CN112511972A - Transformer substation inspection robot positioning method and device based on 5G - Google Patents
Transformer substation inspection robot positioning method and device based on 5G Download PDFInfo
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Abstract
The invention provides a transformer substation inspection robot positioning method and device based on 5G, relating to the technical field of transformer substation inspection, and comprising the following steps: s1: acquiring substation position information, dividing the substation position information into M grids based on the substation position information, acquiring the central position of each grid, and acquiring an AP signal of each grid; s2: grouping the AP signals of the M grids, and performing Kalman filtering on the grouped AP signals to acquire a fingerprint database; s3: acquiring a real-time RSS fingerprint and matching the real-time RSS fingerprint with an RSS fingerprint of a position in a fingerprint database to acquire an initial position; s4: acquiring the step length position difference between human body inertia k-1 and k-2, and predicting the position at the k moment based on the estimated position to acquire a predicted position; s5: and acquiring a range set, acquiring a reference fingerprint based on the fingerprint database and the predicted position, and acquiring the position of the inspection robot based on the reference fingerprint. The method and the device provided by the invention can reduce the labor cost and improve the intelligent degree of the substation equipment.
Description
Technical Field
The invention relates to the technical field of robot inspection, in particular to a transformer substation inspection robot positioning method and device based on 5G.
Background
The power grid construction project is the most important of energy Internet, and is related to life-line industry of national civilization, and the power grid is responsible for perfecting the power grid energy network framework, but along with the continuous expansion of the power grid scale, the equipment scale growth speed obviously exceeds the number of configured maintainers, the maintenance work is obviously increased, and simultaneously, the service life of equipment design is reached along with the operation years of a plurality of equipment, the equipment performance and the insulating capability tend to age, large-area replacement and maintenance are needed, and various maintenance works in the future are very difficult. The method is characterized in that a new generation of intelligent substation inspection robot is used, a 5G communication technology and an indoor positioning technology are combined, multi-dimensional correlation analysis and display of equipment data are achieved by an intelligent means, scheduling operation data and operation and inspection basic data are analyzed and fused, and the panoramic state of the equipment is observed in a mode of a time axis of the equipment. The working personnel can master each dynamic state of the equipment, the development of each daily work is assisted, the labor cost is reduced, the economic benefit is improved, and the method has important significance.
With the continuous development of semiconductor technology, wireless communication technology and computer technology, good technical support is provided for the rapid progress of indoor wireless positioning technology, a large number of mainstream indoor positioning technologies are continuously emerged at present, the 5G positioning technology uses a database to compare with an established fingerprint map for positioning by means of a 2.6GHz indoor sub-base station and an indoor propagation model, the result is not influenced by shadow fading and multipath interference, the existing wireless sensor network can be utilized, and the system cost and the positioning precision are guaranteed.
Disclosure of Invention
In view of this, the invention aims to provide a transformer substation inspection robot positioning method based on 5G, which enables a worker to master each dynamic state of equipment, assists development of each daily work, reduces labor cost, improves economic benefits, and improves the intelligent degree of transformer substation equipment.
In a first aspect, an embodiment of the present invention provides a transformer substation inspection robot positioning method based on 5G, including:
s1: acquiring substation position information, dividing the substation position information into M grids based on the substation position information, acquiring the central position of each grid, and acquiring an AP signal of each grid;
s2: grouping the AP signals of the M grids, and performing Kalman filtering on the grouped AP signals to acquire a fingerprint database;
s3: acquiring a real-time RSS fingerprint and matching the real-time RSS fingerprint with an RSS fingerprint of a position in the fingerprint database to acquire an initial position;
s4: acquiring the step length position difference between human body inertia k-1 and k-2, and predicting the position at the moment k based on the estimated position to acquire a predicted position;
s5: and acquiring a range set, acquiring a reference fingerprint based on the fingerprint database and the predicted position, and acquiring the position of the inspection robot based on the reference fingerprint.
