CN111353113B - Visual rendering method suitable for high-frequency state data of unmanned aerial vehicle - Google Patents
Visual rendering method suitable for high-frequency state data of unmanned aerial vehicle Download PDFInfo
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Abstract
The invention discloses a visual rendering method suitable for high-frequency state data of an unmanned aerial vehicle, which comprises the steps of analyzing the change rule of each type of state information in the historical state data of the unmanned aerial vehicle along with time, presetting a corresponding calculation function, receiving the real state data of the unmanned aerial vehicle, drawing the real state data on a browser view at a low frequency rate, substituting the real state data of the unmanned aerial vehicle into the calculation function to calculate the predicted state information of a future moment, judging whether the error between the predicted state information of the unmanned aerial vehicle at the future moment and the real state information of the unmanned aerial vehicle received at the future moment is smaller than a preset threshold value or not, drawing the predicted state information of the unmanned aerial vehicle at the future moment on the browser view if the error is smaller than the preset threshold value, and realizing high-frequency rendering. According to the method, the state information of the unmanned aerial vehicle is predicted, the high-frequency rendering performance of the state information data of the unmanned aerial vehicle on the browser is optimized, the smoothness and the real-time performance of state display of the unmanned aerial vehicle are improved, and the problem of browser rendering blocking is effectively solved.
Description
Technical Field
The invention relates to the field of visual monitoring of unmanned aerial vehicles, in particular to a visual rendering method suitable for high-frequency state data of an unmanned aerial vehicle.
Background
Unmanned aerial vehicle has been the research focus in the field of aviation in recent years, because its characteristics such as with low costs, mobility are strong, function diversification, use more and more extensively in our life, more and more fields begin to explore and use unmanned aerial vehicle to replace artifical the operation, for example: military operations, geological surveying, logistics transportation, agricultural applications, film and television shooting, fire fighting and disaster resistance, rescue patrolling and the like.
Unmanned aerial vehicles have achieved certain achievements in the field of power inspection. Traditional manual work is patrolled and examined and is relied on artifical tower that climbs, inspects electric tower equipment, and working strength is high, and the risk is big, patrols and examines inefficiency. Unmanned aerial vehicle relies on its stable spatial position control, the quick real-time image transmission of high definition for the personnel of patrolling and examining can control unmanned aerial vehicle on ground, realize the inspection of remote iron tower.
The real-time state monitoring of the unmanned aerial vehicle is an important link of the remote operation and the unattended operation of the unmanned aerial vehicle. At the long-range operation in-process of unmanned aerial vehicle, unmanned aerial vehicle keeps away from circuit maintainer, and circuit maintainer hardly accurately knows the current various states of unmanned aerial vehicle through the naked eye observation, can't carry out accurate operation. In a complete unmanned aerial vehicle transmission of electricity system of patrolling and examining, be equipped with the visual interface of controlling of one set of unmanned aerial vehicle that the function is perfect usually, the circuit maintainer can carry out remote operation to unmanned aerial vehicle through visual interface of controlling to various state indexes of real time monitoring unmanned aerial vehicle (for example, flight attitude, geographical positional information, electric quantity, airspeed, GPS health degree etc.). If some extreme condition appear, lead to the unmanned aerial vehicle task to carry out unusually, unmanned aerial vehicle control system can carry out quick strain measure to the unusual condition through monitoring the unmanned aerial vehicle state to in time inform the circuit maintainer, prevent the emergence accident.
To accomplish unmanned aerial vehicle real-time data control, need unmanned aerial vehicle to send unmanned aerial vehicle's state information data to control system with high frequency, but because the actual work environment all is in the field, the hardware equipment of unmanned aerial vehicle control system operation all is the computer of industrial level, has certain advantage to ordinary computer in stability, but can be relatively poor in the performance. If do the rendering with unmanned aerial vehicle's state information data according to same high frequency time on unmanned aerial vehicle controls the interface, can appear the system card pause, render up the obstructed problem of smoothness degree, be unfavorable for the circuit maintenance personnel to look over unmanned aerial vehicle real-time status.
