CN112559787A

CN112559787A - Telemetry data processing method and system based on block chain and cloud platform

Info

Publication number: CN112559787A
Application number: CN202011631514.9A
Authority: CN
Inventors: 詹能勇; 刘振宇
Original assignee: Guangzhou Zhiyunshang Big Data Technology Co ltd
Current assignee: Guizhou Shenma survey and Design Co.,Ltd.
Priority date: 2020-12-31
Filing date: 2020-12-31
Publication date: 2021-03-26
Anticipated expiration: 2040-12-31
Also published as: CN112559787B

Abstract

The invention relates to the technical field of block chains, in particular to a telemetry data processing method and system based on a block chain and a cloud platform. Firstly, resolving telemetering object data of a first associated topological network contained in the present telemetering data of the unmanned aerial vehicle; then generating telemetering control point data of a second associated topological network according to the telemetering object data and the association relation of the first associated topological network, wherein the second associated topological network is an associated topological network obtained after the first associated topological network is subjected to key object screening; and finally, determining a target member block network according to the remote-measuring control point data of the second associated topological network, and storing the remote-measuring control point data to the target member block network. The invention can automatically store the data of the remote control point, thereby solving the problem that the storage efficiency is lower because the related storage address needs to be manually searched when the data of the remote control point is stored in the related technology.

Description

Telemetry data processing method and system based on block chain and cloud platform

Technical Field

The invention relates to the technical field of block chains, in particular to a telemetry data processing method and system based on a block chain and a cloud platform.

Background

Along with the development of unmanned aerial vehicle technique, unmanned aerial vehicle has been can be used in multiple scene to supplementary people accomplish tasks such as regional security monitoring, geographic information collection, environmental status data acquisition, thereby reach purposes such as quick collection and promotion collection efficiency.

For example, the prior art may mount an optoelectronic pod on the drone for capturing telemetry data of the target area, such as an area monitoring picture or an area monitoring video of the target area.

For some telemetering control point data in the current region, because the telemetering control point data is more important than other data, for example, a region monitoring network can be constructed based on the telemetering control point data; therefore, in the related art, the telemetry control point data may be subjected to special storage processing, such as storage to a preset address, or storage after encryption.

However, in the process of saving the telemetry control point data to the preset address, an operator is required to manually configure the saving address, so that the saving efficiency is low.

Disclosure of Invention

The invention aims to provide a telemetry data processing method and system based on a block chain and a cloud platform, so as to solve at least part of technical problems.

In order to achieve the purpose, the technical scheme adopted by the invention is as follows:

in a first aspect, the present invention provides a telemetry data processing method based on a block chain, including:

analyzing telemetry object data of a first association topological network contained in current unmanned aerial vehicle telemetry data, wherein each telemetry object in the first association topological network is a telemetry target included in the current unmanned aerial vehicle telemetry data, an association relation between the telemetry objects in the first association topological network is a topological relation between the telemetry targets included in the current unmanned aerial vehicle telemetry data, and the telemetry object data comprises remote sensing image data;

generating telemetering control point data of a second associated topological network according to the telemetering object data of the first associated topological network and the association relation, wherein the second associated topological network is an associated topological network obtained by screening key objects of the telemetering object in the first associated topological network, and the telemetering control point data at least comprises telemetering image data;

and determining a target member block network according to the telemetry control point data of the second associated topological network, and storing the telemetry control point data to the target member block network, wherein the telemetry targets in member device columns in the target member block network correspond to each other one by one, and each member device corresponds to one telemetry target in the sensitive telemetry target queue.

