CN116910117A - Multi-dimensional high-calculation-force microclimate sensor data analysis system and method - Google Patents

Multi-dimensional high-calculation-force microclimate sensor data analysis system and method Download PDF

Info

Publication number
CN116910117A
CN116910117A CN202310713513.6A CN202310713513A CN116910117A CN 116910117 A CN116910117 A CN 116910117A CN 202310713513 A CN202310713513 A CN 202310713513A CN 116910117 A CN116910117 A CN 116910117A
Authority
CN
China
Prior art keywords
data
analysis
sensor data
grouping
sensor
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202310713513.6A
Other languages
Chinese (zh)
Inventor
蔡云
尤占山
刘琪
陈家伟
陈泽彬
邓文华
张利新
韦兰顺
戴春苑
黄应桢
蔡菱
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong Xindian Electric Power Technology Co ltd
Original Assignee
Guangdong Xindian Electric Power Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangdong Xindian Electric Power Technology Co ltd filed Critical Guangdong Xindian Electric Power Technology Co ltd
Priority to CN202310713513.6A priority Critical patent/CN116910117A/en
Publication of CN116910117A publication Critical patent/CN116910117A/en
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/02—Instruments for indicating weather conditions by measuring two or more variables, e.g. humidity, pressure, temperature, cloud cover or wind speed
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2465—Query processing support for facilitating data mining operations in structured databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/248—Presentation of query results
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2216/00—Indexing scheme relating to additional aspects of information retrieval not explicitly covered by G06F16/00 and subgroups
    • G06F2216/03—Data mining
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Physics & Mathematics (AREA)
  • Computational Linguistics (AREA)
  • Environmental & Geological Engineering (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Mathematical Physics (AREA)
  • Probability & Statistics with Applications (AREA)
  • Software Systems (AREA)
  • Fuzzy Systems (AREA)
  • Environmental Sciences (AREA)
  • Ecology (AREA)
  • Atmospheric Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Testing Or Calibration Of Command Recording Devices (AREA)

Abstract

The embodiment of the application discloses a multi-dimensional high-calculation-force microclimate sensor data analysis system and a method, wherein the method comprises the following steps: receiving a plurality of sensor data with different dimensions, which are reported by a microclimate sensor, and grouping the sensor data to obtain a plurality of grouping data, wherein each grouping data comprises sensor data with at least two dimensions; carrying out joint analysis on each piece of grouping data to obtain grouping analysis results; and integrating all the grouping analysis results to obtain comprehensive analysis results corresponding to the microclimate sensors. According to the scheme, the problems that in the prior art, most of single-dimension monitoring and result display are carried out during data analysis, so that analysis results lack deeper and high-dimension reference content are solved, monitoring data are further mined through multi-dimension joint analysis, and the reference property of the analysis results and the diversity of information display are improved.

Description

Multi-dimensional high-calculation-force microclimate sensor data analysis system and method
Technical Field
The embodiment of the application relates to the technical field of sensor data analysis, in particular to a multi-dimensional high-calculation-force microclimate sensor data analysis system and method.
Background
With the development of hardware equipment in recent years, the precision and the intelligent and integrated degree of the sensor are also higher and higher. In the related art, there are devices such as microclimate sensors that integrate a plurality of different types of sensors, perform comprehensive monitoring or parameter acquisition, and perform the same. The microclimate sensor can monitor air temperature, air humidity, wind speed, wind direction, atmospheric pressure, optical rainfall, total radiation, PM2.5 and PM10 through a high-integration structure, and can realize 24-hour continuous on-line monitoring of outdoor meteorological parameters.
In the related art, when monitoring is performed based on the microclimate sensor, the microclimate sensor can report a plurality of different monitoring parameters for monitoring analysis at the back end. Most of the monitoring and result display in a single dimension lack multi-dimensional linkage analysis.