Preferably, the step of S2: the step of grouping the AP signals of the M grids and performing kalman filtering on the grouped AP signals to obtain a fingerprint database includes:
the state value of the previous moment is calculated by adopting the following formula
And external system input uk-1Predicting the current time state:
A. b are parameter matrixes;
the uncertainty of the predicted state is obtained by the following formula
Based on uncertainty
And the uncertainty R adopts the following formula to obtain the Kalman gain Kk：
H-state variable to observation transition matrix
The weighted average value and the observation value are used to measure the current state by the following formula
Make an estimation
zk-current moment filtering input observed value
The robot position information is subjected to covariance processing by adopting the following formula:
r'i-the filtered ith position signal;
riis the ith filtered front position signal;
n is the sampling number of AP signals;
rm,1-the signal strength received by the mth reference node to the 1 st AP node;
t-number of signal samples with minimum variance.
Preferably, the step S3: acquiring a real-time RSS fingerprint and matching the real-time RSS fingerprint with the RSS fingerprint of the position in the fingerprint database to acquire an initial position, and acquiring a matching distance by adopting the following formula:
Dj-a matching distance;
x'i-real time RSS of the ith AP node;
xi-an element in the fingerprint library that matches the live fingerprint;
k RPs with smaller distances are obtained through calculation of the matching distances, and the positions of the k RPs are averaged to obtain a positioning result, which is shown in the following formula.
x and y-acquiring the horizontal and vertical coordinates of the initial position;
xi、yi-fingerprint vertical and horizontal coordinates in the fingerprint library matching the live fingerprint;
preferably, the step of S4: the step of obtaining the step length position difference between the human body inertia k-1 and k-2 and predicting the position at the k moment based on the estimated position to obtain the predicted position comprises the following steps:
the predicted position is obtained using the following formula:
△xk、△yk-inertial relative displacement.
Preferably, the step of S5: acquiring a range set, acquiring a reference fingerprint based on the fingerprint database and the predicted position, acquiring the position of the inspection robot based on the reference fingerprint,
wherein, (x, y) -reference node coordinates in the fingerprint database —
d is a matching radius;
Rk-RSS fingerprint of the on-line location to be measured;
Rk,nthe RSS fingerprint corresponding to the nth reference fingerprint at the moment k;
the position of the inspection robot is obtained by adopting the following formula:
ωk-joint matching distance dkThe weight of (c);
on the other hand, the invention provides a transformer substation inspection robot positioning device based on 5G, which comprises:
a signal acquisition module: the system comprises a central processing unit, a central processing unit and a central processing unit, wherein the central processing unit is used for acquiring substation position information, dividing the substation position information into M grids based on the substation position information, acquiring the central position of each grid and acquiring an AP signal of each grid;
a fingerprint data acquisition module: the system comprises a processor, a fingerprint database and a processor, wherein the processor is used for grouping the AP signals of the M grids and performing Kalman filtering on the grouped AP signals to acquire the fingerprint database;
an initial position acquisition module: the system comprises a fingerprint database, a real-time RSS fingerprint acquisition module and a real-time RSS fingerprint matching module, wherein the real-time RSS fingerprint is matched with the RSS fingerprint of a position in the fingerprint database to acquire an initial position;
a predicted position acquisition module: the device is used for acquiring the step length position difference between human body inertia k-1 and k-2 and predicting the position at the moment k based on the estimated position to acquire a predicted position;
a position determination module: the system is used for acquiring a range set, acquiring a reference fingerprint based on the fingerprint database and the predicted position, and acquiring the position of the inspection robot based on the reference fingerprint.
