CN115278525B - Method and system for simplifying cluster moving object continuous space-time positioning data - Google Patents
Method and system for simplifying cluster moving object continuous space-time positioning data Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract
The invention relates to the technical field of data communication transmission, in particular to a method and a system for simplifying continuous space-time positioning data of a cluster moving target, which comprises three steps of data acquisition at an acquisition end, data simplification at a transmission end, and data reduction at a receiving end, wherein firstly, a data acquisition module is used for selecting positioning data and acquiring other target data, the positioning data of the target data is subtracted from the positioning data, the positioning data is converted into the distance and radian between the target data and the positioning data, and the data is effectively simplified; and finally, after receiving the data at the receiving end, carrying out data reduction by utilizing the relation between the positioning data and the target position, acquiring, simplifying, transmitting and reducing the data under the condition of limited communication traffic and not influencing the data precision.
Description
Technical Field
The invention relates to the technical field of data communication transmission, in particular to a method and a system for simplifying continuous space-time positioning data of a cluster moving target.
Background
The continuous development in a plurality of fields such as communication, computer, big data, artificial intelligence, aerospace, transportation, wisdom agriculture can all involve a large amount of data communication and transmission, and data acquisition speed is progressively promoted at present, and after a large amount of data acquisition are accomplished, still need data transmission and data reception.
However, the data transmission amount is huge, and the communication and transmission of data are limited by factors such as time, simultaneously communicated data amount, storage space and the like, so that under limited communication amount, most of the current algorithms can exchange the data transmission rate for the data accuracy, and the problem of slow data transmission occurs.
The invention provides a data simplification method according to the characteristics of data (long data length), distribution conditions (the number of clusters, the density degree and the spatial position relationship) and change characteristics (position change generated along with the time), and improves the data transmission efficiency under the condition of not influencing the data precision.
Disclosure of Invention
The invention aims to: aiming at the low efficiency of the current data transmission, the method for simplifying the continuous space-time positioning data of the cluster moving target is provided, the dynamic multi-target positioning data can be obtained under the limited communication traffic, and the data is collected, simplified, transmitted and restored.
In order to achieve the above purpose, the invention provides the following technical scheme:
a method for simplifying continuous space-time positioning data of cluster moving objects comprises the following steps:
s1, data acquisition and data simplification: selecting positioning data and acquiring other target data, and subtracting the positioning data from a plurality of target data to obtain subtracted data;
s2, data transmission and storage: transmitting and storing the obtained positioning data and the subtracted data;
s3, data reduction: and after receiving the positioning data and the subtracted data, adding the subtracted data and the positioning data, and restoring the subtracted data into the target data.
Further, the type of data reduction includes data reduction based on a spatial location relationship, and the data reduction based on the spatial location relationship specifically includes the following steps:
a1, selecting a central position P through a data acquisition module 0 ；
A11, transmitting and storing the central position P through a data transmission module 0 ；
A2, a plurality of target data P n And a central position P 0 By subtraction of a plurality of said target data P n And a central position P 0 The subtraction expression is p n =P n -P 0 ；
A21, transmitting and storing p through the data transmission module n ；
A3, after receiving the data of the step A11 and the step A21, the data restoring module utilizes the central position P 0 And the object location data P n The relation of (2) for data reduction, the center position P 0 And object location data P n Is expressed as P n =p n +P 0 。
Further, after the step A2, a discriminant is introduced: and if N is equal to N, the number of all the target data is determined, N is started from the first target data, if N is not equal to N, N is increased, the step A2 is returned to continue the circulation, if N is equal to N, all the target data difference values are calculated, and the data acquisition process is ended.