Disclosure of Invention
The embodiment of the application provides a multi-dimensional high-calculation-force microclimate sensor data analysis system and a multi-dimensional high-calculation-force microclimate sensor data analysis method, which solve the problems that in the prior art, most of single-dimensional monitoring and result display are carried out during data analysis, so that analysis results lack deeper and high-dimensional reference content, and monitoring data are further mined through multi-dimensional joint analysis, so that the reference property of the analysis results and the diversity of information display are improved.
In a first aspect, an embodiment of the present application provides a method for analyzing data of a microclimate sensor with multi-dimensions and high calculation force, including: receiving a plurality of sensor data with different dimensions, which are reported by a microclimate sensor, and grouping the sensor data to obtain a plurality of grouping data, wherein each grouping data comprises sensor data with at least two dimensions;
carrying out joint analysis on each piece of grouping data to obtain grouping analysis results;
and integrating all the grouping analysis results to obtain comprehensive analysis results corresponding to the microclimate sensors.
Optionally, the grouping the sensor data to obtain a plurality of grouping data includes:
acquiring recorded sensor association relations;
and grouping the sensor data based on the sensor association relationship to obtain a plurality of grouping data.
Optionally, before the acquiring the recorded sensor association relationship, the method further includes:
and determining and recording the association relation of the sensors according to the recorded historical fault data.
Optionally, the performing joint analysis on each packet data to obtain a packet analysis result includes:
and carrying out association analysis on the sensor data recorded in each group of data to obtain a corresponding group analysis result.
Optionally, the performing association analysis on the sensor data recorded in each packet data includes:
determining a degree of change of the sensor data recorded in each packet data;
and determining a correlation analysis result corresponding to each group of data based on the change degree of each sensor data.
Optionally, the association degree analysis result includes a numerical association and a numerical non-association, and the integrating the grouping analysis results to obtain a comprehensive analysis result corresponding to the microclimate sensor includes:
and obtaining a comprehensive analysis result corresponding to the microclimate sensor based on the analysis result of the numerical association in the association analysis result.
Optionally, the obtaining the comprehensive analysis result corresponding to the microclimate sensor based on the analysis result of the numerical association in the association analysis result includes:
determining a pre-recorded optional analysis conclusion corresponding to the analysis result of the numerical association in the association degree analysis result;
and combining the optional analysis results to obtain a comprehensive analysis result and displaying the comprehensive analysis result.
In a second aspect, embodiments of the present application further provide a multi-dimensional high-computing-power microclimate sensor data analysis system, comprising: the data receiving module is configured to receive a plurality of sensor data with different dimensions reported by the microclimate sensor, and group the sensor data to obtain a plurality of group data, wherein each group data comprises sensor data with at least two dimensions;
the grouping analysis module is configured to perform joint analysis on each piece of grouping data to obtain a grouping analysis result;
and the comprehensive analysis module is configured to integrate the grouping analysis results to obtain comprehensive analysis results corresponding to the microclimate sensors.
In a third aspect, embodiments of the present application further provide a multi-dimensional high-computation-force microclimate sensor data analysis device, the device including:
one or more processors;
storage means for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the multi-dimensional high-computing-power microclimate sensor data analysis method according to the embodiments of the present application.
In a fourth aspect, embodiments of the present application also provide a storage medium storing computer-executable instructions that, when executed by a computer processor, are configured to perform the multi-dimensional high-power microclimate sensor data analysis method of embodiments of the present application.
In the embodiment of the application, a plurality of sensor data with different dimensions are obtained by receiving the sensor data with different dimensions reported by a microclimate sensor, and each piece of grouping data comprises at least two pieces of sensor data with different dimensions; carrying out joint analysis on each piece of grouping data to obtain grouping analysis results; and integrating all the grouping analysis results to obtain comprehensive analysis results corresponding to the microclimate sensors. According to the scheme, the problems that in the prior art, most of single-dimension monitoring and result display are carried out during data analysis, so that analysis results lack deeper and high-dimension reference content are solved, monitoring data are further mined through multi-dimension joint analysis, and the reference property of the analysis results and the diversity of information display are improved.