The embodiment of the invention has the following beneficial effects: the invention provides a transformer substation inspection robot positioning method and device based on 5G, relating to the technical field of transformer substation inspection, and comprising the following steps: s1: acquiring substation position information, dividing the substation position information into M grids based on the substation position information, acquiring the central position of each grid, and acquiring an AP signal of each grid; s2: grouping the AP signals of the M grids, and performing Kalman filtering on the grouped AP signals to acquire a fingerprint database; s3: acquiring a real-time RSS fingerprint and matching the real-time RSS fingerprint with an RSS fingerprint of a position in a fingerprint database to acquire an initial position; s4: acquiring the step length position difference between human body inertia k-1 and k-2, and predicting the position at the k moment based on the estimated position to acquire a predicted position; s5: and acquiring a range set, acquiring a reference fingerprint based on the fingerprint database and the predicted position, and acquiring the position of the inspection robot based on the reference fingerprint. By the method and the device, workers can master each dynamic state of the equipment, development of daily work is assisted, labor cost is reduced, economic benefits are improved, and meanwhile the intelligent degree of the substation equipment is improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a grid division diagram of a transformer substation inspection robot positioning method fingerprint positioning system based on 5G provided by the embodiment of the invention;
fig. 2 is a layout diagram of a fingerprint positioning system AP of a transformer substation inspection robot positioning method based on 5G according to an embodiment of the present invention;
fig. 3 is a signal fluctuation diagram before and after off-line fingerprint sampling filtering of a positioning method of a transformer substation inspection robot based on 5G provided by the embodiment of the invention;
fig. 4 is a fingerprint signal hot spot diagram of an AP1 position in a transformer substation inspection robot positioning method based on 5G according to an embodiment of the present invention;
fig. 5 is a fingerprint signal hot spot diagram of an AP2 position in a transformer substation inspection robot positioning method based on 5G according to an embodiment of the present invention;
fig. 6 is a flowchart of a transformer substation inspection robot positioning method based on 5G according to an embodiment of the present invention;
fig. 7 shows the influence of the number N of reference points matched in the multi-fingerprint joint matching of the transformer substation inspection robot positioning method based on 5G on the positioning error;
fig. 8 shows the influence of the matching range D on the positioning error in the multi-fingerprint joint matching of the transformer substation inspection robot positioning method based on 5G provided by the embodiment of the present invention;
fig. 9 is a multi-fingerprint combined matching graph of a path of a positioning method for a transformer substation inspection robot based on 5G according to an embodiment of the present invention;
fig. 10 is a combined matching graph of two multi-fingerprint paths of a positioning method for a transformer substation inspection robot based on 5G provided by the embodiment of the invention;
fig. 11 is a three-system error accumulation distribution diagram of a transformer substation inspection robot positioning method based on 5G provided by the embodiment of the invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
At present, a power grid construction project is the most important of energy Internet, is related to the life-line industry of the national civilization, and the power grid is obligated to improve the power grid energy network framework, but with the continuous expansion of the power grid scale, the equipment scale growth speed obviously exceeds the number of configured maintainers, the maintenance work obviously increases, meanwhile, along with the fact that the service life of the equipment is designed due to the fact that the operation life of a plurality of pieces of equipment reaches the design service life, the performance and the insulating capability of the equipment tend to be aged, large-area replacement and maintenance are needed, and various maintenance works in the future are extremely difficult, on the basis, the method and the device for positioning the transformer substation inspection robot based on the 5G are provided by the embodiment of the invention, can let the staff master every developments of equipment, supplementary development of daily each item work alleviates the human cost, improves economic benefits, improves the intelligent degree of substation equipment simultaneously.
In order to facilitate understanding of the embodiment, a detailed description is first given of a method for positioning a transformer substation inspection robot based on 5G disclosed in the embodiment of the present invention.
The first embodiment is as follows:
as shown in fig. 6, an embodiment of the present invention provides a transformer substation inspection robot positioning method based on 5G, including:
s1: acquiring substation position information, dividing the substation position information into M grids based on the substation position information, acquiring the central position of each grid, and acquiring an AP signal of each grid;
further, the m-th grid is denoted as Bm, the center position of each grid is called Reference Point (RP), and the position mark pm is denoted as [ xm, ym ]
S2: grouping the AP signals of the M grids, and performing Kalman filtering on the grouped AP signals to acquire a fingerprint database;
s3: acquiring a real-time RSS fingerprint and matching the real-time RSS fingerprint with an RSS fingerprint of a position in the fingerprint database to acquire an initial position;
specifically, a deterministic algorithm KNN is used to estimate who position, and the real-time RSS fingerprint is matched with the RSS fingerprint at the known position in the fingerprint database,
s4: acquiring the step length position difference between human body inertia k-1 and k-2, and predicting the position at the moment k based on the estimated position to acquire a predicted position;
s5: and acquiring a range set, acquiring a reference fingerprint based on the fingerprint database and the predicted position, and acquiring the position of the inspection robot based on the reference fingerprint.
