CN117499887A - Data acquisition method and system based on multi-sensor fusion technology - Google Patents
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Abstract
The invention provides a data acquisition method and a system based on a multi-sensor fusion technology, wherein the method comprises the following steps: when a target monitoring area is acquired, detecting a plurality of monitoring points contained in the target monitoring area in real time, and constructing wireless communication connection between each monitoring point to generate a corresponding monitoring network; detecting a central point of a target monitoring area in real time, and dividing the target monitoring area into a plurality of corresponding monitoring subareas according to a preset rule based on the central point; matching a plurality of target monitoring points contained in each monitoring subarea in a monitoring network, and carrying out primary fusion processing on monitoring data acquired by the plurality of target monitoring points to generate a corresponding data family; transmitting each data group to a preset cloud server, and carrying out secondary fusion processing on each data group in the preset cloud server to generate a corresponding target data set, wherein the target data set has uniqueness. The invention can avoid data processing errors and improve user experience.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a data acquisition method and system based on a multi-sensor fusion technology.
Background
With the development of the age and the progress of technology, various sensors, such as a temperature sensor, a pressure sensor, a gas sensor and the like, have been developed, and have been widely applied in various fields, so that the work and the life of people are correspondingly facilitated.
In the field of meteorological monitoring data, in order to cover a larger meteorological monitoring range, corresponding monitoring points are respectively arranged in different areas, and corresponding temperature sensors, air pressure sensors, humidity sensors and the like are arranged on each monitoring point so as to acquire corresponding monitoring data in real time.
Further, in order to process monitoring data generated by different monitoring points in the prior art, most of the monitoring data collected by the monitoring points are collected into the same weather database, and required data are called out from the weather database for analysis and processing, however, the data processing mode is easy to cause phenomena of omission and redundancy of the data, so that certain errors are brought to follow-up monitoring conclusion, and certain hidden danger exists.
Disclosure of Invention
Based on the above, the invention aims to provide a data acquisition method and a system based on a multi-sensor fusion technology, so as to solve the problem that the data processing mode in the prior art is easy to have the phenomena of data omission and redundancy, and the obtained conclusion may have errors.
The first aspect of the embodiment of the invention provides:
a data acquisition method based on a multi-sensor fusion technique, wherein the method comprises:
when a target monitoring area is acquired, detecting a plurality of monitoring points contained in the target monitoring area in real time, and constructing wireless communication connection between each monitoring point to generate a corresponding monitoring network;
detecting a central point of the target monitoring area in real time, and dividing the target monitoring area into a plurality of corresponding monitoring subareas according to a preset rule based on the central point;
matching a plurality of target monitoring points contained in each monitoring subarea in the monitoring network, and carrying out primary fusion processing on monitoring data acquired by the plurality of target monitoring points to generate a corresponding data family;
transmitting each data group to a preset cloud server, and performing secondary fusion processing on each data group in the preset cloud server to generate a corresponding target data set, wherein the target data set has uniqueness.
The beneficial effects of the invention are as follows: all monitoring points contained in the current target monitoring area are detected in real time, and meanwhile, in order to facilitate the collection of subsequent data, a corresponding monitoring network needs to be constructed. Furthermore, the center of the current target monitoring area is used as a standard, a plurality of monitoring subareas are divided, and based on the monitoring subareas, the monitoring data collected by a plurality of target monitoring points in each monitoring subarea can be subjected to primary fusion processing in real time, and corresponding data families are fused. On the basis, finally, a plurality of acquired data families are subjected to secondary fusion processing in a cloud server which can be used by all staff, a required target data set can be finally generated, and the target data set already contains the generated data content in the current target monitoring area, so that the phenomena of data omission and redundancy cannot occur in the subsequent data processing process, and the use experience of a user is correspondingly improved.
Further, the step of dividing the target monitoring area into a plurality of corresponding monitoring subareas based on the central point according to a preset rule includes:
when the central point is acquired, detecting the area of the target monitoring area in real time, and calculating the target quantity corresponding to all the monitoring points in the target monitoring area;
Calculating corresponding resolution factors in real time according to the area of the area and the target number, wherein the resolution factors are specific numerical values, and the size of the resolution factors is between 0 and 1;
dividing the target monitoring area into a plurality of corresponding monitoring subareas according to the central point and the splitting factors, wherein the area of each monitoring subarea is different.