Optionally, as an embodiment, the generating telemetry control point data of a second associated topological network according to the telemetry object data of the first associated topological network and the association relationship includes:

acquiring an importance index corresponding to each telemetering object in the second associated topological network in the first associated topological network;

when the second associated topological network comprises a first target telemetry object sequence, configuring the telemetry control point data of the first target telemetry object sequence to comprise remote sensing image data of each telemetry object in the first target telemetry object sequence, wherein the corresponding importance index of each telemetry object in the first target telemetry object sequence in the first associated topological network is greater than a first set threshold value;

when the second association topological network comprises a second target telemetry object sequence, using the difference between telemetry data between each telemetry object in the second target telemetry object sequence and a corresponding matched association telemetry object in the association relation as a telemetry data update parameter, and configuring telemetry control point data of the second target telemetry object sequence to comprise remote sensing image data and the telemetry data update parameter of each telemetry object in the second target telemetry object sequence, wherein the corresponding importance degree index of each telemetry object in the second target telemetry object sequence in the first association topological network is greater than a second set threshold value.

Optionally, as an embodiment, the obtaining the importance index of each telemetry object in the second associated topology network in the first associated topology network includes:

acquiring a historical statistical information table item, wherein historical statistical record information is recorded in the historical statistical information table item, and the historical statistical record information is used for indicating corresponding record information of a telemetering object in the first associated topological network;

analyzing the historical statistical information table entry, and screening target historical statistical record information corresponding to each telemetering object from the historical statistical information table entry, wherein the target historical statistical record information is obtained by searching a target object serial number, and each target object serial number corresponds to one telemetering object;

extracting the historical statistics times corresponding to the sequence numbers of the target objects from the historical statistics record information of the targets;

and generating a target importance degree index corresponding to each target object serial number based on the historical statistical times corresponding to each target object serial number and a corresponding preset weight parameter so as to obtain the importance degree index corresponding to each telemetering object in the second associated topological network in the first associated topological network.

Optionally, as an implementation manner, the parsing the historical statistical information entry, and screening out the target historical statistical record information corresponding to each telemetry object in the historical statistical information entry includes:

searching a target statistical address corresponding to each target object serial number according to the target object serial number corresponding to each telemetered object, and screening out first historical statistical record information from the historical statistical information table item according to each target statistical address, wherein the target statistical address is used for indicating the address of the historical statistical information recorded in the historical statistical information table item by the corresponding target object serial number;

and screening the tags according to the received timestamps, and screening each piece of first historical statistical record information to obtain target historical statistical record information of each telemetering object which meets the timestamp screening tags.

Optionally, as an implementation manner, the generating a target importance degree index corresponding to each target object serial number based on the historical statistics times corresponding to each target object serial number and a corresponding preset weight parameter includes:

generating data statistical frequency corresponding to each target object serial number according to the historical statistical frequency corresponding to each target object serial number;

determining an importance degree analysis strategy corresponding to the target object serial number according to each data statistics frequency, wherein the data statistics frequencies in different interval ranges respectively correspond to different importance degree analysis strategies, the importance degree analysis strategy is used for indicating that historical statistics times corresponding to the target object serial number are analyzed through multiple dimensions, and the importance degree analysis strategy comprises a statistics frequency analysis strategy, a weight proportion analysis strategy and a sorting sequence analysis strategy;

analyzing the historical statistics times by using the statistical frequency analysis strategy to obtain a first importance degree contribution value corresponding to each target object sequence number;

updating the first important program index corresponding to each target object sequence number by using the weight proportion analysis strategy corresponding to each target object sequence number to obtain a second important degree contribution value corresponding to each target object sequence number;

performing sequencing analysis on the second importance degree contribution values corresponding to all the target object sequence numbers according to the sequencing order analysis strategy to obtain third importance degree contribution values corresponding to all the target object sequence numbers, wherein each third importance degree contribution value is used for indicating the importance degree corresponding to each target object sequence number under the importance degree analysis strategy;

according to the contribution value interval to which each third importance degree contribution value belongs, finding out a first contribution degree grade score corresponding to each target object sequence number from a preset contribution strategy;

processing each first contribution level score by using the statistical frequency analysis strategy to generate a second contribution level score corresponding to each target object sequence number;

processing each second contribution level score according to the weight proportion analysis strategy to generate a third contribution level score corresponding to each target object sequence number;

processing each third contribution level score according to the sorting sequence analysis strategy to generate a fourth contribution level score corresponding to each target object sequence number;

weighting and summing the second contribution degree grade score, the third contribution degree grade score and the fourth contribution degree grade score corresponding to each target object sequence number to generate a contribution degree comprehensive score corresponding to each target object sequence number;

and determining the ratio of the contribution degree comprehensive score corresponding to each target object sequence number to all the contribution degree comprehensive scores as a target importance degree index corresponding to each target object sequence number.