Preferably, the step of S2: the step of grouping the AP signals of the M grids and performing kalman filtering on the grouped AP signals to obtain a fingerprint database includes:
the state value of the previous moment is calculated by adopting the following formula
And external system input uk-1Predicting the current time state:
A. b are parameter matrixes;
the uncertainty of the predicted state is obtained by the following formula
Based on uncertainty
And the uncertainty R adopts the following formula to obtain the Kalman gain Kk：
H-transition matrix of state variables to observations;
the weighted average value and the observation value are used to measure the current state by the following formula
Make an estimation
zk-filtering the input observations at the current time;
the robot position information is subjected to covariance processing by adopting the following formula:
r'i-the filtered ith position signal;
riis the ith filtered front position signal;
n is the sampling number of AP signals;
rm,1-the signal strength received by the mth reference node to the 1 st AP node;
t-number of signal samples with minimum variance.
Preferably, the step S3: acquiring a real-time RSS fingerprint and matching the real-time RSS fingerprint with the RSS fingerprint of the position in the fingerprint database to acquire an initial position, and acquiring a matching distance by adopting the following formula:
Dj-matching distance
Further, a smaller matching distance indicates a higher degree of matching, and when p is 1, DjFor manhattan distance, when p is 2, DjIs a European tableThe reed distance is often measured as the euclidean distance. (ii) a
x'i-real time RSS of the ith AP node;
xi-elements in a fingerprint library matching live fingerprints
K RPs with smaller distances are obtained through calculation of the matching distances, and the positions of the k RPs are averaged to obtain a positioning result, which is shown in the following formula.
x and y-acquiring the horizontal and vertical coordinates of the initial position;
xi、yi-fingerprint vertical and horizontal coordinates in the fingerprint library matching the live fingerprint;
preferably, the step of S4: the step of obtaining the step length position difference between the human body inertia k-1 and k-2 and predicting the position at the k moment based on the estimated position to obtain the predicted position comprises the following steps:
the predicted position is obtained using the following formula:
△xk、△yk-inertial relative displacement.
Preferably, the step of S5: acquiring a range set, acquiring a reference fingerprint based on the fingerprint database and the predicted position, acquiring the position of the inspection robot based on the reference fingerprint,
wherein, (x, y) -reference node coordinates in the fingerprint database —
d is a matching radius;
Rk-RSS fingerprint of the on-line location to be measured;
Rk,nthe RSS fingerprint corresponding to the nth reference fingerprint at the moment k;
the position of the inspection robot is obtained by adopting the following formula:
ωk-joint matching distance dkThe weight of (c);
example two:
the second embodiment of the invention provides a transformer substation inspection robot positioning device based on 5G, which comprises:
a signal acquisition module: the system comprises a central processing unit, a central processing unit and a central processing unit, wherein the central processing unit is used for acquiring substation position information, dividing the substation position information into M grids based on the substation position information, acquiring the central position of each grid and acquiring an AP signal of each grid;
a fingerprint data acquisition module: the system comprises a processor, a fingerprint database and a processor, wherein the processor is used for grouping the AP signals of the M grids and performing Kalman filtering on the grouped AP signals to acquire the fingerprint database;
an initial position acquisition module: the system comprises a fingerprint database, a real-time RSS fingerprint acquisition module and a real-time RSS fingerprint matching module, wherein the real-time RSS fingerprint is matched with the RSS fingerprint of a position in the fingerprint database to acquire an initial position;
a predicted position acquisition module: the device is used for acquiring the step length position difference between human body inertia k-1 and k-2 and predicting the position at the moment k based on the estimated position to acquire a predicted position;
a position determination module: the system is used for acquiring a range set, acquiring a reference fingerprint based on the fingerprint database and the predicted position, and acquiring the position of the inspection robot based on the reference fingerprint.
Example three:
in the embodiment, a certain unit lobby is used as a system environment, the size is 7m by 7m, and the height of the lobby is about 3 m. The system environment is divided into 49 unit grids by taking 1m by 1m as a unit, a terminal side positioning method is adopted in the example, a 5G omnidirectional ceiling Antenna (AP) deployment environment is used, the working frequency of a room-division antenna is generally 2.6GHz, the AP deployment position is arranged in a square shape, and therefore more node RSS signals within the optimal positioning distance are received by a person handheld terminal, and multipath fading is reduced, as shown in figure 2.