Further, the data reduction type includes data reduction based on a continuous time relationship, and the data reduction based on the continuous time relationship specifically includes the following steps:
b1, acquiring a first time T of a target object through a data acquisition module 0 Location data D T0 ；
B11, transmitting through the data transmission module and storing D T0 ；
B21, acquiring a second time T of the target object 1 Positioning data D T1 Calculating the difference d between the positioning data at the first time and the positioning data at the second time T1 =D T1 -D T0 ；
B211, transmitting and storing the difference data d through the data transmission module T1 ；
B22, acquiring data D of the third time and later Tn Obtaining the difference d between the positioning data of each time and the last time Tn =D Tn -D T(n-1) Then, the second difference d between each difference and the previous difference is obtained n =d Tn -d T(n-1) ；
B221, transmitting and storing the secondary difference data d through the data transmission module n ；
B3, the data recovery module receives the complete data D at the first moment T0 Difference data D of the second time with respect to the first time T1 =d T1 +D T0 Second order difference data d of the third time and later with respect to the previous time Tn =d T(n-1) +d n Using the difference relation D Tn =D T(n-1) +d Tn And restoring the original value at each moment.
Further, after the step B22, a discriminant is introduced: and if N is equal to N, the N is all the time, the N is increased from the first time, if the N is not equal to the N, the step B21 is returned to the continuous circulation step, if the N is equal to the N, the data difference value calculation at all the time is completed, and the data acquisition process is ended.
Further, the data reduction type includes data reduction based on a continuous spatiotemporal relationship, where k is a sub-region and j is a time interval, and specifically includes the following steps:
c1, selecting the central position P of each area through a data acquisition module k0 Obtaining the target data quantity N in each region k ；
C11, transmitting and storing the data P through the data transmission module k0 ；
C21 at T 1 Obtaining the positioning data D of each target object in each area at any moment 1nk To obtain a difference d 1nk =D 1nk -P k0 ；
C211, transmitting and storing data d through the data transmission module 1nk ；
C22, from T 2 Starting from the moment, the positioning data D of each target object in each area is acquired jnk To obtain a primary difference d jnk =D jnk -P k0 The first difference is the distance between the target data and the central position, and a second difference d is obtained nk =d jnk -d (j-1)nk The secondary difference value is the displacement relation between the current moment and the next moment of the target data;
c221, transmitting and storing data d through the data transmission module nk ；
C3, after the data recovery module receives the data of the step C11, the step C211 and the step C221, obtaining d by using a difference relation nk =d jnk -d (j-1)nk ，D jnk =d jnk +P k0 And original data are restored.
Further, after the step C1, a discriminant is introduced: whether j is equal to 1 or not,
if j is equal to 1, it indicates a first time T 1 Entering the next discriminant: whether N is greater than N k If N is not greater than N k If N is greater than N, the step C21 is proceeded k Indicating that the target data calculation of the area is completed;
entering the next discriminant: whether N is greater than N kmax ，
If N is not greater than N kmax And indicating that the target data calculation of other areas is not completed, immediately entering the next discriminant:if K is equal to K, K is all the sub-regions, if K is not equal to K, entering the next sub-region, and returning to the decision formula: if j is equal to 1, if K is equal to K, entering next target data, and returning to the decision formula: whether j is equal to 1;
e.g. N is greater than N kmax And when the calculation of target data of all the areas is finished, entering the next moment, and returning to the judgment formula: whether j is equal to 1;
if j is not equal to 1, it is expressed as a first time T 1 At a later time, the subsequent flow divides the discriminant N to be greater than N kmax Otherwise, consistent with the flow where j is equal to 1, when N is greater than N kmax Entering the next discriminant: whether J is larger than or equal to all the J moments, if J is larger than or equal to all the J moments, the fact that the target data calculation of the moments is not completed is shown, and the discriminant formula is returned: whether N is greater than N k ；
And if J is not larger than all the J moments, the calculation of the target data at all the moments is completed, and the data acquisition process is ended.