Disclosure of Invention
The invention aims to provide a visual rendering method suitable for high-frequency state data of an unmanned aerial vehicle, which comprises the steps of analyzing the change rule of each type of state information in the historical state data of the unmanned aerial vehicle along with time, presetting a corresponding calculation function, receiving the real state data of the unmanned aerial vehicle, drawing the real state data on a browser view at a low frequency rate, substituting the real state data of the unmanned aerial vehicle into the calculation function to calculate the predicted state information of a future moment, judging whether the error between the predicted state information of the unmanned aerial vehicle at the future moment and the real state information of the unmanned aerial vehicle received at the future moment is smaller than a preset threshold value, drawing the predicted state information of the unmanned aerial vehicle at the future moment on the browser view if the error is smaller than the preset threshold value, and realizing high-frequency rendering. According to the method, the state information of the unmanned aerial vehicle is predicted, the high-frequency rendering performance of the state information data of the unmanned aerial vehicle on the browser is optimized, the smoothness and the real-time performance of state display of the unmanned aerial vehicle are improved, and the problem of browser rendering blocking is effectively solved.
To achieve the above object, with reference to fig. 1, the present invention provides a visual rendering method suitable for high-frequency state data of an unmanned aerial vehicle, where the method includes:
s1: and analyzing the prestored historical state data of the unmanned aerial vehicle, and presetting and calling a corresponding calculation function according to the change rule of each type of state information in the historical state data of the unmanned aerial vehicle along with time.
S2: the communication connection between the unmanned aerial vehicle and the background server is established, the real state data of the unmanned aerial vehicle are received, and the real state data of the unmanned aerial vehicle are stored in the first waiting queue.
S3: establishing communication connection between a background server and a browser, extracting real state data of the unmanned aerial vehicle from a first waiting queue at a first preset time interval, storing the real state data in a second waiting queue, setting a timing function, extracting the real state data of the unmanned aerial vehicle from the second waiting queue at a second preset time interval, and drawing the real state data of the unmanned aerial vehicle on a browser view.
The first preset time interval is smaller than the second preset time interval.
S4: and substituting each type of state information in the real state data of the unmanned aerial vehicle into a corresponding calculation function for calculation to obtain the predicted state information of the unmanned aerial vehicle at a future moment, and storing the predicted state information of the unmanned aerial vehicle at the future moment.
S5: and comparing the predicted state information of the unmanned aerial vehicle at a future moment with the real state information of the unmanned aerial vehicle received at the future moment, jumping to the step S6 if the error between the predicted state information and the real state information of the unmanned aerial vehicle is smaller than a preset threshold value, and otherwise, ending the rendering process.
S6: inserting the unmanned aerial vehicle predicted state information at a certain future moment into a browser DOM node, rendering the page, and rendering the browser at the time point corresponding to the unmanned aerial vehicle predicted state information.
In a further embodiment, the change rule of each type of state information with time includes: linear relationship, exponential relationship, custom relationship.
In a further embodiment, the corresponding calculation function comprises: linear function, exponential function, custom relationship function.
In a further embodiment, the historical state data of the drone includes a part of the drone state data and all the drone state data stored in advance.
In a further embodiment, FP-free can be adopted to mine the change rule of each type of state information in the historical state data of the unmanned aerial vehicle along with time.
In a further embodiment, the communication connection between the server and the browser includes a websocket connection.
In a further embodiment, the method further comprises:
setting N variable parameters in the calculation function, wherein N is more than or equal to 1.
And calculating the error between the predicted state information of the unmanned aerial vehicle at a future moment and the real state information of the unmanned aerial vehicle received at the future moment.
And feeding back the calculation result to a calculation function, and correcting the variable parameters.
In a further embodiment, the process of variable parameter modification can be decomposed into:
(1) predicting the state information g of the unmanned aerial vehicle at the current time tnowObtaining the required known parameters according to the numerical value of time, wherein the known parameters comprise: the last prediction result of g, k pieces of real state information data g (T) in the time period T1),g(t2)… g(tk)。
(2) Defining the influence weight W of the last prediction result on the next prediction resultp。
Defining a binary function f (x), wherein when x>Time 0 f (x)>0, for generating g (t)1),g(t2)…g(tk) Influence coefficient on prediction results.
Defining a correction weight W for a deviation between a predicted result and a true resultl。
(3) Let t0＝tnow-T, calculating f (T)1–t0),f(t2–t0)……f(tk-t0) The value of (c).
(4) Establishing an xOy coordinate system, inserting ((t)1-t0),f(t1–t0)*g(t1))，((t2-t0),f(t2–t0)*g(t2))……((tk-t0),f(tk– t0)*g(tk) Values for these k points and a function F point-fitted to the coordinate system is obtained by newton's interpolation.
