CN113486481B - Optimized setting method of environment-aware network - Google Patents

Optimized setting method of environment-aware network Download PDF

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CN113486481B
CN113486481B CN202110713800.8A CN202110713800A CN113486481B CN 113486481 B CN113486481 B CN 113486481B CN 202110713800 A CN202110713800 A CN 202110713800A CN 113486481 B CN113486481 B CN 113486481B
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CN113486481A (en
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王海
杨光
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Tongji University
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Abstract

The invention provides an optimal setting method of an environment-aware network, which comprises the following steps: step S1: constructing a network model of the current environment-aware network; step S2: judging whether the environment space is divided completely; if so, go to step S3; otherwise, adjusting the divided polygon region to eliminate the non-divided region; if all the undivided regions cannot be eliminated by adjustment, dividing the undivided regions into one or more polygonal regions based on the existing boundary points; step S3: and searching an optimal setting mode and outputting the optimal setting mode. The method can combine the existing sensing nodes and the current environmental space characteristics to jointly find the optimal sensing node setting mode, thereby fully utilizing the existing physical resources.

Description

Optimized setting method of environment-aware network
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of building energy management, and provides an optimal setting method of an environment perception network.
[ background of the invention ]
The building energy management technology is gradually and closely combined with a new-generation informatization technology, and how to apply the new-generation informatization technology to integrate and apply forward integration design and resident satisfaction evaluation and analysis, collaborative design management and control, environmental variable coupling design and other technologies to achieve the integrated collaboration of the whole specialty and the whole process of green residential design is always a technical problem faced by China. The invention combines a machine learning method to construct an environment network monitoring and early warning model and a frame, and realizes real-time perception of health conditions and building environment and active prediction of risks. Therefore, the corresponding home environment entities are reflected, and the integration efficiency of the family health building information and the personnel health information and the reliability, consistency and integrity of the multi-dimensional data fusion are improved. The problem of providing accurate basis for public environment improvement and transformation and subsequent project design is solved, and accurate data support is provided for building environment variable coupling design of community public spaces. However, in the existing situation, existing environmental sensors are already ubiquitous, and therefore, in the process of constructing an environmental sensor network, how to effectively utilize existing sensing nodes, how to combine the existing sensing nodes with a complex environmental space, and how to form an effective environmental network together with the created sensing nodes is a problem to be solved. The method can (1) combine the existing sensing nodes and the current environmental space characteristics to jointly find the optimal sensing node setting mode, thereby fully utilizing the existing physical resources; (2) expanding the sensing node into a three-dimensional space for setting multi-level sensing nodes; (3) and setting the perception network by associating the setting of the perception node with the capability of the perception node, thereby constructing an optimal perception network setting mode which accords with the current social development condition and the available resources of the user.
[ summary of the invention ]
In order to solve the above problems in the prior art, the present invention provides an optimal setting method for an environment-aware network, including:
step S1: constructing a network model of the current environment-aware network;
step S2: judging whether the environment space is divided completely; if so, go to step S3; otherwise, adjusting the divided polygon region to eliminate the non-divided region; if all the undivided regions cannot be eliminated by adjustment, dividing the undivided regions into one or more polygonal regions based on the existing boundary points;
step S3: and searching an optimal setting mode and outputting the optimal setting mode.
Further, the step S1 is specifically: the current environment space is divided into polygonal areas, and the current environment space can be divided into a space structure divided by a polygonal structure through polygonal division; the polygonal area is defined by boundary points Bi, sensing nodes Ii are arranged in the polygonal area, and the sensing nodes are used for environment information in the polygonal area where the sensing nodes are located; the distance between any point in the polygonal area and the sensing node is smaller than the distance between any point and other sensing nodes; the size of the polygonal area is related to the perception capability of the perception node, the larger the perception capability is, the larger the corresponding polygonal area is, and vice versa.