The utility model provides a simplification system of continuous space-time location data of cluster motion target, includes terminal equipment, LAN network deployment communication module, WAN network deployment communication module, internet module and satellite, terminal equipment collects and takes notes livestock activity data, the satellite is to terminal equipment carries out the position location, terminal equipment constitutes LAN network deployment communication module with unmanned aerial vehicle, unmanned aerial vehicle constitutes WAN network deployment communication module again with the satellite, the satellite transmits data to internet module, cell-phone APP accessible internet module looks over terminal equipment positional information, terminal equipment carries out data acquisition and retrench according to arbitrary a simplification method of cluster motion target continuous space-time location data, LAN network deployment communication module, WAN network deployment communication module and satellite carry out data transmission according to arbitrary a simplification method of cluster motion target continuous space-time location data, internet module carries out data reduction according to arbitrary a simplification method of cluster motion target continuous space-time location data, a cluster motion target continuous space-time retrench data's simplification system uses in the LAN network deployment communication module of LoRa-short message mode group and supports unmanned aerial vehicle communication.
Further, terminal equipment includes neck ring positioning device and ear tag positioning device, and the satellite includes GPS satellite and big dipper satellite, terminal equipment supports simultaneously the bimodulus location of GPS satellite and big dipper satellite, unmanned aerial vehicle carries on the short message mobile base station of machine-carried big dipper, through machine-carried big dipper short message mobile base station and terminal equipment LAN communication, through machine-carried big dipper short message mobile base station and big dipper satellite wide area network communication, the internet module includes big dipper short message ground base station, mobile communication basic station, cloud accumulator and cell-phone APP, big dipper short message ground base station communicates with the big dipper satellite, the rethread mobile communication basic station with communication information store extremely the cloud accumulator, cell-phone APP passes through communication information is looked over to the cloud accumulator.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the scheme of the application, the method for simplifying the continuous space-time positioning data of the cluster moving target comprises three steps of data acquisition at an acquisition end, data simplification at a transmission end and data reduction at a receiving end, firstly, the data acquisition module is used for selecting the positioning data and acquiring other target data, the positioning data of the target data is subtracted from the positioning data, the positioning data is converted into the distance and radian between the target data and the positioning data, and the data is effectively simplified; then, subtracting the central position from the plurality of target positioning data, transmitting and storing the subtracted target positioning data, and finally, after receiving the data at a receiving end, performing data reduction by using the relation between the positioning data and the target position, so that dynamic multi-target positioning data can be obtained under limited communication traffic, and the data is acquired, simplified, transmitted and reduced under the condition of not influencing data precision;
2. in the data compaction type based on the spatial position relationship, the position data is directly substituted into the positioning data and other target data, then a discriminant is added, as long as all the position data are not compacted, the next position data is automatically compacted in the process, and finally the data acquisition is ended after all the position data are compacted;
3. in the data compaction type based on the continuous time relationship, position data is replaced by positioning data of a certain moment, the difference value between the positioning data of a first moment and the positioning data of a second moment is calculated, data of a third moment and later moments are acquired, and so on, the difference value between the positioning data of the moment and the positioning data of the last moment is calculated, the secondary difference value between the difference value and the difference value of the last moment is calculated, and so on; the receiving end calculates the original value of each time by using the received complete data of the first time, the difference data of the second time relative to the first time and the secondary difference data of the third time and later relative to the previous time;
4. in the data reduction type based on the continuous space-time relationship, the data reduction type based on the space position relationship and the data reduction type based on the continuous time relationship are fused, the frequency of the reaction of a time interval j has influence on the reduction effect, and the smaller the time interval is, the smaller the difference value of the subtraction of the positioning position data is; dividing k subregions, dividing the regions according to the position of the target object, and selecting the central position according to the regions; the primary difference value is the relation between the target object and the central position, the data size reflects the distance between the target object and the central position, the secondary difference value is the displacement relation between the previous moment and the next moment of the target object, and the data volume is influenced by time interval j and central position division; applicability of the data compaction type based on the continuous spatiotemporal relationship is as follows: according to the characteristics of data (long data length), distribution conditions (the number of clusters, the density degree and the spatial position relation) and change characteristics (position change generated along with time), the values of the sub-regions k and the time intervals j are comprehensively adjusted to obtain the optimal simplification effect, and the method has universal applicability.