(5) When x ═ T is calculated, the value of the function F is taken to obtain the initial value g' ═ F (T)/F (T) of the prediction result.
(6) According to the last prediction result glastAdjusting the value of g' to obtain gm＝(g’+glast*Wp)/(1+Wp)。
(7) Determining the adjusted prediction result gmIf the state information deviates from the known state information, the numerical value g of the closest state information in the state information is usedlAdjusting gmTo obtain gm’，gm’＝(gm+gl*Wl)/(1+Wl)。
(8) And (4) executing the steps (3) to (7) on all the generated variable parameter sets, calculating to obtain and store the corresponding prediction results, and selecting the value of the variable parameter set with the best current score as the value of the current prediction result.
(9) And receiving real state information update of the unmanned aerial vehicle, correcting the prediction result according to the real state information update, grading the generated variable parameter set again, and selecting the variable parameter set with the best grade.
In a further embodiment, the status data includes flight attitude, geographical location information, electrical quantity, flight speed, GPS health.
Compared with the prior art, the technical scheme of the invention has the following remarkable beneficial effects:
(1) according to the invention, by analyzing the historical state data of the unmanned aerial vehicle and presetting and calling the corresponding calculation function according to the change rule of each type of state information along with time, the state information of the unmanned aerial vehicle can be predicted, and the state information data at a certain future moment can be predicted in advance.
(2) According to the method, the change trend of the state information of the unmanned aerial vehicle is predicted, the high-frequency rendering performance of the state information data of the unmanned aerial vehicle on the browser is optimized, the smoothness and the real-time performance of state display of the unmanned aerial vehicle are improved, and the problem of browser rendering blocking is effectively solved.
(3) The method and the device can select different browser rendering strategies according to different resource conditions, effectively solve the problem that the javascript engine and the rendering engine compete for resources, and realize the balance of browser performance optimization and data real-time display.
(4) The method utilizes the variable parameters in the calculation function to feed the error between the predicted data set and the actual data set of the unmanned aerial vehicle back to the calculation function, and corrects the variable parameters of the calculation function so as to ensure the effectiveness of the predicted data.
It should be understood that all combinations of the foregoing concepts and additional concepts described in greater detail below can be considered as part of the inventive subject matter of this disclosure unless such concepts are mutually inconsistent. In addition, all combinations of claimed subject matter are considered a part of the presently disclosed subject matter.
The foregoing and other aspects, embodiments and features of the present teachings will be more fully understood from the following description taken in conjunction with the accompanying drawings. Additional aspects of the present invention, such as features and/or advantages of exemplary embodiments, will be apparent from the description which follows, or may be learned by practice of specific embodiments in accordance with the teachings of the present invention.
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The drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures may be represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. Embodiments of various aspects of the present invention will now be described, by way of example, with reference to the accompanying drawings, in which:
fig. 1 is a flowchart of a visualization rendering method suitable for high-frequency state data of an unmanned aerial vehicle according to the present invention.
Detailed Description
In order to better understand the technical content of the present invention, specific embodiments are described below with reference to the accompanying drawings.
In this disclosure, aspects of the present invention are described with reference to the accompanying drawings, in which a number of illustrative embodiments are shown. Embodiments of the present disclosure are not necessarily defined to include all aspects of the invention. It should be appreciated that the various concepts and embodiments described above, as well as those described in greater detail below, may be implemented in any of numerous ways, as the disclosed concepts and embodiments are not limited to any one implementation. In addition, some aspects of the present disclosure may be used alone, or in any suitable combination with other aspects of the present disclosure.
With reference to fig. 1, the invention provides a visual rendering method suitable for high-frequency state data of an unmanned aerial vehicle, and the specific embodiment is as follows:
s1: and analyzing the prestored historical state data of the unmanned aerial vehicle, and presetting and calling a corresponding calculation function according to the change rule of each type of state information in the historical state data of the unmanned aerial vehicle along with time.
Firstly, collecting part or all of the historical state data of the unmanned aerial vehicle as a training set, analyzing the prestored historical state data of the unmanned aerial vehicle, and presetting and calling a corresponding calculation function according to the change rule of each type of state information in the historical state data of the unmanned aerial vehicle along with time.
Specifically, the relation between each type of state information in the unmanned aerial vehicle state data and time is judged, and if the relation is linear with the time, a linear correlation function is used as a calculation function. If the time is exponential, the exponential correlation function is used as the calculation function.
