road. The specific data processing process is as follows.

[0102] (1) Data screening: When performing data screening, a buffer zone analysis will be performed with the trajectory point as the center. Figure 4 As shown, Figure 4 This is a schematic diagram of buffer zone analysis and point selection proposed in the present disclosure, wherein if there are more than two other points within a radius of 2 meters of a point, the point data will be retained; if there are less than two, the point data (as shown in the buffer zone A in the figure) will be discarded. On the contrary, the points that meet the conditions (as shown in the buffer zone B in the figure) will be retained.

[0103] Element definition:

[0104] P i =(x i ,y i ): represents the i-th trajectory point, each trajectory point contains coordinate information.

[0105] r: represents the radius of the buffer zone, here r = 2 meters.

[0106] N(P1): indicates the position P i Within the radius r (excluding P i The number of points of itself).

[0107] Input data:

[0108] Point set P = {P1, P2, ..., P n}, where each point P i Contains coordinate information.

[0109] Calculation formula:

[0110] For each point P i , calculate the number of other points in its buffer zone (excluding P i itself).

[0111]

[0112] Filters:

[0113] If the number of other points in the buffer is greater than 2, keep P i .

[0114] If the number of other points in the buffer is ≤ 2, discard P i .

[0115] Algorithm steps:

[0116] Initialization: Traverse all points and initialize a list of reserved points.

[0117] Statistics: For each point P i , count the number of other points N(P i ).

[0118] Filtering point: According to the filtering conditions, if N(P i )≥2, then P i Add the point to the list of retained points; otherwise, discard the point.

[0119] Output result: Output the list of retained points.

[0120] (2) Road division: After careful screening of the point data, a series of path point collections will be obtained. A comprehensive judgment is made based on the number of points in different directions around each point to determine the edge point data of the path. Figure 5 As shown, Figure 5 It is a schematic diagram of the orientation division and edge point determination proposed in the present disclosure.

[0121] Element definition:

[0122] P i =(x i ,y i): represents the i-th trajectory point, each trajectory point contains coordinate information.

[0123] o: Indicates the direction around each point, and defines 8 directions: [east, southeast, south, southwest, west, northwest, north, northeast].

[0124] N i (k): represents point P i The number of points in the surrounding k-th orientation interval.

[0125] T: Edge degree threshold, used to determine which points are edge points.

[0126] E i :P i The edge degree of a point.

[0127] Input data:

[0128] The selected path point set P = {P1, P2, ..., P n}, where each point P i Contains coordinate information.

[0129] Calculation formula:

[0130] By edge degree E i Indicators to measure point P i The possibility of edge points:

[0131]

[0132] (3) Edge mapping: Forest roads often have irregular boundaries, which is very different from the parallel boundaries of traditional municipal roads. Therefore, the road boundaries are determined directly based on the determined edge point data without additional buffering. Figure 6 As shown, Figure 6 This is a schematic diagram of edge mapping proposed in the present disclosure to ensure the accuracy of subsequent path planning and avoid possible deviations.

[0133] (4) Information extraction: In the information extraction stage, based on the connected edge point data, a path width value is extracted every 1 meter along the road boundary, and key information such as the maximum width and minimum width of the road are recorded at the same time.

[0134] (5) Road extraction: In the road extraction phase, the processed road vector data is subjected to accurate segment centerline extraction using a 0.5-meter width difference as the standard, thereby forming basic road segment data. At the same time, this data is associated with relevant road segment information to ensure data integrity and accuracy.

[0135] Element definition:

[0136] L, R: The edge point sets on both sides of the road. The data in the two sets correspond one to one.

[0137] W: Road width

[0138] ΔW: road width difference threshold, which is preset to 0.5 in the present invention.

[0139] Input data:

[0140] The calculated edge point set L = {(x l1 ,y l1 ),(x l2 ,y l2 ),...} and R={(x r1 ,y r1 ),(x r2 ,y r2 ),...}, the data in the two sets correspond one to one.

[0141] Calculation formula:

[0142] Calculation of road width W:

[0143]

[0144] Whether to judge by segmentation:

[0145] |W i -W i+1 |>ΔW

[0146] Center point calculation (for the start and end points of each segment):

[0147]

[0148] By connecting the center points, we can get the road information, such as Figure 7 As shown, Figure 7 It is a schematic diagram of road extraction proposed in the present disclosure.

