A multi-domain data transmission and presentation method based on WiFi networking

Abstract

The present invention discloses a multi-domain data transmission and presentation method based on WiFi networking, which belongs to the field of wireless communication technology. It includes the following steps: pre-processing the multi-domain data perceived by distributed nodes, eliminating abnormal data and converting it into a standard format; the distributed nodes perform WiFi networking and transmit the multi-domain data to fixed nodes; the fixed nodes forward the received data to the multi-domain data processing and display platform; the platform stores the received multi-domain data in a local database, and then interpolates and fills the data; the multi-domain data processing and display platform uses a mapping algorithm to visualize the multi-domain data. The present invention can effectively solve the reliable transmission and mapping problems of multi-domain data in an unknown environment, and has a good application effect.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communication technology, and in particular relates to a multi-domain data transmission and presentation method based on WiFi networking. Background Art

[0002] In recent years, with the rapid development of information technology, new challenges have been brought to situational awareness technology. Situational awareness based on field maps needs to be based on multiple domains. A single perception field is highly correlated with the differences in sensor environmental adaptability, detection performance, and field spatial distribution. Situational awareness based on a single field has the problem of insufficient reliability. Through multi-domain field interaction, the spatial distribution characteristics and sensor characteristics of different fields can be fully utilized, and it has stronger adaptability. With the development of information transmission, fusion, visualization and other technologies, the connotation of situation information has been greatly expanded, and the transmission, aggregation and visualization of multi-domain data have become a research hotspot.

[0003] At present, most of the research on field map construction is based on a single field, but the field map constructed based on a single field has the problem of insufficient reliability and cannot meet the current credibility requirements for situation information. Therefore, the research on situation information has gradually turned to multi-domain. Compared with single-domain data, multi-domain data can not only increase the data dimension and information volume, but also improve the credibility of field maps. However, the existing technology still lacks a method for transmitting and presenting multi-domain data. Summary of the invention

[0004] In view of this, the present invention proposes a multi-domain data transmission and presentation method based on WiFi networking, which can realize the perception, reliable transmission and visual presentation of multi-domain data in indoor and outdoor environments.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A multi-domain data transmission and presentation method based on WiFi networking includes the following steps:

[0007] Step 1: Data preprocessing: Distributed nodes perceive multi-domain data of the surrounding environment through the sensors they carry. Distributed nodes preprocess the acquired multi-domain data, remove abnormal data, and convert the multi-domain data into a standard format.

[0008] Step 2, data transmission: the distributed nodes transmit the multi-domain data in a standard format to the fixed nodes through WiFi networking, and the fixed nodes transmit the collected multi-domain data to the multi-domain data processing and display platform through wired means;

[0009] Step 3, multi-domain data interpolation: After receiving the multi-domain data, the multi-domain data processing and display platform stores the data in the local database and then fills the data using the interpolation method;

[0010] Step 4: Data visualization: The multi-domain data processing and display platform stores the processed data in the local database, and visualizes the data in the form of a heat map through a mapping method.

[0011] Furthermore, the specific method of step 1 is:

[0012] The sensor samples once every ΔT time, and collects I data each time, with a total of m different types, m≤I;

[0013] Observe the data stream sequence in the most recent sampling period through a sliding window, take a sliding window of length |W| for the data stream, and divide the window into m blocks of size n, B1, B2, ... B m ; If the sampling frequency is f times / minute, the window is full after mn / f minutes, and the matrix r composed of the data sampled multiple times by the sensor is obtained:

[0014]

[0015] Among them, t1, t2, …, t n is the sampling time;

[0016] For the multi-domain data entering the sliding window at a new moment, calculate its distance from the multi-domain data at the previous moment:

[0017]

[0018] In the formula, j and i are the two sampling moments before and after, x i and x j is the sampling data at the corresponding moment, x ik 、x jk Represents x i 、x j The kth data in ;

[0019] Calculate x i and x j Similarity:

[0020] q(x i ,x j )=1 / d(x i ,x j )

[0021] If the similarity is less than 0.5, the multi-domain data at the new moment is determined to be abnormal data and is directly removed;

[0022] Convert the multi-domain data after exception processing into a standard format, which includes the device number, longitude, latitude, altitude values, as well as geomagnetic intensity and electromagnetic intensity.