Description of the drawings:
FIG. 1 is a schematic flow chart of a data reduction method based on spatial position relationship;
FIG. 2 is a schematic flow chart of a data reduction method based on a continuous time relationship;
FIG. 3 is a schematic flow chart of a data reduction method based on continuous spatiotemporal relationship;
FIG. 4 is a schematic diagram of a system for streamlining moving object data in a cluster;
FIG. 5 is a simplified system of cluster moving object data applied to a mountain pasture in a plateau Tibetan region.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments.
Thus, the following detailed description of the embodiments of the invention is not intended to limit the scope of the invention as claimed, but is merely representative of some embodiments of the 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.
It should be noted that the embodiments of the present invention and the features and technical solutions thereof may be combined with each other without conflict.
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.
In the description of the present invention, it should be noted that the terms "upper", "lower", and the like refer to orientations or positional relationships based on those shown in the drawings, or orientations or positional relationships that are conventionally arranged when the products of the present invention are used, or orientations or positional relationships that are conventionally understood by those skilled in the art, and such terms are used for convenience of description and simplification of the description, and do not refer to or imply that the devices or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like are used merely to distinguish one description from another, and are not to be construed as indicating or implying relative importance.
The first embodiment is as follows: the method for simplifying the continuous space-time positioning data of the cluster moving target provided by the embodiment comprises the following steps:
s1, data acquisition and data simplification: selecting positioning data and acquiring other target data, and subtracting the positioning data from a plurality of target data to obtain subtracted data;
s2, data transmission and storage: transmitting and storing the obtained positioning data and the subtracted data;
s3, data reduction: and after receiving the positioning data and the subtracted data, adding the subtracted data and the positioning data, and restoring the subtracted data into the target data.
The method for simplifying the continuous space-time positioning data of the cluster moving target comprises three steps of data acquisition at an acquisition end, data simplification at a transmission end, data reduction at a transmission end and data reduction at a receiving end, firstly, the data acquisition module is used for selecting positioning data and acquiring other target data, the positioning data of the target data is subtracted from the positioning data, the positioning data is converted into the distance and radian between the target data and the positioning data, and the data is effectively simplified; and finally, after receiving the data at the receiving end, carrying out data reduction by utilizing the relation between the positioning data and the target position, acquiring, simplifying, transmitting and reducing the data under the condition of limited communication traffic and not influencing the data precision.
Furthermore, the data reduction types comprise data reduction based on a space position relationship, data reduction based on a continuous time relationship and data reduction based on a continuous space-time relationship, the data reduction is divided into three types, and a further method based on the data reduction method provided by the embodiment I is adopted to collect, reduce, transmit and restore data of each type of data reduction type;
example two: as shown with reference to figure 1 of the drawings,
in this embodiment, on the basis of the first embodiment, a method for reducing continuous spatiotemporal positioning data of a cluster moving object is further provided, and further, the data reduction type based on the spatial position relationship specifically includes the following steps:
a1, selecting a central position P through the data acquisition module 0 Transmitting and storing positioning data P 0 ；
A2, a plurality of target data P are transmitted through the data transmission module n And a central position P 0 A plurality of target data P are transmitted and stored after subtraction n And a central position P 0 The expression of subtraction is p n =P n -P 0 ；
A3, after the data recovery module receives the data of the step A1 and the step A2 at a receiving end, the central position P is utilized 0 With said object location data P n The center position P is used for data reduction 0 And object location data P n Is expressed as P n =p n +P 0 ；
Further, after the step A2, a discriminant is introduced: and if N is equal to N, the number of all the target data is determined, N is started from the first target data, if N is not equal to N, N is increased, the step A2 is returned to continue the circulation, if N is equal to N, all the target data difference values are calculated, and the data acquisition process is ended.
In the data compaction type based on the spatial position relationship, the position data is directly substituted into the positioning data and other target data, then a discriminant is added, as long as all the position data are not compacted, the next position data is automatically compacted by the process, and finally, the data acquisition is ended after all the position data are compacted.