Further, the dividing of the current environment space into polygonal regions specifically includes the following steps:
step SA 1: acquiring an initial sensing node; constructing a polygonal area for the initial sensing node; the constructed polygonal area is matched with the perception capability of the initial perception node; when the environment space has a preset sensing node, the initial sensing node is the sensing node closest to the center of the environment space; otherwise, setting a sensing node at a position which is nearest to the center of the environment space and can be set; putting the constructed polygonal area into a polygonal area queue to be processed;
step SA 2: acquiring a polygon area to be processed from the head of a polygon area queue, and constructing an adjacent polygon area for the polygon area to be processed; constructing adjacent polygon areas based on each adjacent surface or each adjacent edge in sequence, and constructing a maximum adjacent polygon area capable of covering a preset sensing node if one adjacent polygon area exists so that the adjacent polygon area can cover the preset sensing node; the boundary point of the adjacent polygonal area is positioned in an circumscribed circle which takes the preset sensing node as the center and takes the sensing range of the sensing node as the radius; otherwise, not constructing adjacent polygonal areas for the adjacent surfaces/edges; putting the constructed polygonal area into the tail of a queue of the polygonal area to be processed;
step SA 3: if the queue of the polygon area to be processed is empty, go to step SA 4; otherwise, go to step SA 2;
step SA 4: for each sensing node which is not covered in the polygonal area, constructing the polygonal area based on the existing boundary points, and establishing the polygonal area based on the sensing capability of one sensing node by taking one sensing node as a center; in the construction process, the existing boundary points are utilized to the maximum extent, and the utilized existing boundary points are positioned in a circumscribed circle which takes the preset sensing node as the center and the sensing range of the sensing node as the radius.
Furthermore, when a plurality of sensing nodes exist in the same polygonal area, one sensing node is selected based on the credibility degree.
Further, information of the perception section is fused for environmental control.
Further, the polygonal area is a two-dimensional or three-dimensional space.
An environment-aware network optimized setting device, the apparatus comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a method for optimized setting of the environment-aware network.
An apparatus, characterized in that the apparatus comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a method for optimized setting of the environment-aware network.
An environment-aware network optimized setup system, the system comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a method for optimized setting of the environment-aware network.
A computer-readable storage medium on which a computer program is stored, which, when being executed by a processor, implements the method for optimal setting of an environment-aware network.
Has the advantages that:
(1) the optimal sensing node setting mode can be searched by combining the existing sensing nodes and the current environmental space characteristics, so that the existing physical resources are fully utilized; (2) the method can be expanded into a three-dimensional space for multi-level sensing node setting; (3) the method can set the perception network by associating the setting of the perception node with the ability of the perception node, thereby constructing an optimal perception network setting mode which accords with the current social development condition and the available resources of the user.
[ description of the drawings ]
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, and are not to be considered limiting of the invention, in which:
fig. 1 is a schematic diagram of an optimal setting method of an environment-aware network according to the present invention.
Fig. 2 is a schematic diagram of a partitioning method of the context aware network according to the present invention.
[ detailed description ] embodiments
The present invention will now be described in detail with reference to the drawings and specific embodiments, wherein the exemplary embodiments and descriptions are provided only for the purpose of illustrating the present invention and are not to be construed as limiting the present invention.
The method for optimizing and setting the environment-aware network comprises the following steps:
step S1: constructing a network model of the current environment-aware network; specifically, the method comprises the following steps: the current environment space is divided into polygonal areas, and the current environment space can be divided into a space structure divided by a polygonal structure through polygonal division; the polygonal area is defined by boundary points Bi, sensing nodes Ii are arranged in the polygonal area, and the sensing nodes are used for environment information in the polygonal area where the sensing nodes are located; the distance between any point in the polygonal area and the sensing node is smaller than the distance between any point and other sensing nodes; the size of the polygonal area is related to the perception capability of the perception node, the larger the perception capability is, the larger the corresponding polygonal area is, and vice versa; that is, the network modeling mode can adapt to sensing nodes with different sensing capabilities, and the current hardware setting situation is more consistent;
preferably: the polygonal area is a two-dimensional or three-dimensional space;
the method for dividing the current environment space into the polygonal areas specifically comprises the following steps:
step SA 1: acquiring an initial sensing node; constructing a polygonal area for the initial sensing node; the constructed polygonal area is matched with the perception capability of the initial perception node; when the environment space has a preset sensing node, the initial sensing node is the sensing node closest to the center of the environment space; otherwise, setting a sensing node at a position which is nearest to the center of the environment space and can be set; putting the constructed polygonal area into a polygonal area queue to be processed; whether the setting can be carried out or not depends on the actual physical condition of the environment space, and the setting cannot be carried out in some cases when the physical condition is not allowed;