Drawings
FIG. 1 is a flow chart of a method for analyzing data of a multi-dimensional high-calculation-force microclimate sensor according to an embodiment of the present application;
FIG. 2 is a flow chart of another multi-dimensional high-computing-power microclimate sensor data analysis method provided by an embodiment of the present application;
FIG. 3 is a block diagram of a data analysis method of a multi-dimensional high-calculation-force microclimate sensor according to an embodiment of the present application;
FIG. 4 is a schematic structural diagram of a multi-dimensional high-computing-force microclimate sensor data analysis device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application will be described in further detail below with reference to the drawings and examples. It should be understood that the particular embodiments described herein are illustrative only and are not limiting of embodiments of the application. It should be further noted that, for convenience of description, only some, but not all of the structures related to the embodiments of the present application are shown in the drawings.
Fig. 1 is a flowchart of a multi-dimensional high-calculation-force microclimate sensor data analysis method according to an embodiment of the present application, and as shown in fig. 1, the scheme execution body is a background server, and specifically includes:
step S101, receiving a plurality of sensor data with different dimensions, which are reported by a microclimate sensor, and grouping the sensor data to obtain a plurality of grouping data, wherein each grouping data comprises sensor data with at least two dimensions.
In one embodiment, the microclimate sensor may report data to the background in real-time or periodically. The microclimate sensor can be equipment integrating multiple types of sensors, and the microclimate sensor is used for collecting corresponding parameters respectively through integrating multiple different types of sensors and reporting the parameters to the background.
In one embodiment, the received sensor data for a plurality of different dimensions is grouped, where each dimension corresponds to a sensor type. After the packet data is obtained, each packet thereof is analyzed. Wherein each packet data comprises sensor data of at least two dimensions.
Optionally, a grouping manner specifically includes:
step S1011, acquiring a recorded sensor association relationship.
In one embodiment, the sensor association is pre-recorded. Alternatively, the sensor association may be determined from recorded historical fault data. If the historical fault data are analyzed, abnormal data corresponding to the type of the historical fault are judged when the current historical fault occurs, and the association relation of the sensors corresponding to the abnormal data type in the historical fault is established. For a certain piece of historical fault data, three types of abnormal data corresponding to the historical fault data are provided, each type of abnormal data corresponds to one type of sensor, the three types of sensors are determined to have an association relationship, and corresponding collected data also have an association relationship.
Step S1012, grouping the sensor data based on the sensor association relationship to obtain a plurality of grouping data.
In one embodiment, grouping is performed based on a determined sensor association. The data uploaded by the microclimate sensor comprises data corresponding to 10 sensors respectively, which are respectively marked as sensor data 1, sensor data 2, sensor data 3, sensor data 4, sensor data 5, sensor data 6, sensor data 7, sensor data 8, sensor data 9 and sensor data 10. At this time, based on a pre-recorded association relationship such as sensor data 1, sensor data 2, and sensor data 4; sensor data 2, sensor data 3, and sensor data 5; when the sensor data 7 and the sensor data 8 are associated and the sensor data 9 and the sensor data 10 are associated, the obtained group is "sensor data 1, sensor data 2 and sensor data 4", "sensor data 2, sensor data 3 and sensor data 5", "sensor data 7, sensor data 8" and "sensor data 9 and sensor data 10".
And step S102, carrying out joint analysis on each piece of grouping data to obtain grouping analysis results.
In one embodiment, after obtaining the packets, a joint analysis is performed on the data in each packet to obtain a packet analysis result. Alternatively, the process of joint analysis may be: and carrying out association analysis on the sensor data recorded in each group of data to obtain a corresponding group analysis result. And analyzing the association degree between the sensor data of different dimensions in each group data to obtain a group analysis result corresponding to each group.