Preferably, association rules of various types of state information of the unmanned aerial vehicle changing along with time can be mined by using the FP-tree.
Preferably, if the correlation function in the existing basic algorithm cannot describe the change relationship of the state information along with time, the user can also customize the state correlation function of the unmanned aerial vehicle, and the customized function is taken as a calculation function to cope with the situation of a complex change rule.
S2: establishing communication connection between the unmanned aerial vehicle and the background server, receiving real state data of the unmanned aerial vehicle, and storing the real state data of the unmanned aerial vehicle in a first waiting queue.
And establishing communication connection between the unmanned aerial vehicle and a background server, and receiving real-time state data transmitted back by the unmanned aerial vehicle by the background server. And a storage queue with a fixed length is set at the background server side as a first waiting queue, and the background server side stores the received real state data of the unmanned aerial vehicle in the waiting queue.
Preferably, the real state data of the unmanned aerial vehicle which is firstly input into the storage queue is input into the tail of the queue, and the first-in first-out principle is observed.
Preferably, in order to deal with the situation of returning a large amount of unmanned aerial vehicle state data, the length of the storage queue can be set to be variable.
S3: establishing communication connection between a background server and a browser, extracting real state data of the unmanned aerial vehicle from a first waiting queue at a first preset time interval, storing the real state data in a second waiting queue, setting a timing function, extracting the real state data of the unmanned aerial vehicle from the second waiting queue at a second preset time interval, and drawing the real state data of the unmanned aerial vehicle on a browser view;
the first preset time interval is smaller than the second preset time interval.
And starting browser rendering, establishing communication connection between the background server and the browser, wherein the communication connection between the server and the browser adopts a websocket connection mode.
And setting a storage queue with a fixed length as a second waiting queue, and reading real state data of the unmanned aerial vehicle from the tail of the first waiting queue by the browser at a fixed frequency and storing the real state data in the second waiting queue. And the browser reads the real state data of the unmanned aerial vehicle from the second waiting queue at regular time intervals according to a preset timing cache function, and draws the real state data on a view of the browser.
In a specific embodiment, the frequency of receiving the real-time status information returned by the unmanned aerial vehicle by the background server is high, the interval time is short, and if the browser reads the stored real status data of the unmanned aerial vehicle according to the high frequency and performs browser rendering, the rendering tree calculation layout and the drawing view need to be frequently generated in a short time, which consumes resources. The present invention therefore sets 2 time intervals for reading data and browser rendering, respectively. The browser reads the stored data in the background of the server at a relatively high frequency and in a relatively short time interval, and the browser renders the data at a relatively low frequency and in a relatively long time interval. Therefore, the frequency of page rendering by the browser with real state data is reduced, and the problem of low real-time rendering picture is possibly caused. Therefore, the possible state information of the unmanned aerial vehicle at a future moment is predicted by adopting the real state data information of the unmanned aerial vehicle in the subsequent steps, the browser is rendered according to the prediction result, and the display fluency and real-time performance are guaranteed.
The specific embodiment is developed based on a browser, and the current browser rendering interface roughly comprises the following steps: the browser creates a DOM Tree (Document Object Model Tree), analyzes a CSS (Cascading Style Sheets) rule to generate a CSSOM Tree (Cascading Style Sheets Object Model Tree), generates a rendering Tree according to the DOM Tree and the CSS rule, and finally draws the rendering Tree on a screen.
The browser needs to analyze an HTML source code requested from a server first when creating a DOM tree, each HTML tag on the DOM tree generates a corresponding node, and a text in the HTML tag also has a corresponding text node. During HTML parsing, when some tags for loading static resources are encountered, a request, such as a link tag for loading css files, is initiated to the server. And (3) when the DOM tree is rendered, the browser analyzes the loaded CSS source code to generate a CSSOM tree. The priority of CSS concatenation is from low to high: the browser defaults to the basic rule, style sheets referenced by the user, and the highest priority is inline style that is externally introduced and eventually added to the style property of the HTML tag. After the DOM Tree and the CSSOM Tree are analyzed, the browser matches the DOM node with the CSS rule, and finally a Render Tree (Render Tree) is created.