Further, the step of dividing the target monitoring area into a plurality of corresponding monitoring subareas according to the center point and the splitting factor includes:
when the splitting factor is acquired, detecting the minimum length of the target monitoring area in real time, and calculating a corresponding dividing radius according to the minimum length and the splitting factor;
generating a corresponding target circular area according to the center point and the dividing radius, and detecting the target difference between the target circular area and the target monitoring area in real time;
and setting the target circular area as a first monitoring subarea, and setting the area corresponding to the target distinction as a second monitoring subarea.
Further, the step of performing a fusion process on the monitoring data collected by the plurality of target monitoring points to generate a corresponding data group includes:
Detecting target time generated when each target monitoring point collects the monitoring data at preset time intervals, sequencing the monitoring data collected by each target monitoring point once according to the sequence of each target time, and generating a corresponding first sequence table;
detecting the data quantity corresponding to the monitoring data acquired by each target monitoring point in real time, and secondarily sequencing the ranks in the first sequence table according to the data quantity of each monitoring data, and generating a corresponding second sequence table;
and carrying out fusion processing on each monitoring data for one time according to the ranking in the second sequence table so as to correspondingly generate the data family, wherein the first sequence table and the second sequence table are unique.
Further, the step of performing a fusion process on each of the monitoring data according to the ranking in the second sequence table to generate the data family correspondingly includes:
sequentially adding corresponding target identifiers to each piece of monitoring data according to the ranking in the second sequence table, and detecting the same data and different data generated between each piece of monitoring data in real time;
Deleting the same data in real time, and reserving the different data to generate a plurality of corresponding target monitoring data;
and sequentially carrying out fusion processing on a plurality of target monitoring data based on the target identifiers so as to correspondingly generate the data family, wherein the target identifiers have uniqueness.
Further, the step of sequentially fusing the plurality of target monitoring data based on the target identifier to correspondingly generate the data family includes:
when a plurality of target monitoring data are acquired, detecting a plurality of attribute values contained in the plurality of target monitoring data, and matching target sub-data matched with each attribute value in the plurality of target monitoring data;
and fusing target sub-data corresponding to each attribute value into a plurality of corresponding sub-data sets, and fusing the plurality of sub-data sets into the data family, wherein each attribute value has uniqueness.
Further, the method further comprises:
when the target data set is fused through the data family, extracting target numbers and target letters respectively contained in the target data set, and carrying out random arrangement processing on the target numbers and the target letters so as to generate a plurality of corresponding sequence codes;
And screening the plurality of sequence codes to generate a plurality of corresponding target sequence codes, randomly selecting one target sequence code, and carrying out dynamic encryption on the target data set.
A second aspect of an embodiment of the present invention proposes:
a data acquisition system based on a multi-sensor fusion technique, wherein the system comprises:
the detection module is used for detecting a plurality of monitoring points contained in the target monitoring area in real time when the target monitoring area is acquired, and constructing wireless communication connection between each monitoring point so as to generate a corresponding monitoring network;
the dividing module is used for detecting the center point of the target monitoring area in real time and dividing the target monitoring area into a plurality of corresponding monitoring subareas according to a preset rule based on the center point;
the first fusion module is used for matching a plurality of target monitoring points contained in each monitoring sub-area in the monitoring network, and carrying out primary fusion processing on monitoring data acquired by the plurality of target monitoring points so as to generate a corresponding data family;
and the second fusion module is used for transmitting each data group to a preset cloud server, and carrying out secondary fusion processing on each data group in the preset cloud server so as to generate a corresponding target data set, wherein the target data set has uniqueness.
Further, the step of dividing the target monitoring area into a plurality of corresponding monitoring subareas based on the central point according to a preset rule includes:
when the central point is acquired, detecting the area of the target monitoring area in real time, and calculating the target quantity corresponding to all the monitoring points in the target monitoring area;
calculating corresponding resolution factors in real time according to the area of the area and the target number, wherein the resolution factors are specific numerical values, and the size of the resolution factors is between 0 and 1;
dividing the target monitoring area into a plurality of corresponding monitoring subareas according to the central point and the splitting factors, wherein the area of each monitoring subarea is different.
Further, the step of dividing the target monitoring area into a plurality of corresponding monitoring subareas according to the center point and the splitting factor includes:
when the splitting factor is acquired, detecting the minimum length of the target monitoring area in real time, and calculating a corresponding dividing radius according to the minimum length and the splitting factor;
generating a corresponding target circular area according to the center point and the dividing radius, and detecting the target difference between the target circular area and the target monitoring area in real time;
And setting the target circular area as a first monitoring subarea, and setting the area corresponding to the target distinction as a second monitoring subarea.