Optionally, as an implementation manner, the determining a target member block network according to the telemetry control point data of the second associated topology network includes:

when the second associated topological network comprises a first target telemetry object sequence, taking each telemetry object in the first telemetry object sequence as a current telemetry object, and respectively performing the following operations on the telemetry control point data of each telemetry object in the first target telemetry object sequence by using a plurality of threads:

searching a shot object serial number matched with remote sensing image data included in the remote sensing control point data of the current remote sensing object in a remote sensing target included in the current remote sensing data of the unmanned aerial vehicle;

and determining corresponding current member equipment by using the shot object serial numbers corresponding to the remote sensing image data included in the remote sensing control point data of the current remote sensing object, wherein the target member block network consists of the current member equipment corresponding to all the shot object serial numbers, the corresponding importance index of each remote sensing object in the first target remote sensing object sequence in the first associated topological network is greater than a first set threshold value, and the remote sensing control point data of each remote sensing object in the first target remote sensing object sequence comprises the remote sensing image data of each remote sensing object in the first target remote sensing object sequence.

when the second associated topology network includes a second sequence of target telemetry objects, with each telemetry object in the second sequence of target telemetry objects as a current telemetry object, performing the following operations on the telemetry control point data of each telemetry object in the second sequence of target telemetry objects:

searching a target telemetering data updating parameter and a shot object serial number matched with target telemetering image data in a telemetering target included in the current telemetering data of the unmanned aerial vehicle, wherein the target telemetering data updating parameter is the telemetering data updating parameter included in the telemetering control point data of the current telemetering object, and the target telemetering image data is the telemetering image data included in the telemetering control point data of the current telemetering object;

and determining corresponding current member equipment by using a shooting sequence number corresponding to the remote sensing image data included in the remote sensing control point data of the current remote sensing object, wherein the target member block network consists of the current member equipment corresponding to all the shooting objects, the corresponding importance degree index of each remote sensing object in the second target remote sensing object sequence in the first association topological network is greater than a second set threshold value, and the remote sensing control point data of each remote sensing object in the second target remote sensing object sequence comprises the remote sensing image data of each remote sensing object in the second target remote sensing object sequence and the remote sensing data updating parameter of each remote sensing object in the association relationship.

Optionally, as an embodiment, the parsing telemetry object data of the first associated topology network included in the current drone telemetry data includes:

the telemetry object data of a third association topological network corresponding to the current unmanned aerial vehicle telemetry data are analyzed, wherein the telemetry target indicated by the telemetry object in the third association topological network comprises all the telemetry targets contained in the current unmanned aerial vehicle telemetry data, and the first association topological network is the third association topological network; or

The telemetry object data of a fourth associated topological network corresponding to the current unmanned aerial vehicle telemetry data is analyzed, wherein the telemetry target indicated by the telemetry object in the fourth associated topological network comprises at least part of telemetry targets contained in the current unmanned aerial vehicle telemetry data, the first associated topological network is the fourth associated topological network, and the part of telemetry targets are the telemetry targets corresponding to the first object identifier in all the telemetry targets contained in the current unmanned aerial vehicle telemetry data; or

And telemetry object data of a fifth associated topological network corresponding to the current unmanned aerial vehicle telemetry data are analyzed, wherein the fifth associated topological network consists of one telemetry object, the telemetry object indicates a telemetry target corresponding to a second object identifier in the current unmanned aerial vehicle telemetry data, and the first associated topological network is the fifth associated topological network.