[0023] Furthermore, the specific method of step 2 is:

[0024] The nodes are networked, fixed nodes start AP mode, distributed nodes start Managed mode and connect to fixed nodes, and fixed nodes are connected to the multi-domain data processing and display platform using network cables.

[0025] Distributed nodes transmit multi-domain data in standard format to fixed nodes through WiFi networking, and fixed nodes transmit the collected multi-domain data to the multi-domain data processing and display platform through wired mode; the data transmission mode is UDP transmission, and the transmission frequency is 1Hz;

[0026] The fixed node receives the background instruction of the multi-domain data processing and display platform. If the background instruction is a termination instruction, it will be forwarded to the distributed node; if the fixed node and the distributed node receive the termination instruction, the transmission of the multi-domain data will be terminated, otherwise the multi-domain data will continue to be transmitted.

[0027] Furthermore, in step 3, the specific method of filling the data using the interpolation method is:

[0028] According to the stored multi-domain data, the moth-to-flame optimization algorithm is used to fit the variogram, and a curve that can estimate the variogram value of any point z is obtained, where z corresponds to the multi-domain data perceived at a sampling moment;

[0029] Calculate all known points z i 、z j The variogram value r ij , i,j=1,2,...,n;

[0030] For an unknown point z0, calculate its distance to all known points z i The variogram value r i0 ;

[0031] Solve the following system of equations to obtain the optimal coefficient λ i , i=1,2,...,n:

[0032]

[0033] Then, the estimated value of the unknown point z0 is obtained according to the following formula:

[0034]

[0035] Furthermore, in step 4, after receiving the data, the multi-domain data processing and display platform first calls the data storage interface to store the data in the local database, and then uses the mapping algorithm to draw the data on the map in the form of a heat map.

[0036] The beneficial effects of the present invention are:

[0037] 1. The present invention proposes a multi-domain data transmission and presentation method based on WiFi networking. Through data preprocessing algorithm, interpolation algorithm, node networking, multi-domain mapping and other technical means, it solves the problem of perception and visualization of multi-domain data in indoor and outdoor environments, and has good application prospects.

[0038] 2. The multi-domain scene map construction method proposed in the present invention solves the problem of insufficient reliability of single scene situation awareness, improves the credibility of constructed scene maps, and has research significance in the field of multi-domain situation awareness technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 The following is a workflow diagram for distributed nodes.

[0040] Figure 2 This is a workflow diagram for fixed nodes. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] A multi-domain data transmission and presentation method based on WiFi networking includes the following steps:

[0043] Step 1, data preprocessing: The node senses electromagnetic, geomagnetic and other data of the surrounding environment through the sensors it carries. The node preprocesses the acquired data, removes abnormal data and converts the data into a defined standard format;

[0044] Step 2, data transmission: the distributed nodes transmit the multi-domain data in standard format to the fixed nodes through WiFi networking. The fixed nodes transmit the collected multi-domain data to the multi-domain data processing and display platform through wired transmission, using UDP transmission with a transmission frequency of 1 Hz.

[0045] Step 3, multi-domain data interpolation: After receiving the data, the multi-domain data processing and display platform stores the data in the local database, and then uses the interpolation algorithm to fill the data;

[0046] Step 4: Data visualization: The multi-domain data processing and display platform stores the processed data in the local database, and visualizes the data in the form of a heat map through a mapping algorithm.

[0047] Furthermore, in step 1, the data preprocessing method is as follows:

[0048] When the data collected by the sensor node is significantly different from the data at the previous sampling moment, it is called abnormal data.

[0049] The sensor node samples once every ΔT time. At a certain sampling time f, the m different types of data collected can be regarded as a data point set X in an m-dimensional space. The data set of the node in a sampling period can be regarded as a matrix r:

[0050]

[0051] Among them, t1, t2, …, t n is the sampling time.