[0149] (6) Road network data fusion, such as Figure 8 As shown, Figure 8 It is a schematic diagram of road fusion proposed in the present disclosure, in which the extracted road data (solid line A) is compared with the manually mapped road network data (solid lines B and C). Based on the direction of each group of two routes, a position judgment range of plus or minus 90 degrees is set. If the distance between the start and end points of the two routes is within 3 meters (indicated by the dotted lines a1 and a2), corrections are made according to the weight of the extracted route of 0.8 and the weight of the manually mapped route of 0.2; otherwise, they are regarded as different routes, and the previous associated node line of the nearest point is completed based on the extracted route (dotted line b), laying the foundation for subsequent road network data correction.

[0150] Element definition:

[0151] : Direction vector of the extracted road data.

[0152] : Direction vector of manually plotted road network data.

[0153] M ext : According to the extraction route weight, the present invention defaults to 0.8.

[0154] M man : The weight of the manually plotted route is set to 0.2 by default in the present invention.

[0155] Input data:

[0156] The calculated route point collection, the extracted road data ext = {(x ext1 ,y ext1 ), (x ext2 ,y ext2 ), ...} and the manually mapped road network data man{(x man1 ,y man1 ), (x man2 ,y man2 ), ...}.}

[0157] Calculation formula:

[0158] Road direction trend judgment:

[0159]

[0160] If cos(θ) ≥ cos(35°), the directions of the two routes are considered to be similar.

[0161] Point distance judgment: The start and end points of the extracted road data are (x ext1 ,y ext1 ) and (x ext2 ,y ext2 ), the start and end points of the manually mapped road network data are (x man1 ,y man1 ) and (x man2 ,y man2 ). Use the Euclidean distance formula to calculate the distance between points:

[0162]

[0163] If the distance between the starting point and the ending point is ≤3 meters, proceed to the next step of correction.

[0164] Weight Modifier:

[0165] x 1 =0.2xman +0.8x ext

[0166] y 1 =0.2y man +0.8y ext

[0167] The corrected road point coordinates are (x 1 ,y 1 ).

[0168] Non-similar route data correction:

[0169] If the directions of the two routes are not similar or the distance between the start and end points is too far, they are considered different routes. In this case, it is necessary to complete the previous associated node route of the nearest point based on the extracted route. Use the shortest path algorithm or the nearest neighbor search algorithm to find the nearest associated node and complete the route.

[0170] (7) Data correction: In terms of data correction, the present invention will comprehensively correct the road network data every six months based on the continuously collected and improved movement trajectory data. At the same time, the present invention provides the staff with a correction function for the segmented route information. In addition to adjusting the extracted measurement data, it can also supplement more detailed description information such as road surface material and ruggedness.

[0171] 2. Application of road network data in forest fire command and rescue scenarios

[0172] In the command and rescue scenario of forest fires, based on the completed and optimized forest road network data, by analyzing the path route, road width, road surface material and other information, it is possible to achieve refined path planning, assist the rescue team in selecting transportation facilities, and ensure that the rescue team can accurately and quickly reach the location closest to the fire source. Accurate command is achieved, providing strong support for emergency response to forest fires.

[0173] Fig. 9 It is a structural schematic diagram of a device for completing and optimizing forest fire rescue paths proposed in an embodiment of the present disclosure.

[0174] like Fig. 9 As shown, the device 90 for completing and optimizing the forest fire rescue path includes:

[0175] The first acquisition module 901 is used to acquire remote sensing image data of a target area;

[0176] The information extraction module 902 is used to extract road information from the remote sensing image data to obtain initial road data;

[0177] The second acquisition module 903 is used to acquire the movement trajectory data of the user in the target area;

[0178] The correction module 904 is used to correct the initial road data based on the action trajectory data to obtain the target road data.

[0179] It should be noted that the aforementioned explanation of the method for completing and optimizing the forest fire rescue path is also applicable to the device for completing and optimizing the forest fire rescue path in this embodiment, and will not be repeated here.

[0180] In this embodiment, remote sensing image data of the target area is obtained; road information is extracted from the remote sensing image data to obtain initial road data; the user's movement trajectory data in the target area is obtained; and the initial road data is corrected based on the movement trajectory data to obtain target road data. Thus, the accuracy and reliability of the obtained target road data can be effectively improved by combining the remote sensing image data and the user's movement trajectory data in the target area.

[0181] Fig.10 A block diagram of an exemplary computer device suitable for implementing embodiments of the present disclosure is shown. Fig.10 The computer device 12 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0182] like Fig.10 As shown, the computer device 12 is in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0183] The bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnection (PCI) bus.

[0184] The computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0185] The memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Fig.10 Not shown, often called a "hard drive").

[0186] although Fig.10 Not shown, a disk drive for reading and writing a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing a removable non-volatile optical disk (e.g., a compact disc read only memory (Compact Disc Read Only Memory; hereinafter referred to as: CD-ROM), a digital versatile disc read only memory (Digital Video Disc Read Only Memory; hereinafter referred to as: DVD-ROM) or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present disclosure.