In a second aspect, the present invention provides a blockchain-based telemetry data processing system, comprising:

the analysis module is used for analyzing telemetry object data of a first association topology network contained in the current unmanned aerial vehicle telemetry data, wherein each telemetry object in the first association topology network is a telemetry target included in the current unmanned aerial vehicle telemetry data, an association relationship among the telemetry objects in the first association topology network is a topological relationship among the telemetry targets included in the current unmanned aerial vehicle telemetry data, and the telemetry object data comprises remote sensing image data;

the processing module is used for generating telemetering control point data of a second associated topological network according to the telemetering object data of the first associated topological network and the association relation, wherein the second associated topological network is an associated topological network obtained by screening key objects of the telemetering object in the first associated topological network, and the telemetering control point data at least comprises telemetering image data;

the processing module is further configured to determine a target member block network according to the telemetry control point data of the second associated topology network, and store the telemetry control point data to the target member block network, where the telemetry targets in the member device rows in the target member block network correspond to one another one by one, and each member device corresponds to one telemetry target in the sensitive telemetry target queue.

In a third aspect, the present invention provides a cloud platform comprising a memory for storing one or more programs; a processor; the one or more programs, when executed by the processor, implement the blockchain-based telemetry data processing method described above.

In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described method for processing telemetry data based on blockchains.

The invention provides a telemetry data processing and system based on a block chain, which comprises the steps of firstly, resolving telemetry object data of a first associated topological network contained in the current unmanned aerial vehicle telemetry data, wherein the telemetry object data comprises remote sensing image data, and each telemetry object in the first associated topological network is a telemetry target contained in the current unmanned aerial vehicle telemetry data; then generating telemetering control point data of a second associated topological network according to the telemetering object data and the association relation of the first associated topological network, wherein the second associated topological network is an associated topological network obtained after the first associated topological network is subjected to key object screening; and finally, determining a target member block network according to the telemetering control point data of the second associated topological network, and storing the telemetering control point data to the target member block network, wherein member devices in the target member block network correspond to telemetering targets in a sensitive telemetering target queue one by one, each member device corresponds to one telemetering target in the sensitive telemetering target queue, and the sensitive telemetering target queue comprises the telemetering targets represented by each telemetering object in the second associated topological network. The method and the device can automatically generate the target member block network, and store the remote-measuring control point data by using the target member block network, thereby solving the problem that in the related technology, when the remote-measuring control point data is stored, the related storage address needs to be manually searched, so that the storage efficiency is low.

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 technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to these drawings without inventive effort.

Fig. 1 is a block diagram of an electronic device according to the present invention.

Fig. 2 is a flowchart of a telemetry data processing method based on a blockchain according to the present invention.

FIG. 3 is a block diagram of a blockchain-based telemetry data processing system provided by the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages 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 in some embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. The components of the present invention, as generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations.

Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on a part of the embodiments of the present invention, belong to the protection 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. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.

It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

Some embodiments of the invention are described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.

Referring to fig. 1, fig. 1 is a block diagram of an electronic device 100 provided by the present invention, where the electronic device 100 includes a memory 101, a processor 102 and a communication interface 103, and the memory 101, the processor 102 and the communication interface 103 are electrically connected to each other directly or indirectly to implement data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines.

The memory 101 may be configured to store software programs and modules, such as program instructions/modules corresponding to the block chain-based telemetry data processing system provided by the present invention, and the processor 102 executes the software programs and modules stored in the memory 101 to execute various functional applications and data processing, thereby executing the steps of the block chain-based telemetry data processing method provided by the present invention. The communication interface 103 may be used for communicating signaling or data with other node devices.

The Memory 101 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Programmable Read-Only Memory (EEPROM), and the like.

The processor 102 may be an integrated circuit chip having signal processing capabilities. The Processor 102 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.

Referring to fig. 2, fig. 2 is a flowchart of a block chain-based telemetry data processing method according to the present invention, the method including the following steps:

s100, telemetry object data of a first associated topological network contained in the current unmanned aerial vehicle telemetry data are analyzed.