[0052] The sliding window model is used to observe the data stream sequence in the most recent sampling period. The method is to take a sliding window of length |W| on the data stream and divide the window into m blocks of size n, B1, B2, ... B m If the sampling frequency is f times / minute, the window will be full after mn / f minutes. The new time t new The data entering the sliding window and the time t at which it will be replaced old The relationship between the sampling data is as follows:

[0053] mod(t new ,|W|)=mod(t old ,|W|)

[0054] Where mod(a,b) represents the modulo function. The new sampling time t new The data at the moment is placed in the sliding window B i In the block, the distance calculation formula of the data points is as follows:

[0055]

[0056] In the formula, j and i are the two sampling moments before and after, x i and x j is the sampling data at the corresponding moment, x ik 、x jk Represents x i 、x j The kth data in ;

[0057] The perception data x at sampling time i and sampling time j i and x j The similarity is:

[0058] q(x i ,x j )=1 / d(x i ,x j)

[0059] The distance between two identical data is 0 and the similarity is 1. If the relative distance between two data is infinite, their similarity tends to 0. Therefore, the similarity between two sensor readings is always between (0,1].

[0060] Data similarity is an important criterion for measuring the similarity of two sensor readings, and is also an important basis for measuring whether a data point is abnormal. Data points that are determined to be abnormal are directly removed to complete data preprocessing.

[0061] Furthermore, the specific method of step 2 is:

[0062] The nodes are networked, fixed nodes are turned on in AP mode, other distributed nodes are turned on in Managed mode and connected to AP, and fixed nodes are connected to the multi-domain data processing and display platform using network cables.

[0063] The distributed nodes send the multi-domain data in a standard format after data preprocessing to the fixed nodes through networking transmission, with a transmission frequency of 1Hz. They also receive the background instructions forwarded by the fixed nodes and determine whether they have received the termination instruction. If so, they terminate the data transmission, otherwise, they continue to send data.

[0064] The workflow of distributed nodes is as follows: Figure 1 As shown in the figure, the workflow of a fixed node is as follows Figure 2 shown.

[0065] Furthermore, the data interpolation algorithm in step 3 is as follows:

[0066] Assume that the variance and expectation of any point in the space are constants, that is:

[0067] E[z(x)]=c

[0068] D[z(x)]=σ 2

[0069] Based on the above assumptions, the value of any point can be composed of the average value of the space and the random deviation R(x) of the point, that is:

[0070] z(x)=E[z(x)]+R(x)=c+R(x)

[0071] Among them, R(x) represents the random deviation at point x, and its variance is a constant, that is:

[0072] Var[R(x)]=σ 2

[0073] The interpolation formula is:

[0074]

[0075] in, is the estimated value at the point (x0, y0), that is, z0 = z(x0, y0), λ i It is a set of optimal coefficients with the smallest error between the sampling point and the estimated point, and satisfies and unbiased Condition, as long as λ is calculated i , the estimated value of the point can be calculated by weighted summation. The value of the regionalized variable at any point in space can be estimated by a linear combination of the known observation point values.

[0076] The specific steps are as follows:

[0077] (1) According to the stored multi-domain data, the moth-to-flame optimization algorithm is used to fit the variogram to obtain a curve that can estimate the variogram value of any point z, where z corresponds to the multi-domain data perceived at a sampling time;

[0078] (2) Calculate all known points z i 、z j The variogram value r ij , i,j=1,2,...,n;

[0079] (3) For an unknown point z0, calculate its distance to all known points z i The variogram value r i0 ;

[0080] (4) Solve the following equations to obtain the optimal coefficient λ i , i=1,2,...,n:

[0081]

[0082] (5) Calculate the estimated value of the unknown point z0 according to the following formula:

[0083]

[0084] Furthermore, in step 4, after receiving the data, the multi-domain data processing and display platform first calls the data storage interface to store the data in the local database. The storage format is shown in Table 1:

[0085] Table 1

[0086] DeviceId Ln Lat H GeoMag ElecMag 1 114.809 39.534 4.69 28194 25.478

[0087] Then, the data is plotted on the map using a heat map using a mapping algorithm.