step SA 2: acquiring a polygon area to be processed from the head of a polygon area queue, and constructing an adjacent polygon area for the polygon area to be processed; constructing adjacent polygon areas based on each adjacent surface or each adjacent edge in sequence, and constructing a maximum adjacent polygon area capable of covering a preset sensing node if one adjacent polygon area exists so that the adjacent polygon area can cover the preset sensing node; the boundary point of the adjacent polygonal area is positioned in an circumscribed circle which takes the preset sensing node as the center and takes the sensing range of the sensing node as the radius; otherwise, not constructing an adjacent polygonal area for the adjacent surface/edge; putting the constructed polygonal area into the tail of a queue of the polygonal area to be processed;
preferably: when an adjacent polygon region exists, so that the adjacent polygon region can cover a plurality of preset sensing nodes, one preset sensing node can be randomly selected, the preset sensing node can be selected according to the sensing capability of the sensing node, the coverage range of the sensing nodes can be viewed, and the sensing node with the highest contact ratio between the coverage range and other sensing nodes is selected as the sensing node of the selected threshold;
because the difference of the sensing capabilities of the sensing nodes is not large in a certain technical capability range, the difference of the divided polygons is not too large; however, the existing sensing nodes may generate a sensing range and a sensing capability with large differences due to different setting times;
step SA 3: if the queue of the polygon area to be processed is empty, go to step SA 4; otherwise go to step SA 2;
step SA 4: for each sensing node which is not covered in the polygonal area, constructing the polygonal area based on the existing boundary points, and establishing the polygonal area based on the sensing capability of one sensing node by taking one sensing node as a center; in the construction process, the existing boundary points are utilized to the maximum extent, and the utilized existing boundary points are positioned by taking the preset sensing nodes as the center to take the senseKnowing that the sensing range of the node is in a circumscribed circle with a radius; selecting perceptual loss a when there are multiple existing boundary points available I，j Constructing a plurality of minimum boundary points; the perception loss is calculated by adopting the following formula;
Figure BDA0003133984440000071
wherein: h is I Is the distance between the jth boundary point and sensing node I; s is I The sensing range of the sensing node I is assumed to be circular, so that the sensing range participates in calculation by using a sensing radius R; the perception radius is related to the perception capability;
when the sensing node is created, the sensing node is created by taking one sensing node as a center as much as possible, namely, on the basis of keeping the polygonal shape of a polygonal area, a plurality of sensing nodes are prevented from being included in the same sensing node capacity range, and when the capacity of one sensing node completely covers the capacity range of another sensing node, the covered sensing node can be covered into the polygonal area corresponding to the another sensing node; thus, two sensing nodes play a role of redundancy, or one sensing node is wasted;
preferably: when a plurality of sensing nodes exist in the same polygonal area, selecting one sensing node based on the credibility; alternatively: fusing information of the perception nodes for environmental control;
preferably, the following components: the polygon is a triangle or a triangular pyramid; the polygonal areas adopted by the same environment space are the same; as shown in fig. 2, the environment space is a two-dimensional space and is divided into triangular regions; by the division mode, under the initial condition, the balance is kept to the maximum extent, the occurrence of a long and narrow polygonal area is avoided, and the division is closest to regularization;
step S2: judging whether the environment space is divided completely; if yes, go to step S3; otherwise, adjusting the divided polygon region to eliminate the non-divided region; if all the undivided regions cannot be eliminated by adjustment, dividing the undivided regions into one or more polygonal regions based on the existing boundary points;
the adjusting the divided polygon area to eliminate the non-divided area specifically includes: acquiring adjacent polygon areas of the non-divided polygon areas, and adjusting adjacent edges or adjacent surfaces of the adjacent polygons on the premise that the distance between any point in any polygon area and a sensing node is smaller than the distance between any point and other sensing nodes so as to reduce the area of the non-divided polygons;
the adjusting of the adjacent sides or the adjacent surfaces of the adjacent polygons specifically comprises: selecting one mode from the following two optimization modes to enable the target value corresponding to the selected mode to be minimum, and continuously adjusting until the following two modes cannot be adopted for adjustment or the target value cannot be further reduced by adopting the following two adjustment modes;
preferably: target value
Figure BDA0003133984440000081
Wherein: ag 2 Is to adjust the sum of internal angles, Ag 1 Is to adjust the sum of front internal angles, Sz 2 Is adjusted area/volume, Sz 1 Is to adjust the area/volume, Ln, before 2 Is the number of the adjusted edges, Ln 1 Adjusting the number of the front edges; a, b and c are constant adjusting values;
the method I comprises the following steps: starting from an adjacent side/adjacent surface corresponding to the maximum inner angle of an undivided polygonal area, if the maximum inner angle is within a first angle range, adjusting the angle to be 180 degrees, and under the condition that the premise is not met, meeting the premise by adjusting the position of an intersection point of the adjacent side/an intersection line of the adjacent surface;