In this embodiment, for all the telemetry targets in the target area photographed by the drone, an association topology network may be constructed, where each telemetry target in the association topology network is used to simulate each telemetry target in the target area.

For example, the telemetry object may be used to simulate a telemetry target such as a house building, a water source monitoring point, a farming monitoring point, a road choke monitoring point, etc. within the target area.

For the current telemetry data of the unmanned aerial vehicle, the electronic device may construct a first association topology network based on the association topology network, that is: the first associated topological network is a sub-network of the associated topological network; in addition, each telemetering object in the first association topological network is also a telemetering target included in the current unmanned aerial vehicle telemetering data, the association relationship among the telemetering objects in the first association topological network is the topological relationship among the telemetering targets included in the current unmanned aerial vehicle telemetering data, the telemetering object data includes telemetering image data, such as a telemetering monitoring image of a target area, and the environment pollution condition of the target area can be analyzed by analyzing the telemetering monitoring image.

S200, generating telemetering control point data of a second associated topological network according to the telemetering object data of the first associated topological network and the association relation.

In this embodiment, key object screening may be performed on all telemetry objects in the first associated topology network, so as to screen a second associated topology network from the first associated topology network, where the second associated topology network is an associated topology network obtained by performing key object screening on the telemetry objects in the first associated topology network, that is, the second associated topology network is a sub-network of the first associated topology network; and the remote sensing control point data in the second associated topological network at least comprises remote sensing image data.

S300, determining a target member block network according to the remote-measuring control point data of the second associated topological network, and storing the remote-measuring control point data to the target member block network.

In this embodiment, a target member block network may be determined for telemetry control point data in the second associated topology network, where member devices in the target member block network correspond to telemetry targets in a sensitive telemetry target queue one to one, and each member device corresponds to a telemetry target in the sensitive telemetry target queue; and, the telemetry control data may also be saved to the target member zone network.

In summary, in this embodiment, first, telemetry object data of a first associated topology network included in current unmanned aerial vehicle telemetry data is analyzed, where the telemetry object data includes remote sensing image data, and each telemetry object in the first associated topology network is a telemetry target included in the current unmanned aerial vehicle telemetry data; then generating telemetering control point data of a second associated topological network according to the telemetering object data and the association relation of the first associated topological network, wherein the second associated topological network is an associated topological network obtained after the first associated topological network is subjected to key object screening; and finally, determining a target member block network according to the telemetering control point data of the second associated topological network, and storing the telemetering control point data to the target member block network, wherein member devices in the target member block network correspond to telemetering targets in a sensitive telemetering target queue one by one, each member device corresponds to one telemetering target in the sensitive telemetering target queue, and the sensitive telemetering target queue comprises the telemetering targets represented by each telemetering object in the second associated topological network. The method and the device can automatically generate the target member block network, and store the remote-measuring control point data by using the target member block network, thereby solving the problem that in the related technology, when the remote-measuring control point data is stored, the related storage address needs to be manually searched, so that the storage efficiency is low.

In some embodiments, in order to improve the matching accuracy of the first association topology network, S100 may be performed by adopting the following scheme.

First, telemetry object data of a third associated topological network corresponding to the current drone telemetry data may be parsed, where telemetry targets indicated by the telemetry objects in the third associated topological network include all telemetry targets included in the current drone telemetry data, and the first associated topological network is the third associated topological network.

Alternatively, telemetry object data of a fourth associated topological network corresponding to the current drone telemetry data may be parsed, where the telemetry objects indicated by the telemetry objects in the fourth associated topological network include at least part of the telemetry objects included in the current drone telemetry data, the first associated topological network is the fourth associated topological network, and the part of the telemetry objects is the telemetry objects corresponding to the first object identifier in all the telemetry objects included in the current drone telemetry data.