Further, the step of performing a fusion process on the monitoring data collected by the plurality of target monitoring points to generate a corresponding data group includes:
detecting target time generated when each target monitoring point collects the monitoring data at preset time intervals, sequencing the monitoring data collected by each target monitoring point once according to the sequence of each target time, and generating a corresponding first sequence table;
detecting the data quantity corresponding to the monitoring data acquired by each target monitoring point in real time, and secondarily sequencing the ranks in the first sequence table according to the data quantity of each monitoring data, and generating a corresponding second sequence table;
and carrying out fusion processing on each monitoring data for one time according to the ranking in the second sequence table so as to correspondingly generate the data family, wherein the first sequence table and the second sequence table are unique.
Further, the step of performing a fusion process on each of the monitoring data according to the ranking in the second sequence table to generate the data family correspondingly includes:
Sequentially adding corresponding target identifiers to each piece of monitoring data according to the ranking in the second sequence table, and detecting the same data and different data generated between each piece of monitoring data in real time;
deleting the same data in real time, and reserving the different data to generate a plurality of corresponding target monitoring data;
and sequentially carrying out fusion processing on a plurality of target monitoring data based on the target identifiers so as to correspondingly generate the data family, wherein the target identifiers have uniqueness.
Further, the step of sequentially fusing the plurality of target monitoring data based on the target identifier to correspondingly generate the data family includes:
when a plurality of target monitoring data are acquired, detecting a plurality of attribute values contained in the plurality of target monitoring data, and matching target sub-data matched with each attribute value in the plurality of target monitoring data;
and fusing target sub-data corresponding to each attribute value into a plurality of corresponding sub-data sets, and fusing the plurality of sub-data sets into the data family, wherein each attribute value has uniqueness.
Further, the method further comprises:
When the target data set is fused through the data family, extracting target numbers and target letters respectively contained in the target data set, and carrying out random arrangement processing on the target numbers and the target letters so as to generate a plurality of corresponding sequence codes;
and screening the plurality of sequence codes to generate a plurality of corresponding target sequence codes, randomly selecting one target sequence code, and carrying out dynamic encryption on the target data set.
A third aspect of an embodiment of the present invention proposes:
a computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements a data acquisition method based on a multisensor fusion technique as described above when executing the computer program.
A fourth aspect of the embodiment of the present invention proposes:
a readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements a data acquisition method based on a multisensor fusion technique as described above.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
Fig. 1 is a flowchart of a data acquisition method based on a multi-sensor fusion technology according to a first embodiment of the present invention;
fig. 2 is a block diagram of a data acquisition system based on a multi-sensor fusion technique according to a sixth embodiment of the present invention.
The invention will be further described in the following detailed description in conjunction with the above-described figures.
Detailed Description
In order that the invention may be readily understood, a more complete description of the invention will be rendered by reference to the appended drawings. Several embodiments of the invention are presented in the figures. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
It will be understood that when an element is referred to as being "mounted" on another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like are used herein for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Referring to fig. 1, a data acquisition method based on a multi-sensor fusion technology according to a first embodiment of the present invention is shown.
First embodiment
Specifically, the present embodiment provides:
a data acquisition method based on a multi-sensor fusion technology specifically comprises the following steps:
step S10, when a target monitoring area is obtained, detecting a plurality of monitoring points contained in the target monitoring area in real time, and constructing wireless communication connection between each monitoring point to generate a corresponding monitoring network;
step S20, detecting the central point of the target monitoring area in real time, and dividing the target monitoring area into a plurality of corresponding monitoring subareas according to a preset rule based on the central point;
Step S30, a plurality of target monitoring points contained in each monitoring sub-area are matched in the monitoring network, and the monitoring data acquired by the plurality of target monitoring points are subjected to primary fusion processing to generate a corresponding data group;
step S40, transmitting each data group to a preset cloud server, and performing secondary fusion processing on each data group in the preset cloud server to generate a corresponding target data set, where the target data set has uniqueness.
Specifically, in this embodiment, it should be noted that, first, in order to quickly and effectively collect the required data, and meanwhile, to perform corresponding classification processing on the collected data, when a server disposed in the background receives, in real time, a target monitoring area input by a user, all monitoring points corresponding to the current target monitoring area are further searched in an existing database, and specifically, the target monitoring area may be a county, a city or a province, and the corresponding monitoring can be completed. Based on the method, a plurality of current monitoring points are connected into a whole in a wireless mode through the existing wireless connection technology, and a needed monitoring network is correspondingly generated. Furthermore, since the area of the target monitoring area is generally larger, in order to facilitate the classification and collection of data, the center point corresponding to the current target monitoring area is further detected in real time, and meanwhile, the target monitoring area is correspondingly divided into a plurality of monitoring subareas through the current center point according to a preset rule, wherein the area of each monitoring subarea can be the same or different.