In the acquisition process, personnel are required to hold a mobile phone for data acquisition, and move in a hall along the same direction every time (the influence of body shielding factors is weakened), and each reference point position is sampled for many times, so that more accurate position information is obtained. Each grid is sampled by using 100 samples as training data, the sampling frequency is 1 time/s, and the weighted average value of the samples is used as the signal intensity of the sampling point of the grid at the time. The source data is shown in table 1:
TABLE 1 AP1 partial reference points RSS measurement data values
| X | Y | RSS(dBm) |
| 1 | 1 | -57.57 |
| 1 | 2 | -61.55 |
| 1 | 3 | -64.56 |
| 1 | 4 | -66.87 |
| 1 | 5 | -68.71 |
| 1 | 6 | -70.25 |
| 1 | 7 | -71.55 |
| 1 | 8 | -72.69 |
The restriction condition D is set to 0.7m, and is restricted to the range of the set of the phase differences R.
A fingerprint database hotspot graph, wherein partial nodes are as shown in fig. 4 and 5, a three-dimensional perspective graph is drawn according to RSS fingerprints and coordinates of APs, that is, RSS position fingerprint signal graphs of AP1 and AP2, wherein X and Y axes respectively correspond to reference coordinates of the nodes, Z axis corresponds to a signal strength RSS value of the node under the coordinates, the right side is a signal strength gradient graph of the AP node, the gradient graph decreases from yellow to blue, the threshold is-55 dBm to-85 dBm, and corresponds to grid colors in the graph, and the initial position information can refer to fig. 9 and 10 because each path position information is different;
the algorithm mainly comprises two parameters, namely a matching range D and a matching reference point number N. Firstly, analyzing the number of matching points N, setting D as the whole fingerprint map, as shown in fig. 7, when N is very small, the error is very large, as N becomes large, the error gradually decreases, and when N is greater than 7, the positioning error tends to be stable, so that the value of N should not be too large, which may affect the calculation amount, and loses the significance of the algorithm, so the system takes N as 8. After the value of N is determined, a matching range D is analyzed, it can be known from FIG. 8 that when the matching range is the whole fingerprint map, the positioning error is affected by the signal intensity of other reference points and cannot be reduced to below 1m, and the system requirements cannot be met, as shown in FIG. 8, through experimental observation, the positioning error becomes smaller gradually when the value of D becomes larger, but when the value reaches 0.6m, the system positioning error becomes larger along with the increase of D, actually, when the matching range is very small, the system completely degrades into a KNN algorithm and is not affected by range fingerprint matching, and when the matching range is too large, the influence of the KNN algorithm on the system positioning becomes too small, so that the system takes D0.7 m as an experimental simulation parameter, two main parameters are determined, and the positioning effect of the system can be greatly improved.
In a multi-fingerprint combined matching positioning method (namely, a range fingerprint matching method RFM), a set parameter D is 0.7m, and N is 8, so that positioning simulation is performed on the online random position equipment to be tested. From the positioning results of the two paths of fig. 9 and 10, the average positioning accuracy is 0.735m and 0.756m, respectively, most of the path estimation coordinates are fitted with the real coordinates very well, and especially the algorithm matching position is almost fitted with the real position at the place with small curvature.
FIG. 11 is a distribution diagram of the error accumulation of the three algorithms, and the average positioning accuracy of the range fingerprint matching RFM system is 0.78 m. As can be seen from the figure, the 90% positioning accuracy of the system is within 1.367m, the 70% positioning accuracy is within 0.983m, and the overall positioning effect is good. Through comparison of the error accumulation distribution maps of the three algorithms, the difference between the RFM and the PF is small, the positioning effect is poor relative to a KF Kalman filtering method, the RFM is reduced by 2.6% compared with the PF, but the RFM is improved by 53.3% compared with the single KNN fingerprint positioning method with the average positioning precision of 1.67m, and is improved by 42.2% compared with the Kalman filtering method with the average positioning precision of 1.35m, and the positioning precision is relatively high.