Example three: as shown with reference to figure 2 of the drawings,
in this embodiment, on the basis of the first embodiment, a method for reducing continuous spatiotemporal positioning data of a cluster moving object is further provided, and further, the data reduction type based on a continuous time relationship specifically includes the following steps:
b1, acquiring a first time T of a target object through the data acquisition module 0 Positioning data D T0 Carry out transmission and storage of D T0 ；
B21, acquiring a second time T of the target object through the data transmission module 1 Positioning data D T1 Calculating the difference d between the positioning data at the first time and the positioning data at the second time T1 =D T1 -D T0 Transmitting and storing the difference data d T1 ；
B22, acquiring data D of the third time and later Tn Obtaining the difference d between the positioning data of each time and the last time Tn =D Tn -D T(n-1) Then, the second difference d between each difference and the previous difference is obtained n =d Tn -d T(n-1) Transmitting and storing the secondary difference data d n ；
B3, the data recovery module receives complete data D of the first moment at a receiving end T0 Difference data D of the second time with respect to the first time T1 =d T1 +D T0 Second order difference data d of the third time and later with respect to the previous time Tn =d T(n-1) +d n Using the difference relation D Tn =D T(n-1) +d Tn Reducing the original value of each moment;
further, after the step B22, a discriminant is introduced: and if N is equal to N, the N is all the time, the N is increased from the first time, if the N is not equal to the N, the step B21 is returned to the continuous circulation step, if the N is equal to the N, the data difference value calculation at all the time is completed, and the data acquisition process is ended.
In a data compaction type based on a continuous time relation, replacing position data with positioning data at a certain moment, calculating a difference value between the positioning data at the first moment and the positioning data at the second moment, acquiring data at the third moment and later moments, and so on, calculating a difference value between the positioning data at the moment and the positioning data at the previous moment, calculating a secondary difference value between the difference value and the difference value at the previous moment, and so on; and the receiving end calculates the original value of each moment by using the difference relation by using the received complete data of the first moment, the difference data of the second moment relative to the first moment and the secondary difference data of the third moment and later moments relative to the previous moment.
Example four: as shown with reference to figure 3 of the drawings,
on the basis of the first embodiment, the embodiment further provides a method for reducing continuous spatio-temporal positioning data of a cluster moving object by fusing a data reduction type based on a spatial position relationship and a data reduction type based on a continuous time relationship, where k is each sub-region and j is a time interval, and the method specifically includes the following steps:
c1, selecting the central position P of each area through the data acquisition module k0 Obtaining the target data quantity N in each region k Transmitting and storing data P k0 ；
C21, transmitting the data at T through the data transmission module 1 Obtaining the positioning data D of each target object in each area at any moment 1nk To obtain a difference d 1nk =D 1nk -P k0 Transmitting and storing data d 1nk ；
C22, from T 2 Acquiring the positioning data D of each target object in each area from the beginning of time jnk To obtain a difference d jnk =D jnk -P k0 And the second order difference d nk =d jnk -d (j-1)nk Transmitting and storing data d nk ;
C3, after the data of the step C1, the step C21 and the step C22 are received by the data recovery module at the receiving end, the difference relation d is utilized nk =d jnk -d (j-1)nk ，D jnk =d jnk +P k0 And original data are restored.
Further, after the step C1, a discriminant is introduced: whether j is equal to 1 or not,
if j is equal to 1, it indicates a first time T 1 Entering the next discriminant: whether N is greater than N k If N is not more than N k Indicating that the target data of the region is not calculatedThen, go to step C21, if N is greater than N k Indicating that the target data calculation of the area is completed;
entering the next discriminant: whether N is greater than N kmax ，
If N is not more than N kmax And indicating that the target data calculation of other areas is not completed, immediately entering the next discriminant: if K is equal to K, K is all the sub-regions, if K is not equal to K, entering the next sub-region, and returning to the decision formula: if j is equal to 1, if K is equal to K, entering next target data, and returning to the decision formula: whether j is equal to 1;
if N is greater than N kmax And when the calculation of target data of all the areas is finished, entering the next moment, and returning to the judgment formula: whether j is equal to 1;
if j is not equal to 1, it is expressed as a first time T 1 At a later time, the subsequent flow divides the discriminant N to be greater than N kmax Otherwise, consistent with the flow where j is equal to 1, when N is greater than N kmax Entering the next discriminant: whether J is larger than or equal to all the J moments, if J is larger than or equal to all the J moments, the calculation of target data at the moment is not completed, and the discriminant is returned: whether N is greater than N k；
And if J is not more than all the J moments, the calculation of the target data at all the moments is completed, and the data acquisition process is ended.