Furthermore, a certain amount of monitoring points are correspondingly arranged in each monitoring subarea, so that a plurality of target monitoring points corresponding to each current monitoring subarea are required to be matched in the monitoring network, and separate acquisition of data can be realized. Based on the above, after each monitoring sub-area respectively collects the corresponding monitoring data, in order to avoid the generation of redundant data, a fusion process is further required to be performed on the current plurality of monitoring data, and a plurality of corresponding data families are generated. Furthermore, in order to enable each worker to acquire the acquired data, at the moment, each generated data group is further uploaded to a cloud server which is set in advance, meanwhile, in order to further reduce the storage space of the data, a plurality of current data groups are further subjected to secondary fusion processing in the current cloud server, namely are finally fused into a whole, so that a needed target data set can be finally generated, the data contained in the target data set are unique, further, the subsequent processing of the data by a user can be facilitated, and the use experience of the user is correspondingly improved.
Second embodiment
Further, the step of dividing the target monitoring area into a plurality of corresponding monitoring subareas based on the central point according to a preset rule includes:
when the central point is acquired, detecting the area of the target monitoring area in real time, and calculating the target quantity corresponding to all the monitoring points in the target monitoring area;
calculating corresponding resolution factors in real time according to the area of the area and the target number, wherein the resolution factors are specific numerical values, and the size of the resolution factors is between 0 and 1;
dividing the target monitoring area into a plurality of corresponding monitoring subareas according to the central point and the splitting factors, wherein the area of each monitoring subarea is different.
Specifically, in this embodiment, in order to accurately and effectively divide the current target monitoring area, after detecting the center point of the current target monitoring area in real time, the area of the current target monitoring area needs to be correspondingly detected, and at the same time, the number of targets corresponding to all the monitoring points in the current target monitoring area needs to be calculated in real time.
Further, at this time, the required splitting factor may be directly calculated according to the area and the target number of the obtained areas respectively, specifically, for example, the area of the current target monitoring area is 1600 square meters, and correspondingly, the number of monitoring points in the current target monitoring area is 32, then the current area and the target number are divided at this time, specifically, the current target number is divided by the current area, and then the further obtained splitting factor may be "0.02". Based on the method, the target monitoring area is divided into a plurality of corresponding monitoring subareas in real time according to the current center point and the splitting factor, and the area of each monitoring subarea is different in size so as to facilitate subsequent processing.
Further, the step of dividing the target monitoring area into a plurality of corresponding monitoring subareas according to the center point and the splitting factor includes:
when the splitting factor is acquired, detecting the minimum length of the target monitoring area in real time, and calculating a corresponding dividing radius according to the minimum length and the splitting factor;
generating a corresponding target circular area according to the center point and the dividing radius, and detecting the target difference between the target circular area and the target monitoring area in real time;
And setting the target circular area as a first monitoring subarea, and setting the area corresponding to the target distinction as a second monitoring subarea.
Specifically, in this embodiment, it should also be noted that, after the required splitting factor is obtained through the above steps, in order to reasonably divide the current target monitoring area into a plurality of corresponding monitoring sub-areas, the minimum length of the current target monitoring area needs to be detected correspondingly at this time, specifically, since the shape of the current target monitoring area may be regular or irregular, but only the minimum length of the current target monitoring area needs to be detected in real time, specifically, for example, the minimum length may be "1600m", further, the corresponding dividing radius may be further calculated by immediately multiplying the minimum length of the current target monitoring area with the current splitting factor, and specifically, the calculated dividing radius may be "320m". Based on the method, a corresponding target circular area is generated in the target monitoring area according to the current center point and the current dividing radius in real time, and meanwhile, the difference between the current target circular area and the current target monitoring area is detected in real time. Namely, the overlapping area and the non-overlapping area between the current target circular area and the current target monitoring area are detected in real time, and the non-overlapping area is correspondingly set as the target difference between the current target circular area and the current target monitoring area. Based on the method, the current target circular area can be directly set as a first monitoring subarea which is needed, and correspondingly, the area corresponding to the target distinction between the current target circular area and the first monitoring subarea is set as a second monitoring subarea so as to facilitate subsequent processing.