Or telemetry object data of a fifth associated topological network corresponding to the current unmanned aerial vehicle telemetry data can be analyzed, wherein the fifth associated topological network is composed of one telemetry object, the telemetry object indicates a telemetry target corresponding to a second object identifier in the current unmanned aerial vehicle telemetry data, and the first associated topological network is the fifth associated topological network.

It is understood that the above-mentioned parallel manners of executing S100 are only examples, and in a specific implementation, any one of the manners of executing S100 may be selected through an operation manner configured by a user; of course, in some other embodiments of the present invention, S100 may also be executed in some other manners, which is not limited in the present invention.

Additionally, in some embodiments, to increase xxx for a telemetry control point, S200 may be performed in the following manner.

Firstly, acquiring the corresponding importance degree index of each telemetering object in the second associated topological network in the first associated topological network.

In one aspect, when the second associated topological network includes a first sequence of target telemetry objects, the telemetry control point data of the first sequence of target telemetry objects is configured to include remote sensing image data of each telemetry object in the first sequence of target telemetry objects, wherein the corresponding importance index of each telemetry object in the first sequence of target telemetry objects in the first associated topological network is greater than a first set threshold.

On the other hand, when the second association topological network comprises a second target telemetry object sequence, using the difference of telemetry data between each telemetry object in the second target telemetry object sequence and a corresponding matched association telemetry object in the association relationship as a telemetry data update parameter, and configuring the telemetry control point data of the second target telemetry object sequence to comprise the telemetry image data and the telemetry data update parameter of each telemetry object in the second target telemetry object sequence, wherein the corresponding importance degree index of each telemetry object in the second target telemetry object sequence in the first association topological network is greater than a second set threshold value.

As an embodiment, for each telemetry object in the second associated topology network, the corresponding importance degree index in the first associated topology network may be calculated by:

firstly, obtaining a historical statistical information table entry, wherein historical statistical record information is recorded in the historical statistical information table entry, and the historical statistical record information is used for indicating corresponding record information of a telemetering object in the first associated topological network.

And then analyzing the historical statistical information table entry, and screening target historical statistical record information corresponding to each telemetering object from the historical statistical information table entry, wherein the target historical statistical record information is obtained by searching a target object serial number, and each target object serial number corresponds to one telemetering object.

And then, extracting the history statistics times corresponding to the sequence numbers of the target objects from the target history statistics record information.

And then, generating a target importance degree index corresponding to each target object serial number based on the historical statistical times corresponding to each target object serial number and a corresponding preset weight parameter so as to obtain the importance degree index corresponding to each telemetering object in the second associated topological network in the first associated topological network.

Therefore, by adopting the scheme provided by the embodiment, the corresponding importance degree index of each telemetering object in the second associated topological network in the first associated topological network can be automatically generated by combining with the historical statistical information table entry, and the calculation efficiency is improved.

In addition, in some embodiments, the target statistical record information corresponding to each telemetry object may be obtained by parsing in the following manner:

first, a target statistical address corresponding to each target object serial number may be searched according to the target object serial number corresponding to each telemetry object, and first historical statistical record information may be screened from the historical statistical information entry according to each target statistical address, where the target statistical address is used to indicate an address of the historical statistical information recorded in the historical statistical information entry by the corresponding target object serial number.

And then, screening the tags according to the received timestamps, and screening each piece of first historical statistical record information to obtain target historical statistical record information of each telemetering object, wherein each telemetering object meets the timestamp screening tags.

That is, in this embodiment, a timestamp filtering tag may be received, and the history statistical record information that satisfies the timestamp filtering tag in each piece of first history statistical record information may be used as the target history statistical record information corresponding to each telemetry object, so as to satisfy a user's requirement for processing data in a specific time range.

In addition, the target importance degree index corresponding to each target object sequence number may be obtained by calculating in the following manner:

firstly, according to the historical statistics times corresponding to each target object serial number, generating data statistics frequency corresponding to each target object serial number.