Example five: as shown in reference to figures 4 and 5,
the utility model provides a simplification system of continuous space-time location data of cluster moving object, including terminal equipment, LAN network deployment communication module, WAN network deployment communication module, internet module and satellite, terminal equipment collects and takes notes livestock activity data, the satellite is to terminal equipment carries out position location, terminal equipment constitutes LAN network deployment communication module with unmanned aerial vehicle, unmanned aerial vehicle constitutes WAN network deployment communication module with the satellite again, the satellite transmits data to internet module, cell-phone APP accessible internet module looks over terminal equipment positional information, terminal equipment carries out data acquisition and retrencies according to arbitrary a method of simplifying of the continuous space-time location data of cluster moving object, LAN network deployment communication module, WAN network deployment communication module and satellite carry out data transmission according to arbitrary a method of simplifying of the continuous space-time location data of cluster moving object, internet module carries out data reduction according to arbitrary a method of simplifying of the continuous space-time location data of cluster moving object, a system of simplifying of cluster moving object continuous space-time uses in the big dipper mode of the LAN message of LoRa-big animal network deployment communication, unmanned aerial vehicle supports the LAN communication.
Further, terminal equipment includes neck ring positioning device and ear tag positioning device, and the satellite includes GPS satellite and big dipper satellite, terminal equipment supports simultaneously the bimodulus location of GPS satellite and big dipper satellite, unmanned aerial vehicle carries on the short message mobile base station of airborne big dipper, through the short message mobile base station of airborne big dipper and terminal equipment LAN communication, through the short message mobile base station of airborne big dipper and the communication of big dipper satellite wide area network, the internet module includes the short message ground of big dipper basic station, mobile communication basic station, cloud accumulator and cell-phone APP, the short message ground of big dipper basic station communicates with the big dipper satellite, the rethread mobile communication basic station with communication information store extremely the cloud accumulator, cell-phone APP passes through communication information is looked over to the cloud accumulator.
In the data reduction type based on the continuous space-time relationship, the data reduction type based on the space position relationship and the data reduction type based on the continuous time relationship are fused, the frequency of the reaction of a time interval j has influence on the reduction effect, and the smaller the time interval is, the smaller the difference value of the subtraction of the positioning position data is; dividing k subregions, namely dividing regions according to the position of the target object and selecting a central position according to the regions; the primary difference is the relation between a target object and a central position, the data size reflects the distance between the target object and the central position, the secondary difference is the displacement relation between the last moment and the next moment of the target object, and the data volume is influenced by time interval j and central position division; applicability of the data compaction type based on the continuous spatiotemporal relationship is as follows: according to the characteristics of data (long data length), distribution conditions (the number of clusters, the density degree and the spatial position relationship) and change characteristics (position change generated along with the time lapse), the values of the sub-region k and the time interval j are comprehensively adjusted to obtain the optimal simplification effect, and the method has universal applicability.
The method is particularly suitable for being applied to the fields related to satellite/GPS positioning and navigation, and has the outstanding advantages of greatly simplifying positioning coordinate data, reducing redundant data transmission, improving transmission efficiency and accurately restoring data.
The method can also be widely applied to simplification, transmission and reduction of other position information, positioning data and multi-dimensional information data, and comprises the related applications of inertial navigation, airline planning, cluster flight data processing and transmission in the aerospace field, the related applications of data processing and transmission in the automation fields of robots, automation plants, intelligent manufacturing and the like, the related applications of data processing and transmission in the traffic and transportation fields of logistics, automatic driving, fleet management and the like, and the related applications of data processing and transmission in the fields of intelligent agriculture, frontier defense, marine defense, wild animal protection and ecological monitoring.