Third embodiment
Further, the step of performing a fusion process on the monitoring data collected by the plurality of target monitoring points to generate a corresponding data group includes:
detecting target time generated when each target monitoring point collects the monitoring data at preset time intervals, sequencing the monitoring data collected by each target monitoring point once according to the sequence of each target time, and generating a corresponding first sequence table;
detecting the data quantity corresponding to the monitoring data acquired by each target monitoring point in real time, and secondarily sequencing the ranks in the first sequence table according to the data quantity of each monitoring data, and generating a corresponding second sequence table;
and carrying out fusion processing on each monitoring data for one time according to the ranking in the second sequence table so as to correspondingly generate the data family, wherein the first sequence table and the second sequence table are unique.
In addition, in this embodiment, in order to simply and effectively complete a fusion process between the monitoring data, specifically, a plurality of target monitoring points in each monitoring sub-area are detected once every 30 minutes. The method comprises the steps of detecting target moments generated by each target monitoring point when monitoring data are collected in real time, and meanwhile, sorting the monitoring data collected by each target monitoring point once directly according to the sequence of each target moment and generating a corresponding first sequence table as each target moment has a certain sequence.
Further, because the data volume collected by each target monitoring point is different, the data volume corresponding to the monitoring data collected by each target monitoring point is further detected in real time, and meanwhile, the ranking in the first sequence table is further subjected to secondary ranking according to the data volume of each monitoring data, and a corresponding second sequence table is generated. Based on the above, the above-mentioned each monitoring data is fused once according to the ranking in the second sequence table generated at present, and the required data family is fused correspondingly, so that the subsequent processing is facilitated.
Further, the step of performing a fusion process on each of the monitoring data according to the ranking in the second sequence table to generate the data family correspondingly includes:
sequentially adding corresponding target identifiers to each piece of monitoring data according to the ranking in the second sequence table, and detecting the same data and different data generated between each piece of monitoring data in real time;
deleting the same data in real time, and reserving the different data to generate a plurality of corresponding target monitoring data;
and sequentially carrying out fusion processing on a plurality of target monitoring data based on the target identifiers so as to correspondingly generate the data family, wherein the target identifiers have uniqueness.
In addition, in this embodiment, it should be further noted that, after the required second sequence table is obtained through the above steps, at this time, a corresponding target identifier may be sequentially added to each of the above monitored data directly according to the ranking in the current second sequence table, that is, the current plurality of monitored data are distinguished. Meanwhile, the same data and different data generated between each monitoring data are correspondingly detected. The method comprises the steps of carrying out corresponding deletion processing on the same data, carrying out corresponding reservation processing on the same data, generating a plurality of corresponding target monitoring data, and carrying out fusion processing on the plurality of current target monitoring data according to the target identification to generate each corresponding data group so as to facilitate subsequent processing, wherein the same data is redundant data, and the corresponding deletion processing is required, and the corresponding different data is data which need to be used.
Fourth embodiment
Further, the step of sequentially fusing the plurality of target monitoring data based on the target identifier to correspondingly generate the data family includes:
when a plurality of target monitoring data are acquired, detecting a plurality of attribute values contained in the plurality of target monitoring data, and matching target sub-data matched with each attribute value in the plurality of target monitoring data;
And fusing target sub-data corresponding to each attribute value into a plurality of corresponding sub-data sets, and fusing the plurality of sub-data sets into the data family, wherein each attribute value has uniqueness.
In this embodiment, it should be noted that, after the required target monitoring data is obtained through the above steps, since the target monitoring data may include multiple types of data, based on this, it is necessary to further detect in real time a plurality of attribute values included in the current target monitoring data, that is, attributes of a plurality of types of data. Further, the target sub-data adapted to each attribute value is further matched in all the current target monitoring data, meanwhile, the target sub-data corresponding to each attribute value is fused into a plurality of corresponding sub-data sets, and on the basis, the current plurality of sub-data sets can be directly fused into the data family, so that subsequent processing is facilitated.
Fifth embodiment
Further, the method further comprises:
when the target data set is fused through the data family, extracting target numbers and target letters respectively contained in the target data set, and carrying out random arrangement processing on the target numbers and the target letters so as to generate a plurality of corresponding sequence codes;
And screening the plurality of sequence codes to generate a plurality of corresponding target sequence codes, randomly selecting one target sequence code, and carrying out dynamic encryption on the target data set.
In this embodiment, it should be noted that, after the required target data set is finally obtained through the above steps, in order to further improve the security performance of the current target data set, the target numbers and target letters respectively included in the current target data set may be further extracted at this time, and meanwhile, the current target numbers and target letters are randomly arranged, and a plurality of corresponding sequence passwords may be generated.
Furthermore, in order to ensure the effectiveness of the passwords, a plurality of current sequence passwords are screened at the moment, a plurality of required target sequence codes are screened, and on the basis, only one target sequence code is selected at random to serve as an encryption key of the current target data set, so that the encryption processing of each target data set can be completed simply and quickly.