And then, determining importance degree analysis strategies corresponding to the target object serial numbers according to each data statistical frequency, wherein the data statistical frequencies in different interval ranges respectively correspond to different importance degree analysis strategies, the importance degree analysis strategies are used for indicating that historical statistical times corresponding to the target object serial numbers are analyzed through multiple dimensions, and the importance degree analysis strategies comprise statistical frequency analysis strategies, weight proportion analysis strategies and sorting sequence analysis strategies.

And then, analyzing the historical statistics times by using the statistical frequency analysis strategy to obtain a first importance contribution value corresponding to each target object sequence number.

Next, updating the first importance program index corresponding to each target object sequence number by using the weight proportion analysis strategy corresponding to each target object sequence number, so as to obtain a second importance degree contribution value corresponding to each target object sequence number.

Then, performing sequencing analysis on the second importance degree contribution values corresponding to all the target object sequence numbers according to the sequencing order analysis strategy to obtain third importance degree contribution values corresponding to all the target object sequence numbers, wherein each third importance degree contribution value is used for indicating the importance degree corresponding to each target object sequence number under the importance degree analysis strategy;

and then, according to the contribution value interval to which each third importance contribution value belongs, finding out a first contribution degree grade score corresponding to each target object sequence number from a preset contribution strategy.

And then, processing each first contribution degree grade score by using the statistical frequency analysis strategy to generate a second contribution degree grade score corresponding to each target object sequence number.

And then, processing each second contribution level score according to the weight proportion analysis strategy to generate a third contribution level score corresponding to each target object sequence number.

And then, processing each third contribution degree grade score according to the sorting sequence analysis strategy to generate a fourth contribution degree grade score corresponding to each target object sequence number.

And then, carrying out weighted summation on the second contribution degree grade score, the third contribution degree grade score and the fourth contribution degree grade score corresponding to each target object sequence number to generate a contribution degree comprehensive score corresponding to each target object sequence number.

And then, determining the contribution degree comprehensive score corresponding to each target object sequence number as a ratio of all the contribution degree comprehensive scores, and determining the ratio as a target importance degree index corresponding to each target object sequence number.

Therefore, by the scheme provided by the invention, the calculation accuracy of the target importance degree index corresponding to each target object serial number can be improved.

In this embodiment, as an example, for the target member block network determined in the above S300, the target member block network may be determined in a manner that:

when the second associated topological network comprises a first target telemetry object sequence, each telemetry object in the first telemetry object sequence can be used as a current telemetry object, and the following operations are respectively executed on the telemetry control point data of each telemetry object in the first target telemetry object sequence by utilizing a plurality of threads:

firstly, searching a shot object serial number matched with remote sensing image data included in remote sensing control point data of the current remote sensing object in a remote sensing target included in the current remote sensing data of the unmanned aerial vehicle.

Then, the corresponding current member device is determined by utilizing the shot object serial number corresponding to the remote sensing image data included in the remote sensing control point data of the current remote sensing object, wherein the target member block network is composed of the current member devices corresponding to all the shot object serial numbers, the corresponding importance degree index of each remote sensing object in the first target remote sensing object sequence in the first associated topological network is larger than a first set threshold value, and the remote sensing control point data of each remote sensing object in the first target remote sensing object sequence comprises the remote sensing image data of each remote sensing object in the first target remote sensing object sequence.

Optionally, as another possible implementation manner, for the target member block network determined in S300, the target member block network may be determined in the following manner:

when the second associated topological network comprises a second target telemetry object sequence, taking each telemetry object in the second target telemetry object sequence as a current telemetry object, and executing the following operations on the telemetry control point data of each telemetry object in the second target telemetry object sequence:

firstly, searching a target telemetering data updating parameter and a shot object serial number matched with target telemetering image data in a telemetering target included in the current telemetering data of the unmanned aerial vehicle, wherein the target telemetering data updating parameter is the telemetering data updating parameter included in the telemetering control point data of the current telemetering object, and the target telemetering image data is the telemetering image data included in the telemetering control point data of the current telemetering object.