The render tree resembles the DOM tree, but it is not one-to-one with the DOM tree. The rendering tree collects all DOM node information on the web page, and all CSSOM style information for each node. The DOM elements may have multiple nodes in the render tree, for example, there are some nested elements in the middle of each DOM node or one render node is required for the text of the current DOM element. In addition, a node on the rendering tree is referred to as a box. Each node has CSS box attributes including width, height, border, margin, etc. After the rendering tree is successfully created, the browser can accurately capture the exact position and size of each element in the viewport according to the rendering tree, and finally, each calculated node is drawn on the screen, so that a complete rendering process of the browser is completed.
And (3) state output of the unmanned aerial vehicle, the browser end receives a large amount of state data, and if the interface is rendered directly according to the data receiving frequency, the browser needs to frequently generate a rendering tree calculation layout and a drawing view in a short time, so that resources are consumed. In addition, due to the characteristic that the rendering engine thread is mutually exclusive with the javascript engine thread in the browser, when the javascript engine executes, the rendering engine can execute the rendering process only after the javascript engine finishes executing, which causes the resource contention between the javascript engine and the rendering engine. Therefore, because the unmanned aerial vehicle usually works in a field operation environment, the computing resources are relatively deficient, and the phenomenon that the unmanned aerial vehicle controls the interface to be jammed easily occurs.
S4: and substituting each type of state information in the real state data of the unmanned aerial vehicle into a corresponding calculation function for calculation to obtain the predicted state information of the unmanned aerial vehicle at a future moment, and storing the predicted state information of the unmanned aerial vehicle at the future moment.
And the background server substitutes each type of state information in the real state data of the unmanned aerial vehicle into a calculation function set in a training stage, calculates the predicted state information at a certain future moment, and stores the calculated predicted state information for subsequent judgment.
Specifically, taking the geographical location information as an example, by using the relationship between the change of the geographical location information and the speed and time in the flight process of the unmanned aerial vehicle, a related calculation function is called, a function correlation coefficient prediction value of the geographical location change in the current unmanned aerial vehicle data set can be obtained, and the geographical location information of the unmanned aerial vehicle at a future moment can be calculated by using the prediction values and combining the related function.
S5: and comparing the predicted state information of the unmanned aerial vehicle at a future moment with the real state information of the unmanned aerial vehicle received at the future moment, jumping to the step S6 if the error between the predicted state information and the real state information of the unmanned aerial vehicle is smaller than a preset threshold value, and otherwise, ending the rendering process.
The method comprises the steps of comparing predicted state information of the unmanned aerial vehicle at a certain future moment with real state information of the unmanned aerial vehicle received at the future moment, calculating errors of the predicted state information and the real state information of the unmanned aerial vehicle, judging the size of a calculation result and a preset minimum error threshold, executing a subsequent process if the calculated result is smaller than the preset threshold, not inserting the predicted state information into a browser DOM node for rendering if the calculated result is larger than or equal to the preset threshold, only using low-frequency real state information data of the unmanned aerial vehicle for rendering by a rendering strategy, avoiding the situation that the difference between information displayed by a browser and actual information is larger due to the adoption of prediction information with larger errors, and accordingly ensuring the smoothness of browser operation.
S6: inserting the unmanned aerial vehicle predicted state information at a certain future moment into a browser DOM node, rendering the page, and rendering the browser at the time point corresponding to the unmanned aerial vehicle predicted state information.
Under the condition that the predicted information is closer to the real state information, inserting the predicted state information of the unmanned aerial vehicle at a certain future moment into a browser DOM node, rendering the page, and rendering the browser at the time point corresponding to the predicted state information of the unmanned aerial vehicle.
Preferably, the method for visually rendering high-frequency state data of an unmanned aerial vehicle according to the present invention further includes:
setting N variable parameters in the calculation function, wherein N is more than or equal to 1.
And calculating an error between the predicted state information of the unmanned aerial vehicle at a future moment and the real state information of the unmanned aerial vehicle received at the future moment, feeding back a calculation result to a calculation function for correcting the variable parameter, and continuously adjusting to enable the calculation function to more accurately predict the state information of the unmanned aerial vehicle.
The process of modifying the variable parameter includes the steps of:
(1) predicting the state information g of the unmanned aerial vehicle at the current time tnowObtaining the required known parameters according to the numerical value of the time, wherein the known parameters comprise: the last prediction result of g, k real state information data g (T) in the time period T1),g(t2)… g(tk)。
(2) Defining the influence weight W of the last prediction result on the next prediction resultp。
Defining a binary function f (x), wherein when x>Time 0 f (x)>0, for generating g (t)1),g(t2)…g(tk) For predicted resultsThe influence coefficient.