Unless specifically stated otherwise, the relative steps, numerical expressions, and values of the components and steps set forth in these embodiments do not limit the scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (6)
1. The utility model provides a transformer substation inspection robot positioning method based on 5G which characterized in that includes:
s1: acquiring substation position information, dividing the substation position information into M grids based on the substation position information, acquiring the central position of each grid, and acquiring an AP signal of each grid;
s2: grouping the AP signals of the M grids, and performing Kalman filtering on the grouped AP signals to acquire a fingerprint database;
s3: acquiring a real-time RSS fingerprint and matching the real-time RSS fingerprint with an RSS fingerprint of a position in the fingerprint database to acquire an initial position;
s4: acquiring the step length position difference between human body inertia k-1 and k-2, and predicting the position at the moment k based on the estimated position to acquire a predicted position;
s5: and acquiring a range set, acquiring a reference fingerprint based on the fingerprint database and the predicted position, and acquiring the position of the inspection robot based on the reference fingerprint.
2. The method according to claim 1, wherein the step of S2: the step of grouping the AP signals of the M grids and performing kalman filtering on the grouped AP signals to obtain a fingerprint database includes:
the state value of the previous moment is calculated by adopting the following formula
And external system input uk-1Predicting the current time state:
A. b are parameter matrixes;
the uncertainty of the predicted state is obtained by the following formula
Based on uncertainty
And the uncertainty R adopts the following formula to obtain the Kalman gain Kk：
H-transition matrix of state variables to observations;
the weighted average value and the observation value are used to measure the current state by the following formula
Make an estimation
zk-filtering the input observations at the current time;
the robot position information is subjected to covariance processing by adopting the following formula:
ri' -a filtered ith position signal;
riis the ith filtered front position signal;
n is the sampling number of AP signals;
rm,1-the signal strength received by the mth reference node to the 1 st AP node;
t-number of signal samples with minimum variance.
3. The method according to claim 1, wherein the step S3: acquiring a real-time RSS fingerprint and matching the real-time RSS fingerprint with the RSS fingerprint of the position in the fingerprint database to acquire an initial position, and acquiring a matching distance by adopting the following formula:
Dj-a matching distance;
x′i-real time RSS of the ith AP node;
xi-elements in a fingerprint library matching live fingerprints
K RPs with smaller distances are obtained through calculation of matching distances, and the positions of the k RPs are averaged to obtain a positioning result, which is shown in the following formula;
x and y-acquiring the horizontal and vertical coordinates of the initial position;
xi、yi-fingerprint vertical and horizontal coordinates in the fingerprint library matching the live fingerprint.
4. The method according to claim 1, wherein the step of S4: the step of obtaining the step length position difference between the human body inertia k-1 and k-2 and predicting the position at the k moment based on the estimated position to obtain the predicted position comprises the following steps:
the predicted position is obtained using the following formula:
△xk、△yk-inertial relative displacement.
5. The method according to claim 1, wherein the step of S5: acquiring a range set, acquiring a reference fingerprint based on the fingerprint database and the predicted position, acquiring the position of the inspection robot based on the reference fingerprint,
wherein, (x, y) -reference node coordinates in the fingerprint database —
d is a matching radius;
Rk-RSS fingerprint of the on-line location to be measured;
Rk,nthe RSS fingerprint corresponding to the nth reference fingerprint at the moment k;
the position of the inspection robot is obtained by adopting the following formula:
ωk-joint matching distance dkThe weight of (c);
6. the utility model provides a transformer substation patrols and examines robot positioner based on 5G which characterized in that includes:
a signal acquisition module: the system comprises a central processing unit, a central processing unit and a central processing unit, wherein the central processing unit is used for acquiring substation position information, dividing the substation position information into M grids based on the substation position information, acquiring the central position of each grid and acquiring an AP signal of each grid;
a fingerprint data acquisition module: the system comprises a processor, a fingerprint database and a processor, wherein the processor is used for grouping the AP signals of the M grids and performing Kalman filtering on the grouped AP signals to acquire the fingerprint database;
an initial position acquisition module: the system comprises a fingerprint database, a real-time RSS fingerprint acquisition module and a real-time RSS fingerprint matching module, wherein the real-time RSS fingerprint is matched with the RSS fingerprint of a position in the fingerprint database to acquire an initial position;
a predicted position acquisition module: the device is used for acquiring the step length position difference between human body inertia k-1 and k-2 and predicting the position at the moment k based on the estimated position to acquire a predicted position;
a position determination module: the system is used for acquiring a range set, acquiring a reference fingerprint based on the fingerprint database and the predicted position, and acquiring the position of the inspection robot based on the reference fingerprint.
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