the second method comprises the following steps: starting from an adjacent side/adjacent surface corresponding to the minimum inner angle of the undivided polygonal area, if the minimum inner angle is within a second angle range, adjusting the angle to be 0 degree and satisfying the premise by adjusting the position of a non-intersection line of a non-intersection point/adjacent surface of the adjacent side under the condition that the premise is not satisfied; the adjustment direction is preferably such that the sizes of the adjacent edges/surfaces are close;
when all the undivided polygonal areas cannot be eliminated by adopting the two adjustment modes, calculating the mode of dividing the undivided polygonal areas according to the number of boundary points of the undivided polygonal areas, and selecting one mode for division, wherein the balance among the polygonal areas is ensured while the premise is satisfied; or the existing boundary points are adopted in the dividing process, under the condition that the existing boundary points are unavailable, the existing boundary points are used as the center, the sensing range of the sensing node is used as the radius to set a plurality of circumscribed circles, the intersection points of the circumscribed circles are used as the circle center, the sensing range of the sensing node is used as the radius to set the boundary circles, and the boundary points are set on the boundary circles to form a newly divided polygonal area; the constraint is that the number of line divisions is minimal;
preferably: randomly dividing or manually dividing the finally remained undivided area;
preferably: the first angular range is [120,180), (180,360); the second angular range is: (0, A); wherein A is the internal angle value of the polygonal region;
step S3: searching an optimal setting mode and outputting the optimal setting mode; specifically, the method comprises the following steps: calculating the average size of the polygonal area, and when the difference between the average size and the standard size exceeds a tolerance value, performing global adjustment on the divided polygonal area to find an optimal setting mode; otherwise, setting sensing nodes for all the polygon areas without sensing nodes, taking the setting modes of all the sensing nodes as the optimal setting mode, and outputting the optimal setting mode;
preferably: calculating an average size based on the number of divided regions in the environmental space and the total size of the environmental space; calculating a standard size based on the sensing capability of the sensing node and the total size of the environment space;
the divided polygonal area is globally adjusted to find an optimal setting mode: the method specifically comprises the following steps: for each polygon area, sequentially adjusting from most to least according to the difference between the size of the polygon area and the standard size exceeding the tolerance value, so that the difference between the size of the adjusted polygon area and the standard size is reduced; in the adjustment process, the distance between any point in the polygon area and the sensing node is required to be kept smaller than the distance between any point and other sensing nodes; if the sensing node is not set in the polygonal area, other sensing nodes are assumed to be located in the center/mass center of the polygonal area; after each area is adjusted, judging whether the difference between the average size and the standard size exceeds a tolerance value again, if so, continuing to repeat the step, otherwise, finishing the adjustment;
alternatively, the adjustment is not performed for each region, but only for a region where no sensing node is set;
the area combination can be considered to reduce the wasted area and the wasted sensing node setting, but the local optimum is continuously sought in the dividing process, so the effect brought by the area combination is not very obvious, and the area combination mode can be selected to eliminate possible fragments in the random dividing mode;
the various illustrative logical blocks, modules, and circuits described may be implemented or performed with a general purpose processor, a Digital Signal Processor (DSP), an ASIC, a field programmable gate array signal (FPGA) or other Programmable Logic Device (PLD), discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any commercially available processor, controller, microcontroller or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
The steps of a method or algorithm described in connection with the present disclosure may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may reside in any form of tangible storage medium. Some examples of storage media that may be used include Random Access Memory (RAM), Read Only Memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, and the like. A storage medium may be coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. A software module may be a single instruction, or many instructions, and may be distributed over several different code segments, among different programs, and across multiple storage media.
The functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions on a tangible computer-readable medium. The computer readable medium includes a computer readable storage medium. Computer readable storage media can be any available storage media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Additionally, propagated signals are not included within the scope of computer-readable storage media. Computer-readable media also includes communication media including any medium that facilitates transfer of a computer program from one place to another. The connection may be, for example, a communication medium. For example, if the software is transmitted from a web site, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of communication medium. Combinations of the above should also be included within the scope of computer-readable media. Alternatively or in addition, the functions described herein may be performed, at least in part, by one or more hardware logic components. For example, illustrative types of hardware logic components that may be used include Field Programmable Gate Arrays (FPGAs), program specific integrated circuits (ASICs), program specific standard products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and so forth.