Sixth embodiment
Referring to fig. 2, a sixth embodiment of the present invention provides:
a data acquisition system based on a multi-sensor fusion technique, wherein the system comprises:
The detection module is used for detecting a plurality of monitoring points contained in the target monitoring area in real time when the target monitoring area is acquired, and constructing wireless communication connection between each monitoring point so as to generate a corresponding monitoring network;
the dividing module is used for detecting the center point of the target monitoring area in real time and dividing the target monitoring area into a plurality of corresponding monitoring subareas according to a preset rule based on the center point;
the first fusion module is used for matching a plurality of target monitoring points contained in each monitoring sub-area in the monitoring network, and carrying out primary fusion processing on monitoring data acquired by the plurality of target monitoring points so as to generate a corresponding data family;
and the second fusion module is used for transmitting each data group to a preset cloud server, and carrying out secondary fusion processing on each data group in the preset cloud server so as to generate a corresponding target data set, wherein the target data set has uniqueness.
Further, the step of dividing the target monitoring area into a plurality of corresponding monitoring subareas based on the central point according to a preset rule includes:
When the central point is acquired, detecting the area of the target monitoring area in real time, and calculating the target quantity corresponding to all the monitoring points in the target monitoring area;
calculating corresponding resolution factors in real time according to the area of the area and the target number, wherein the resolution factors are specific numerical values, and the size of the resolution factors is between 0 and 1;
dividing the target monitoring area into a plurality of corresponding monitoring subareas according to the central point and the splitting factors, wherein the area of each monitoring subarea is different.
Further, the step of dividing the target monitoring area into a plurality of corresponding monitoring subareas according to the center point and the splitting factor includes:
when the splitting factor is acquired, detecting the minimum length of the target monitoring area in real time, and calculating a corresponding dividing radius according to the minimum length and the splitting factor;
generating a corresponding target circular area according to the center point and the dividing radius, and detecting the target difference between the target circular area and the target monitoring area in real time;
And setting the target circular area as a first monitoring subarea, and setting the area corresponding to the target distinction as a second monitoring subarea.
Further, the step of performing a fusion process on the monitoring data collected by the plurality of target monitoring points to generate a corresponding data group includes:
detecting target time generated when each target monitoring point collects the monitoring data at preset time intervals, sequencing the monitoring data collected by each target monitoring point once according to the sequence of each target time, and generating a corresponding first sequence table;
detecting the data quantity corresponding to the monitoring data acquired by each target monitoring point in real time, and secondarily sequencing the ranks in the first sequence table according to the data quantity of each monitoring data, and generating a corresponding second sequence table;
and carrying out fusion processing on each monitoring data for one time according to the ranking in the second sequence table so as to correspondingly generate the data family, wherein the first sequence table and the second sequence table are unique.
Further, the step of performing a fusion process on each of the monitoring data according to the ranking in the second sequence table to generate the data family correspondingly includes:
Sequentially adding corresponding target identifiers to each piece of monitoring data according to the ranking in the second sequence table, and detecting the same data and different data generated between each piece of monitoring data in real time;
deleting the same data in real time, and reserving the different data to generate a plurality of corresponding target monitoring data;
and sequentially carrying out fusion processing on a plurality of target monitoring data based on the target identifiers so as to correspondingly generate the data family, wherein the target identifiers have uniqueness.
Further, the step of sequentially fusing the plurality of target monitoring data based on the target identifier to correspondingly generate the data family includes:
when a plurality of target monitoring data are acquired, detecting a plurality of attribute values contained in the plurality of target monitoring data, and matching target sub-data matched with each attribute value in the plurality of target monitoring data;
and fusing target sub-data corresponding to each attribute value into a plurality of corresponding sub-data sets, and fusing the plurality of sub-data sets into the data family, wherein each attribute value has uniqueness.
Further, the method further comprises:
When the target data set is fused through the data family, extracting target numbers and target letters respectively contained in the target data set, and carrying out random arrangement processing on the target numbers and the target letters so as to generate a plurality of corresponding sequence codes;
and screening the plurality of sequence codes to generate a plurality of corresponding target sequence codes, randomly selecting one target sequence code, and carrying out dynamic encryption on the target data set.
Seventh embodiment
A seventh embodiment of the present invention provides a computer, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the data acquisition method based on the multisensor fusion technique as described above when executing the computer program.
Eighth embodiment
An eighth embodiment of the present invention provides a readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements a data acquisition method based on a multisensor fusion technique as described above.