And step S103, integrating all the grouping analysis results to obtain comprehensive analysis results corresponding to the microclimate sensors.
In one embodiment, after each grouping analysis result is obtained, it is integrated to obtain a comprehensive analysis result corresponding to the microclimate sensor. Optionally, when performing packet analysis, the analysis result of each packet obtained includes a numerical association and a numerical non-association. Wherein, the numerical value association represents that the corresponding data among the various types of sensors has association, and otherwise, no association exists. Correspondingly, the integration of each grouping analysis result to obtain the comprehensive analysis result corresponding to the microclimate sensor comprises the following steps: and obtaining a comprehensive analysis result corresponding to the microclimate sensor based on the analysis result of the numerical association in the association analysis result. I.e. for the results for which there is a numerical correlation, to obtain a comprehensive analysis result.
Optionally, obtaining the comprehensive analysis result corresponding to the microclimate sensor based on the analysis result of the numerical association in the association analysis result includes: determining a pre-recorded optional analysis conclusion corresponding to the analysis result of the numerical association in the association degree analysis result; and combining the optional analysis results to obtain a comprehensive analysis result and displaying the comprehensive analysis result. In one embodiment, a respective plurality of selectable analytical findings are pre-recorded for each associated sensor correspondence. If the test and actual monitoring conditions are passed, after the sensor association relations are obtained, a plurality of optional analysis conclusions are correspondingly recorded for each sensor association relation. If the corresponding analysis conclusion is preset for the sensors 1, 2 and 4 with the association relationship, and the numerical association is determined after the numerical association analysis between the data in the group is performed and the grouping is performed, the corresponding analysis conclusion preset is included in the final comprehensive result displayed correspondingly.
According to the method, the sensor data in different dimensions are received and reported by the microclimate sensor, so that the sensor data are grouped to obtain a plurality of group data, wherein each group data comprises at least two dimensions of sensor data; carrying out joint analysis on each piece of grouping data to obtain grouping analysis results; and integrating all the grouping analysis results to obtain comprehensive analysis results corresponding to the microclimate sensors. According to the scheme, the problems that in the prior art, most of single-dimension monitoring and result display are carried out during data analysis, so that analysis results lack deeper and high-dimension reference content are solved, monitoring data are further mined through multi-dimension joint analysis, and the reference property of the analysis results and the diversity of information display are improved.
Fig. 2 is a flowchart of another multi-dimensional high-calculation-force microclimate sensor data analysis method according to an embodiment of the present application, which provides a specific process of performing association analysis on sensor data recorded in each group data, as shown in fig. 2, including:
step S201, receiving a plurality of sensor data with different dimensions reported by a microclimate sensor, acquiring a recorded sensor association relationship, and grouping the sensor data based on the sensor association relationship to obtain a plurality of grouping data.
Step S202, determining the change degree of the sensor data recorded in each piece of grouping data, and determining the association degree analysis result corresponding to each piece of grouping data based on the change degree of each piece of sensor data.
In one embodiment, the degree of change characterizes the amount of change in the data. The sensor data recorded in the packet data comprises a plurality of continuously acquired data for each type of sensor, which are different according to different numbers of acquisition intervals and reporting period conditions. Taking the example of collecting data once every minute, the statistical 1 hour data, which includes 60 for each type of sensor data. The degree of change in 1 hour was evaluated based on the 60 data. The specific representation mode of the variation degree can have a plurality of different realization methods. If the 60 data are subjected to average value obtaining of every 10 continuous data, 6 average value values are obtained, the change rate is calculated in sequence every two in the 6 average value values, and if more than half of the change rate is greater than a preset change rate threshold value, the change degree is judged to be large, otherwise, the change degree is small; and similarly, determining the change degree of the sensor data in the rest of the sensor data in the same group in sequence to obtain a conclusion that the change degree is large and small, and if the change degree of each corresponding sensor data in one group is large, judging that the corresponding association degree analysis results in the group are associated with numerical values, otherwise, are not associated with the numerical values. The preset change rate threshold may be set according to different sensor types, for example, 10%.