Correction weight W defining the deviation of the predicted result from the true resultl。
(3) Let t0＝tnow-T, calculating f (T)1–t0),f(t2–t0)……f(tk-t0) The value of (c).
(4) Establishing an xOy coordinate system, interpolating ((t)1-t0),f(t1–t0)*g(t1))，((t2-t0),f(t2–t0)*g(t2))……((tk-t0),f(tk– t0)*g(tk) Values for these k points and a function F point-fitted to the coordinate system is obtained by newton interpolation.
(5) When x is calculated to be T, the value of the function F is taken to obtain an initial value g' F (T)/F (T) of the prediction result.
(6) According to the last prediction result glastAdjusting the value of g' to obtain gm＝(g’+glast*Wp)/(1+Wp)。
(7) Determining the adjusted prediction result gmIf the state information deviates from the known state information, the numerical value g of the closest state information in the state information is usedlAdjusting gmTo obtain gm’，gm’＝(gm+gl*Wl)/(1+Wl)。
(8) And (4) executing the steps (3) to (7) on all the generated variable parameter sets, calculating to obtain and store the corresponding prediction results, and selecting the value of the variable parameter set with the best current score as the value of the current prediction result.
Specifically, the geographic location information is taken as an example.
The browser outputs the result g for the last predictionlastAnd collecting the result g of two real sampling data before and after the real sampling data1And g2Through g1And g2Approximate value of (a) to calculate the true output result g of the last predictionapp. In the calculation process, if the real sampling data does not give the current glastVelocity component ofIf g is uniformly changed in the sampling time period, and if the real sampling data includes the current speed, g' can be uniformly accelerated in the sampling time period, so as to calculate gapp。
Updating the score m-m factor + for each set of variable parameter sets used for prediction data (g)app+g1)2Wherein the factor is a value set by the algorithm smaller than 1, gappThe smaller the final value of (a), the better the score representing the current variable parameter set.
(9) And receiving the real state information update of the unmanned aerial vehicle, correcting the prediction result according to the real state information update, re-grading the generated variable parameter set, and selecting the variable parameter set with the best grade.
In a further embodiment, the state data of the drone includes flight attitude, geographic location information, electrical quantity, flight speed, GPS health.
The invention provides a visual rendering method suitable for high-frequency state data of an unmanned aerial vehicle, which comprises the steps of analyzing the change rule of each type of state information in the historical state data of the unmanned aerial vehicle along with time, presetting a corresponding calculation function, receiving the real state data of the unmanned aerial vehicle, drawing the real state data on a browser view at a low frequency rate, substituting the real state data of the unmanned aerial vehicle into the calculation function to calculate the predicted state information of a future moment, judging whether the error between the predicted state information of the unmanned aerial vehicle at the future moment and the real state information of the unmanned aerial vehicle received at the future moment is smaller than a preset threshold value or not, drawing the predicted state information of the unmanned aerial vehicle at the future moment on the browser view if the error is smaller than the preset threshold value, and achieving high-frequency rendering. According to the method, the state information of the unmanned aerial vehicle is predicted, the high-frequency rendering performance of the state information data of the unmanned aerial vehicle on the browser is optimized, the smoothness and the real-time performance of state display of the unmanned aerial vehicle are improved, and the problem of browser rendering blocking is effectively solved.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention should be determined by the appended claims.
Claims (8)
1. A visual rendering method suitable for high-frequency state data of an unmanned aerial vehicle is characterized by comprising the following steps:
s1: analyzing prestored historical state data of the unmanned aerial vehicle, and presetting and calling a corresponding calculation function according to the change rule of each type of state information in the historical state data of the unmanned aerial vehicle along with time;
s2: establishing communication connection between the unmanned aerial vehicle and a background server, receiving real state data of the unmanned aerial vehicle, and storing the real state data of the unmanned aerial vehicle in a first waiting queue;
s3: establishing communication connection between a background server and a browser, extracting real state data of the unmanned aerial vehicle from a first waiting queue at a first preset time interval, storing the real state data in a second waiting queue, setting a timing function, extracting the real state data of the unmanned aerial vehicle from the second waiting queue at a second preset time interval, and drawing the real state data of the unmanned aerial vehicle on a browser view;
the first preset time interval is smaller than the second preset time interval;
s4: substituting each type of state information in the real state data of the unmanned aerial vehicle into a corresponding calculation function for calculation to obtain the predicted state information of the unmanned aerial vehicle at a future moment, and storing the predicted state information of the unmanned aerial vehicle at the future moment;
s5: comparing the predicted state information of the unmanned aerial vehicle at a certain future moment with the real state information of the unmanned aerial vehicle received at the moment, if the error between the predicted state information and the real state information of the unmanned aerial vehicle is smaller than a preset threshold value, jumping to the step S6, and if not, ending the rendering process;
s6: inserting the unmanned aerial vehicle predicted state information at a certain future moment into a browser DOM node, rendering the page, and rendering the browser at the time point corresponding to the unmanned aerial vehicle predicted state information.