In summary, the above embodiments of the present invention provide
The above-described respective modules may be functional modules or program modules, and may be implemented by software or hardware. For modules implemented in hardware, the various modules described above may be located in the same processor; or the above modules may be located in different processors in any combination.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (10)
1. A data acquisition method based on a multi-sensor fusion technology, the method comprising:
When a target monitoring area is acquired, detecting a plurality of monitoring points contained in the target monitoring area in real time, and constructing wireless communication connection between each monitoring point to generate a corresponding monitoring network;
detecting a central point of the target monitoring area in real time, and dividing the target monitoring area into a plurality of corresponding monitoring subareas according to a preset rule based on the central point;
matching a plurality of target monitoring points contained in each monitoring subarea in the monitoring network, and carrying out primary fusion processing on monitoring data acquired by the plurality of target monitoring points to generate a corresponding data family;
transmitting each data group to a preset cloud server, and performing secondary fusion processing on each data group in the preset cloud server to generate a corresponding target data set, wherein the target data set has uniqueness.
2. The data acquisition method based on the multi-sensor fusion technology according to claim 1, wherein: the step of dividing the target monitoring area into a plurality of corresponding monitoring subareas based on the central point according to a preset rule comprises the following steps:
When the central point is acquired, detecting the area of the target monitoring area in real time, and calculating the target quantity corresponding to all the monitoring points in the target monitoring area;
calculating corresponding resolution factors in real time according to the area of the area and the target number, wherein the resolution factors are specific numerical values, and the size of the resolution factors is between 0 and 1;
dividing the target monitoring area into a plurality of corresponding monitoring subareas according to the central point and the splitting factors, wherein the area of each monitoring subarea is different.
3. The data acquisition method based on the multi-sensor fusion technology according to claim 2, wherein: the step of dividing the target monitoring area into a plurality of corresponding monitoring subareas according to the center point and the splitting factor comprises the following steps:
when the splitting factor is acquired, detecting the minimum length of the target monitoring area in real time, and calculating a corresponding dividing radius according to the minimum length and the splitting factor;
generating a corresponding target circular area according to the center point and the dividing radius, and detecting the target difference between the target circular area and the target monitoring area in real time;
And setting the target circular area as a first monitoring subarea, and setting the area corresponding to the target distinction as a second monitoring subarea.
4. The data acquisition method based on the multi-sensor fusion technology according to claim 1, wherein: the step of performing primary fusion processing on the monitoring data collected by the plurality of target monitoring points to generate a corresponding data group comprises the following steps:
detecting target time generated when each target monitoring point collects the monitoring data at preset time intervals, sequencing the monitoring data collected by each target monitoring point once according to the sequence of each target time, and generating a corresponding first sequence table;
detecting the data quantity corresponding to the monitoring data acquired by each target monitoring point in real time, and secondarily sequencing the ranks in the first sequence table according to the data quantity of each monitoring data, and generating a corresponding second sequence table;
and carrying out fusion processing on each monitoring data for one time according to the ranking in the second sequence table so as to correspondingly generate the data family, wherein the first sequence table and the second sequence table are unique.
5. The method for data acquisition based on the multi-sensor fusion technique according to claim 4, wherein: the step of performing a fusion process on each monitoring data according to the ranking in the second sequence table to correspondingly generate the data family includes:
sequentially adding corresponding target identifiers to each piece of monitoring data according to the ranking in the second sequence table, and detecting the same data and different data generated between each piece of monitoring data in real time;
deleting the same data in real time, and reserving the different data to generate a plurality of corresponding target monitoring data;
and sequentially carrying out fusion processing on a plurality of target monitoring data based on the target identifiers so as to correspondingly generate the data family, wherein the target identifiers have uniqueness.
6. The method for data acquisition based on the multi-sensor fusion technique according to claim 5, wherein: the step of sequentially fusing the plurality of target monitoring data based on the target identification to correspondingly generate the data family comprises the following steps:
when a plurality of target monitoring data are acquired, detecting a plurality of attribute values contained in the plurality of target monitoring data, and matching target sub-data matched with each attribute value in the plurality of target monitoring data;
And fusing target sub-data corresponding to each attribute value into a plurality of corresponding sub-data sets, and fusing the plurality of sub-data sets into the data family, wherein each attribute value has uniqueness.
7. The method for data acquisition based on the multi-sensor fusion technique according to claim 6, wherein: the method further comprises the steps of:
when the target data set is fused through the data family, extracting target numbers and target letters respectively contained in the target data set, and carrying out random arrangement processing on the target numbers and the target letters so as to generate a plurality of corresponding sequence codes;
and screening the plurality of sequence codes to generate a plurality of corresponding target sequence codes, randomly selecting one target sequence code, and carrying out dynamic encryption on the target data set.