The above embodiments are only used for illustrating the invention and not for limiting the technical solutions described in the invention, and although the present invention has been described in detail in the present specification with reference to the above embodiments, the present invention is not limited to the above embodiments, and therefore, any modification or equivalent replacement of the present invention is made; but all technical solutions and modifications thereof without departing from the spirit and scope of the present invention are encompassed in the claims of the present invention.
Claims (7)
1. A method for simplifying continuous space-time positioning data of cluster moving objects is characterized by comprising the following steps: the method comprises the following steps:
s1, data acquisition and data simplification: selecting positioning data and acquiring other target data, and subtracting the positioning data from a plurality of target data to obtain subtracted data;
s2, data transmission and storage: transmitting and storing the obtained positioning data and the subtracted data;
s3, data reduction: after receiving the positioning data and the subtracted data, adding the subtracted data to the positioning data, and restoring the subtracted data to the target data,
the data reduction type includes data reduction based on a spatial position relationship, and the data reduction based on the spatial position relationship specifically includes the following steps:
a1, selecting a central position P through a data acquisition module 0 ；
A11, transmitting and storing the central position P through a data transmission module 0 ；
A2, a plurality of target data P n And a central position P 0 By subtraction, a plurality of said target data P n And a central position P 0 The subtraction expression is p n =P n -P 0 ；
A21, transmitting and storing p through the data transmission module n ；
A3, after receiving the data of the step A11 and the step A21, the data restoring module utilizes the central position P 0 And the object location data P n The center position P is used for data reduction 0 And object location data P n Is expressed as P n =p n +P 0 ，
Introducing a discriminant after the step A2: and if N is equal to N, the number of all the target data is determined, N is started from the first target data, if N is not equal to N, N is increased, the step A2 is returned to continue the circulation, if N is equal to N, all the target data difference values are calculated, and the data acquisition process is ended.
2. A method of reducing the continuous spatiotemporal positioning data of clustered moving objects as defined in claim 1, wherein: the data reduction type includes data reduction based on a continuous time relationship, and the data reduction based on the continuous time relationship specifically includes the following steps:
b1, obtaining through a data acquisition moduleTarget object first time T 0 Positioning data D T0 ；
B11, transmitting through the data transmission module and storing D T0 ；
B21, acquiring a second time T of the target object 1 Positioning data D T1 Calculating the difference d between the positioning data at the first time and the positioning data at the second time T1 =D T1 -D T0 ；
B211, transmitting and storing the difference data d through the data transmission module T1 ；
B22, acquiring data D of the third time and later Tn Obtaining the difference d between the positioning data of each time and the last time Tn =D Tn -D T(n-1) Then, the second difference d between each difference and the previous difference is obtained n =d Tn -d T(n-1) ；
B221, transmitting and storing the secondary difference data d through the data transmission module n ；
B3, the data recovery module receives the complete data D at the first moment T0 Difference data D of the second time with respect to the first time T1 =d T1 +D T0 Second order difference data d of the third time and later with respect to the previous time Tn =d T(n-1) +d n Using the difference relation D Tn =D T(n-1) +d Tn And restoring the original value at each moment.
3. The method of claim 2, wherein the method comprises the following steps: introducing a discriminant after the step B22: and if N is equal to N, the N is all the time, the N is increased from the first time, if the N is not equal to the N, the step B21 is returned to the continuous circulation step, if the N is equal to the N, the data difference value calculation at all the time is completed, and the data acquisition process is ended.