2. The visual rendering method suitable for the high-frequency state data of the unmanned aerial vehicle according to claim 1, wherein the time-dependent change rule of each type of state information comprises: linear relation, exponential relation, custom relation;
the corresponding calculation function includes: linear function, exponential function, custom relationship function.
3. The visual rendering method suitable for the high-frequency state data of the unmanned aerial vehicle according to claim 1, wherein the historical state data of the unmanned aerial vehicle comprises a part of prestored state data of the unmanned aerial vehicle and all the state data of the unmanned aerial vehicle.
4. The visualization rendering method applicable to the high-frequency state data of the unmanned aerial vehicle of claim 1, wherein the method further comprises: and mining the change rule of each type of state information in the historical state data of the unmanned aerial vehicle along with time by adopting FP-free.
5. The visual rendering method suitable for the high-frequency state data of the unmanned aerial vehicle as claimed in claim 1, wherein the communication connection between the server and the browser includes a websocket connection.
6. The visualization rendering method applicable to the high-frequency state data of the unmanned aerial vehicle of claim 1, wherein the method further comprises:
setting N variable parameters in the calculation function, wherein N is more than or equal to 1;
calculating an error between the predicted state information of the unmanned aerial vehicle at a future moment and the real state information of the unmanned aerial vehicle received at the future moment;
and feeding back the calculation result to a calculation function, and correcting the variable parameters.
7. The method according to claim 6, wherein the step of calculating the error between the predicted state information of the drone at a future time and the real state information of the drone received at the future time feeds back the calculation result to the calculation function, and the step of modifying the variable parameter includes the following steps:
(1) predicting the state information g of the unmanned aerial vehicle at the current time tnowObtaining the required known parameters according to the numerical value of the time, wherein the known parameters comprise: the last prediction result of g, k pieces of real state information data g (T) in the time period T1),g(t2)…g(tk)；
(2) Defining the influence weight W of the last prediction result on the next prediction resultp；
Defining a binary function f (x), wherein when x>Time 0 f (x)>0, for generating g (t)1),g(t2)…g(tk) An influence coefficient on a prediction result;
defining a correction weight W for a deviation between a predicted result and a true resultl；
(3) Let t0＝tnow-T, calculating f (T)1–t0),f(t2–t0)……f(tk-t0) A value of (d);
(4) establishing an xOy coordinate system, inserting ((t)1-t0),f(t1–t0)*g(t1))，((t2-t0),f(t2–t0)*g(t2))……((tk-t0),f(tk–t0)*g(tk) Values of these k points and a function F point-fitted to the coordinate system is obtained by newton interpolation;
(5) when x is T, the value of a function F is calculated to obtain an initial value g' F (T)/F (T) of a prediction result;
(6) according to the last prediction result glastAdjusting the value of g' to obtain gm＝(g’+glast*Wp)/(1+Wp)；
(7) Determining the adjusted prediction result gmIf the state information deviates from the known state information, the numerical value g of the closest state information in the state information is usedlAdjusting gmTo obtain gm’，gm’＝(gm+gl*Wl)/(1+Wl)；
(8) Executing the steps (3) to (7) on all the generated variable parameter sets, calculating to obtain and store the corresponding prediction results, and selecting the value of the variable parameter set with the best current score as the value of the current prediction result;
(9) and receiving the real state information update of the unmanned aerial vehicle, correcting the prediction result according to the real state information update, re-grading the generated variable parameter set, and selecting the variable parameter set with the best grade.
8. The visual rendering method suitable for the high-frequency state data of the unmanned aerial vehicle according to claim 1, wherein the state data comprises flight attitude, geographical location information, electric quantity, flight speed and GPS health degree.
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