8. A data acquisition system based on a multi-sensor fusion technique, the system comprising:
the detection module is used for detecting a plurality of monitoring points contained in the target monitoring area in real time when the target monitoring area is acquired, and constructing wireless communication connection between each monitoring point so as to generate a corresponding monitoring network;
The dividing module is used for detecting the center point of the target monitoring area in real time and dividing the target monitoring area into a plurality of corresponding monitoring subareas according to a preset rule based on the center point;
the first fusion module is used for matching a plurality of target monitoring points contained in each monitoring sub-area in the monitoring network, and carrying out primary fusion processing on monitoring data acquired by the plurality of target monitoring points so as to generate a corresponding data family;
and the second fusion module is used for transmitting each data group to a preset cloud server, and carrying out secondary fusion processing on each data group in the preset cloud server so as to generate a corresponding target data set, wherein the target data set has uniqueness.
9. A computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the data acquisition method based on the multisensor fusion technique according to any one of claims 1 to 7 when executing the computer program.
10. A readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements a data acquisition method based on a multisensor fusion technique according to any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118101718A (en) * | 2024-04-17 | 2024-05-28 | 江西省水投江河信息技术有限公司 | Water conservancy monitoring data transmission method and system based on Internet of things |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160154802A1 (en) * | 2014-12-02 | 2016-06-02 | Nec Laboratories America, Inc. | Quality control engine for complex physical systems |
| CN107172575A (en) * | 2017-05-29 | 2017-09-15 | 上海喆之信息科技有限公司 | A kind of transmission line of electricity monitoring system based on cloud computing |
| WO2022016563A1 (en) * | 2020-07-23 | 2022-01-27 | 南京科沃信息技术有限公司 | Ground monitoring system for plant-protection unmanned aerial vehicle, and monitoring method for same |
| JP2022087821A (en) * | 2020-12-01 | 2022-06-13 | 富士通株式会社 | Data fusion method and device |
| WO2022247915A1 (en) * | 2021-05-27 | 2022-12-01 | 北京百度网讯科技有限公司 | Fusion positioning method and apparatus, device, storage medium and program product |
| CN116228539A (en) * | 2023-03-10 | 2023-06-06 | 贵州师范大学 | Unmanned aerial vehicle remote sensing image stitching method |
| CN116866865A (en) * | 2023-09-04 | 2023-10-10 | 江西五十铃汽车有限公司 | Whole vehicle controller data sharing method and system |
| CN117108458A (en) * | 2023-08-04 | 2023-11-24 | 国家电投集团江苏电力有限公司 | Marine wind power partition corrosion safety assessment method, system, equipment and storage medium |
-
2024
- 2024-01-02 CN CN202410001714.8A patent/CN117499887B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160154802A1 (en) * | 2014-12-02 | 2016-06-02 | Nec Laboratories America, Inc. | Quality control engine for complex physical systems |
| CN107172575A (en) * | 2017-05-29 | 2017-09-15 | 上海喆之信息科技有限公司 | A kind of transmission line of electricity monitoring system based on cloud computing |
| WO2022016563A1 (en) * | 2020-07-23 | 2022-01-27 | 南京科沃信息技术有限公司 | Ground monitoring system for plant-protection unmanned aerial vehicle, and monitoring method for same |
| JP2022087821A (en) * | 2020-12-01 | 2022-06-13 | 富士通株式会社 | Data fusion method and device |
| WO2022247915A1 (en) * | 2021-05-27 | 2022-12-01 | 北京百度网讯科技有限公司 | Fusion positioning method and apparatus, device, storage medium and program product |
| CN116228539A (en) * | 2023-03-10 | 2023-06-06 | 贵州师范大学 | Unmanned aerial vehicle remote sensing image stitching method |
| CN117108458A (en) * | 2023-08-04 | 2023-11-24 | 国家电投集团江苏电力有限公司 | Marine wind power partition corrosion safety assessment method, system, equipment and storage medium |
| CN116866865A (en) * | 2023-09-04 | 2023-10-10 | 江西五十铃汽车有限公司 | Whole vehicle controller data sharing method and system |
Non-Patent Citations (1)
| Title |
|---|
| 米春: "基于多传感器的城市隧道环境监测数据协同融合方法研究", 现代隧道技术, 15 October 2023 (2023-10-15) * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118101718A (en) * | 2024-04-17 | 2024-05-28 | 江西省水投江河信息技术有限公司 | Water conservancy monitoring data transmission method and system based on Internet of things |
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