[0187] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described in the present disclosure.

[0188] The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a human body to interact with the computer device 12, and / or any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed via an input / output (I / O) interface 22. In addition, the computer device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the computer device 12 via a bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0189] The processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28, such as implementing the forest fire rescue path completion optimization method mentioned in the above embodiment.

[0190] In order to implement the above embodiments, the present disclosure also proposes a non-temporary computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the method for completing and optimizing the forest fire rescue path proposed in the above embodiments of the present disclosure is implemented.

[0191] In order to implement the above embodiments, the present disclosure also proposes a computer program product. When the instruction processor in the computer program product is executed, the method for completing and optimizing the forest fire rescue path proposed in the above embodiments of the present disclosure is executed.

[0192] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0193] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

[0194] It should be noted that, in the description of the present disclosure, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present disclosure, unless otherwise specified, the meaning of "plurality" is two or more.

[0195] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present disclosure includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present disclosure belong.

[0196] It should be understood that the various parts of the present disclosure can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0197] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0198] In addition, each functional unit in each embodiment of the present disclosure may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0199] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0200] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0201] Although the embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present disclosure. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present disclosure.

Claims

1. A method for optimizing forest fire rescue path completion, characterized in that: include: Acquire remote sensing image data of the target area; Extracting road information from the remote sensing image data to obtain initial road data; Acquire the user's movement trajectory data in the target area; The initial road data is corrected based on the action trajectory data to obtain target road data.

2. The method according to claim 1, characterized in that The motion trajectory data includes a plurality of candidate trajectory points; The step of correcting the initial road data based on the action trajectory data to obtain target road data includes: Screening the multiple candidate trajectory points to determine the target trajectory point; Determining a boundary trajectory point located at a road boundary among the plurality of target trajectory points; Determining reference road data based on the boundary trajectory points; The initial road data is corrected based on the reference road data to obtain the target road data.

3. The method according to claim 2, characterized in that The screening of the plurality of candidate trajectory points to determine the target trajectory point includes: Determine a buffer zone corresponding to the candidate trajectory point; If the number of candidate trajectory points contained in the buffer is greater than a first threshold, the corresponding candidate trajectory point is used as the target trajectory point.

4. The method according to claim 2, characterized in that The step of determining a boundary trajectory point located at a road boundary among the plurality of target trajectory points comprises: Determine the number of interval points included in different azimuth intervals corresponding to the target trajectory point; Substituting the number of interval points into a preset formula to calculate the edge degree corresponding to the target trajectory point, wherein the edge degree is used to describe the possibility that the target trajectory point is the boundary trajectory point; According to the edge degree, the boundary track point is determined from a plurality of the target track points.

5. The method according to claim 2, characterized in that The step of determining reference road data based on the boundary track point includes: Determining road width information according to the boundary track points; Determining a plurality of reference road sections according to the road width information and a preset width difference threshold; Determine a first starting point and a first ending point of the reference road segment; The reference road data is determined based on the first starting point and the first ending point of different reference road sections.

6. The method according to claim 5, characterized in that The initial road data includes a second starting point and a second ending point corresponding to a plurality of initial road sections; The step of correcting the initial road data based on the reference road data to obtain the target road data includes: Determining an angle value between the reference road section and the initial road section; Determine a first distance between the first starting point and the second starting point, and a second distance between the first ending point and the second ending point; If the reference road section and the initial road section meet preset conditions, the second starting point and the second ending point are corrected based on preset weight information, the first starting point and the first ending point, wherein the preset conditions include: the angle value is less than or equal to a preset angle threshold, the first distance is less than or equal to a preset distance threshold, and the second threshold is less than or equal to the preset distance threshold; If the reference road section and the initial road section do not meet the preset conditions, then determine the starting road section among the multiple initial road sections, determine the target road section among the multiple reference road sections, and connect the second starting point corresponding to the starting road section with the first starting point corresponding to the target road section, wherein the first ending point of the target road section is the point among the first ending points of the multiple reference road sections that is closest to the second starting point of the starting road section.

7. The method according to claim 1, characterized in that The method further comprises: Acquire new movement trajectory data of the user in the target area; The target road data is updated and adjusted based on the new action trajectory data.

8. A device for forest fire rescue path completion and optimization, characterized in that: include: The first acquisition module is used to acquire remote sensing image data of the target area; An information extraction module, used for extracting road information from the remote sensing image data to obtain initial road data; A second acquisition module is used to acquire the movement trajectory data of the user in the target area; The correction module is used to correct the initial road data based on the action trajectory data to obtain target road data.

9. A computer device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: in, The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 7.

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