[0088] The above is a detailed introduction to a multi-domain data transmission and presentation method based on WiFi networking proposed in the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. For those of ordinary skill in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and scope of application. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A multi-domain data transmission and presentation method based on WiFi networking, characterized in that: The following steps are involved: Step 1: Data preprocessing: The distributed nodes perceive the multi-domain data of the surrounding environment through the sensors they carry. The distributed nodes preprocess the acquired multi-domain data, remove abnormal data, and convert the multi-domain data into a standard format. Step 2, data transmission: Distributed nodes transmit multi-domain data in standard format to fixed nodes through WiFi networking, and fixed nodes transmit the collected multi-domain data to the multi-domain data processing and display platform through wired means; the specific method is: The nodes are networked, fixed nodes start AP mode, distributed nodes start Managed mode and connect to fixed nodes, and fixed nodes are connected to the multi-domain data processing and display platform using network cables. Distributed nodes transmit multi-domain data in a standard format to fixed nodes through WiFi networking, and fixed nodes transmit the collected multi-domain data to the multi-domain data processing and display platform through wired means; The data transmission method is UDP transmission, and the transmission frequency is 1Hz; The fixed node receives the background instruction of the multi-domain data processing and display platform. If the background instruction is a termination instruction, it will be forwarded to the distributed node; if the fixed node and the distributed node receive the termination instruction, the transmission of the multi-domain data will be terminated, otherwise the multi-domain data will continue to be transmitted; Step 3, multi-domain data interpolation: After receiving the multi-domain data, the multi-domain data processing and display platform stores the data in a local database, and then fills the data with an interpolation method; the specific method of filling the data with the interpolation method is as follows: According to the stored multi-domain data, the moth-to-flame optimization algorithm is used to fit the variogram, and a curve that can estimate the variogram value of any point z is obtained, where z corresponds to the multi-domain data sensed at a sampling moment; Calculate all known points z i 、z j The variogram value r ij , i,j=1,2,...,n; For an unknown point z0, calculate its distance to all known points z i The variogram value r i0 ; Solve the following system of equations to obtain the optimal coefficient λ i , i=1,2,...,n: Then, the estimated value of the unknown point z0 is obtained according to the following formula: Step 4, data visualization: the multi-domain data processing and display platform stores the processed data in the local database, and visualizes the data in the form of a heat map through a mapping method; after receiving the data, the multi-domain data processing and display platform first calls the data storage interface to store the data in the local database, and then uses the mapping algorithm to draw the data on the map in the form of a heat map.

2. According to the multi-domain data transmission and presentation method based on WiFi networking according to claim 1, it is characterized in that: The specific method of step 1 is: The sensor samples once every ΔT time, and collects I data each time, with a total of m different types, m≤I; Observe the data stream sequence in the most recent sampling period through a sliding window, take a sliding window of length |W| for the data stream, and divide the window into m blocks of size n, B1, B2, ... B m ; If the sampling frequency is f times / minute, the window is full after mn / f minutes, and the matrix r composed of the data sampled multiple times by the sensor is obtained: Among them, t1, t2, …, t n is the sampling time; For the multi-domain data entering the sliding window at a new moment, calculate its distance from the multi-domain data at the previous moment: In the formula, j and i are the two sampling moments before and after, x i and x j is the sampling data at the corresponding moment, x ik 、x jk Represents x i 、x j The kth data in ; Calculate x i and x j Similarity: q(x i ,x j )=1 / d(x i ,x j ) If the similarity is less than 0.5, the multi-domain data at the new moment is determined to be abnormal data and is directly removed; Convert the multi-domain data after exception processing into a standard format, which includes the device number, longitude, latitude, altitude values, as well as geomagnetic intensity and electromagnetic intensity.

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