4. A method of reducing the continuous spatiotemporal positioning data of clustered moving objects as defined in claim 1, wherein: the data reduction type comprises data reduction based on a continuous space-time relationship, wherein k is a sub-region and j is a time interval in the data reduction based on the continuous space-time relationship, and the method specifically comprises the following steps:
c1, selecting the central position P of each area through a data acquisition module k0 Obtaining the target data quantity N in each region k ；
C11, transmitting and storing the data P through the data transmission module k0 ；
C21 at T 1 Obtaining the positioning data D of each target object in each area at any moment 1nk To obtain a difference d 1nk =D 1nk -P k0 ；
C211, transmitting and storing data d through the data transmission module 1nk ；
C22, from T 2 Acquiring the positioning data D of each target object in each area from the beginning of time jnk To obtain a primary difference d jnk =D jnk -P k0 The first difference is the distance between the target data and the central position, and a second difference d is obtained nk =d jnk -d (j-1)nk The secondary difference is the displacement relation between the current moment and the next moment of the target data;
c221, transmitting and storing data d through the data transmission module nk ；
C3, after receiving the data of the step C11, the step C211 and the step C221, the data reduction module obtains d by using a difference relation nk =d jnk -d (j-1)nk ，D jnk =d jnk +P k0 And original data are restored.
5. The method of reducing continuous spatiotemporal positioning data of clustered moving objects as defined in claim 4, wherein: introducing a discriminant after the step C1: whether j is equal to 1 or not,
if j is equal to 1, it indicates a first time T 1 Entering the next discriminant: whether N is greater than N k If N is not greater than N k If N is greater than N, the step C21 is proceeded k Indicating that the target data of the area is calculatedForming;
entering the next discriminant: whether N is greater than N kmax ，
If N is not more than N kmax And indicating that the target data calculation of other areas is not completed, immediately entering the next discriminant: if K is equal to K, K is all the sub-regions, if K is not equal to K, entering the next sub-region, and returning to the decision formula: if j is equal to 1, if K is equal to K, entering next target data, and returning to the decision formula: whether j is equal to 1;
e.g. N is greater than N kmax And when the calculation of target data of all the areas is finished, entering the next moment, and returning to the judgment formula: whether j is equal to 1;
if j is not equal to 1, it is expressed as a first time T 1 At a later time, the subsequent flow divides the discriminant N to be greater than N kmax Otherwise, consistent with the flow where j is equal to 1, when N is greater than N kmax Entering the next discriminant: whether J is larger than or equal to all the J moments, if J is larger than or equal to all the J moments, the calculation of target data at the moment is not completed, and the discriminant is returned: whether N is greater than N k ；
And if J is not more than all the J moments, the calculation of the target data at all the moments is completed, and the data acquisition process is ended.
6. A cluster moving object continuous space-time positioning data simplification system is characterized in that: the livestock activity data are collected and recorded by the terminal device, the satellite positions the terminal device, the terminal device and the unmanned aerial vehicle form a local area network communication module, the unmanned aerial vehicle and the satellite form a wide area network communication module, the satellite transmits the data to the internet module, a mobile phone APP can view the position information of the terminal device through the internet module, the terminal device collects and reduces the data according to the method of any one of claims 1 to 5, the local area network communication module, the wide area network communication module and the satellite transmit the data according to the method of any one of claims 1 to 5, the internet module performs data reduction on the data according to the method of any one of claims 1 to 5, and the system for reducing the cluster moving target continuous space-time positioning data is applied to local area network communication base station equipment which is built in a LoRa-Beidou short message mode and supports wide area livestock-unmanned aerial vehicle local area network communication and wide area network communication.
7. A system for streamlining the continuous spatiotemporal positioning data of clustered moving objects as recited in claim 6 wherein: terminal equipment includes neck ring positioning device and ear tag positioning device, and the satellite includes GPS satellite and big dipper satellite, terminal equipment supports simultaneously the bimodulus location of GPS satellite and big dipper satellite, unmanned aerial vehicle carries on the short message mobile base station of big dipper, through the short message mobile base station of big dipper and terminal equipment LAN communication of machine carries on the short message mobile base station of big dipper and big dipper satellite wide area network communication, the internet module includes big dipper short message ground base station, mobile communication basic station, cloud accumulator and cell-phone APP, big dipper short message ground base station communicates with big dipper satellite, the rethread mobile communication basic station with communication information storage extremely the cloud accumulator, cell-phone APP passes through communication information is looked over to the cloud accumulator.
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