Accordingly, a computer program product may perform the operations presented herein. For example, such a computer program product may be a computer-readable tangible medium having instructions stored (and/or encoded) thereon that are executable by one or more processors to perform the operations described herein. The computer program product may include packaged material.
Software or instructions may also be transmitted over a transmission medium. For example, the software may be transmitted from a website, server, or other remote source using a transmission medium such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, or microwave.
Further, modules and/or other suitable means for carrying out the methods and techniques described herein may be downloaded and/or otherwise obtained by a user terminal and/or base station as appropriate. For example, such a device may be coupled to a server to facilitate the transfer of means for performing the methods described herein. Alternatively, the various methods described herein can be provided via storage means (e.g., RAM, ROM, a physical storage medium such as a CD or floppy disk) so that the user terminal and/or base station can obtain the various methods when coupled to or providing storage means to the device. Further, any other suitable technique for providing the methods and techniques described herein to a device may be utilized.
The above description is only a preferred embodiment of the present invention, and all equivalent changes or modifications of the structure, characteristics and principles described in the present invention are included in the scope of the present invention.

Claims (4)

1. An environment-aware network optimization setting method, the method comprising:
step S1: constructing a network model of the current environment-aware network;
step S2: judging whether the environment space is divided completely; if so, go to step S3; otherwise, adjusting the divided polygon region to eliminate the non-divided region; if all the undivided regions cannot be eliminated by adjustment, dividing the undivided regions into one or more polygonal regions based on the existing boundary points;
step S3: searching an optimal setting mode and outputting the optimal setting mode;
the step S1 specifically includes: the current environment space is divided into polygonal areas, and the current environment space can be divided into a space structure divided by a polygonal structure through polygonal division; the polygonal area is defined by boundary points Bi, sensing nodes Ii are arranged in the polygonal area, and the sensing nodes are used for sensing the environmental information in the polygonal area where the sensing nodes are located; the distance between any point in the polygonal area and the sensing node is smaller than the distance between any point and other sensing nodes; the size of the polygonal area is related to the perception capability of the perception node, the larger the perception capability is, the larger the corresponding polygonal area is, and vice versa;
the method for dividing the current environment space into the polygonal areas specifically comprises the following steps:
step SA 1: acquiring an initial sensing node; constructing a polygonal area for the initial sensing node; the constructed polygonal area is matched with the perception capability of the initial perception node; when the environment space has a preset sensing node, the initial sensing node is the sensing node closest to the center of the environment space; otherwise, setting a sensing node at a position which is nearest to the center of the environment space and can be set; putting the constructed polygonal area into a polygonal area queue to be processed;
step SA 2: acquiring a polygon area to be processed from the head of a polygon area queue, and constructing an adjacent polygon area for the polygon area to be processed; constructing adjacent polygon areas based on each adjacent surface or each adjacent edge in sequence, and constructing a maximum adjacent polygon area capable of covering a preset sensing node if one adjacent polygon area exists so that the adjacent polygon area can cover the preset sensing node; the boundary point of the adjacent polygonal area is positioned in an circumscribed circle which takes the preset sensing node as the center and takes the sensing range of the sensing node as the radius; otherwise, not constructing adjacent polygonal areas for the adjacent surfaces/edges; putting the constructed polygonal area into the tail of a queue of the polygonal area to be processed;
step SA 3: if the queue of the polygon area to be processed is empty, go to step SA 4; otherwise go to step SA 2;
step SA 4: for each sensing node which is not covered in the polygonal area, constructing the polygonal area based on the existing boundary points, and establishing the polygonal area based on the sensing capability of one sensing node by taking one sensing node as a center; in the construction process, the existing boundary points are utilized to the maximum extent, and the utilized existing boundary points are positioned in a circumscribed circle which takes the preset sensing node as the center and the sensing range of the sensing node as the radius.
2. The method as claimed in claim 1, wherein when there are multiple sensing nodes in the same polygon region, one sensing node is selected based on the confidence level.
3. The method for optimizing setting of environment-aware network according to claim 2, wherein information of aware segments is fused for environment control.
4. The method for optimizing setting of environment-aware network according to claim 3, wherein the polygonal area is two-dimensional or three-dimensional space.
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