Then, the shooting sequence number corresponding to the remote sensing image data included in the remote sensing control point data of the current remote sensing object is utilized to determine the corresponding current member device, wherein the target member block network is composed of the current member devices corresponding to all the shooting objects, the corresponding importance degree index of each remote sensing object in the second target remote sensing object sequence in the first association topological network is larger than a second set threshold value, and the remote sensing control point data of each remote sensing object in the second target remote sensing object sequence comprises the remote sensing image data of each remote sensing object in the second target remote sensing object sequence and the remote sensing data updating parameter of each remote sensing object in the association relationship.

Referring to fig. 3, the present invention also provides a telemetry data processing system 300 based on a block chain, wherein the telemetry data processing system 300 based on a block chain includes a parsing module 310 and a processing module 320.

Optionally, as an implementation manner, when the processing module generates telemetry control point data of a second associated topology network according to the telemetry object data of the first associated topology network and the association relationship, the processing module is specifically configured to:

Optionally, as an implementation manner, when obtaining the importance index corresponding to each telemetry object in the second associated topological network in the first associated topological network, the processing module is specifically configured to:

Optionally, as an implementation manner, when analyzing the historical statistical information entry and screening out the target historical statistical record information corresponding to each telemetry object in the historical statistical information entry, the processing module is specifically configured to:

Optionally, as an implementation manner, when the processing module generates a target importance degree index corresponding to each target object serial number based on the historical statistics times corresponding to each target object serial number and a corresponding preset weight parameter, the processing module is specifically configured to:

Optionally, as an implementation manner, when determining the target member block network according to the telemetry control point data of the second associated topology network, the processing module is specifically configured to:

Optionally, as an implementation manner, when parsing telemetry object data of a first associated topology network included in current drone telemetry data, the parsing module is specifically configured to:

In addition, based on the same inventive concept as the above-mentioned web crawler-based data processing method provided by the present invention, the present invention also provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the above-mentioned block chain-based telemetry data processing method.

In addition, based on the same inventive concept as the above data processing method based on web crawler provided by the present invention, the present invention also provides a cloud platform on which a computer program is stored, and the computer program, when executed by a processor, implements the above telemetry data processing method based on blockchain.

In the embodiments provided by the present invention, it should be understood that the disclosed system and method can be implemented in other ways. The system embodiments described above are merely illustrative, and for example, 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 some 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.

In addition, the functional modules in some embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the method according to some embodiments of the present invention. And the aforementioned storage medium includes: u disk, removable hard disk, read only memory, random access memory, magnetic or optical disk, etc. for storing program codes.

The above description is only a partial example of the present invention, and is not intended to limit the present invention, and it is obvious to those skilled in the art that various modifications and variations can be made in the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.

Claims

1. A telemetry data processing method based on a block chain is characterized by comprising the following steps:

2. The method of claim 1, wherein generating telemetry control point data for a second associated topology network based on the telemetry object data for the first associated topology network and the association relationship comprises:

3. The method of claim 2, wherein obtaining the corresponding importance index of each telemetry object in the second linked topology network in the first linked topology network comprises:

4. The method of claim 3, wherein parsing the historical statistics entry to screen out the historical statistics entry for the respective target corresponding to each telemetry object comprises:

5. The method according to claim 3 or 4, wherein the generating a target importance degree index corresponding to each target object serial number based on the historical statistics times corresponding to each target object serial number and a corresponding preset weight parameter comprises:

6. The method of claim 1, wherein determining a target member block network from the telemetry control point data of the second associated topology network comprises:

7. The method of claim 1, wherein determining a target member block network from the telemetry control point data of the second associated topology network comprises:

8. The method of claim 1, wherein resolving telemetry object data of a first associated topology network included in current drone telemetry data comprises:

9. A blockchain-based telemetry data processing system, comprising:

10. A cloud platform, comprising:

a memory for storing one or more programs;

a processor;

the one or more programs, when executed by the processor, implement the blockchain-based telemetry data processing method of any of claims 1-8.