And step 203, obtaining a comprehensive analysis result corresponding to the microclimate sensor based on the analysis result of the numerical association in the association analysis result.
According to the method, the sensor data in different dimensions are received and reported by the microclimate sensor, so that the sensor data are grouped to obtain a plurality of group data, wherein each group data comprises at least two dimensions of sensor data; carrying out joint analysis on each piece of grouping data to obtain grouping analysis results; and integrating all the grouping analysis results to obtain comprehensive analysis results corresponding to the microclimate sensors. According to the scheme, the problems that in the prior art, most of single-dimension monitoring and result display are carried out during data analysis, so that analysis results lack deeper and high-dimension reference content are solved, monitoring data are further mined through multi-dimension joint analysis, and the reference property of the analysis results and the diversity of information display are improved.
Fig. 3 is a block diagram of a module structure of a multi-dimensional high-calculation-force microclimate sensor data analysis method according to an embodiment of the present application, where the smart cable is used for executing the multi-dimensional high-calculation-force microclimate sensor data analysis method according to the above embodiment, and has functional modules and beneficial effects corresponding to the execution method. As shown in fig. 3, the apparatus specifically includes:
the data receiving module 101 is configured to receive a plurality of sensor data with different dimensions reported by the microclimate sensor, and group the sensor data to obtain a plurality of group data, wherein each group data comprises sensor data with at least two dimensions;
the packet analysis module 102 is configured to perform joint analysis on each packet data to obtain a packet analysis result;
and the comprehensive analysis module 103 is configured to integrate the grouping analysis results to obtain comprehensive analysis results corresponding to the microclimate sensors.
According to the scheme, the sensor data with different dimensions, which are reported by the microclimate sensor, are received, and the sensor data are grouped to obtain a plurality of group data, wherein each group data comprises at least two-dimensional sensor data; carrying out joint analysis on each piece of grouping data to obtain grouping analysis results; and integrating all the grouping analysis results to obtain comprehensive analysis results corresponding to the microclimate sensors. According to the scheme, the problems that in the prior art, most of single-dimension monitoring and result display are carried out during data analysis, so that analysis results lack deeper and high-dimension reference content are solved, monitoring data are further mined through multi-dimension joint analysis, and the reference property of the analysis results and the diversity of information display are improved. The functions that each module performs correspondingly are exemplified as follows:
in one possible embodiment, the grouping the sensor data into a plurality of groups includes:
acquiring recorded sensor association relations;
and grouping the sensor data based on the sensor association relationship to obtain a plurality of grouping data.
In one possible embodiment, before the acquiring the recorded sensor association relationship, the method further includes:
and determining and recording the association relation of the sensors according to the recorded historical fault data.
In one possible embodiment, the performing the joint analysis on each packet data to obtain a packet analysis result includes:
and carrying out association analysis on the sensor data recorded in each group of data to obtain a corresponding group analysis result.
In one possible embodiment, the performing association analysis on the sensor data recorded in each packet data includes:
determining a degree of change of the sensor data recorded in each packet data;
and determining a correlation analysis result corresponding to each group of data based on the change degree of each sensor data.
In one possible embodiment, the association degree analysis result includes a numerical association and a numerical non-association, and the integrating the grouping analysis results to obtain the comprehensive analysis result corresponding to the microclimate sensor includes:
and obtaining a comprehensive analysis result corresponding to the microclimate sensor based on the analysis result of the numerical association in the association analysis result.
In one possible embodiment, the obtaining the comprehensive analysis result corresponding to the microclimate sensor based on the analysis result of the numerical correlation in the correlation analysis result includes:
determining a pre-recorded optional analysis conclusion corresponding to the analysis result of the numerical association in the association degree analysis result;
and combining the optional analysis results to obtain a comprehensive analysis result and displaying the comprehensive analysis result.
FIG. 4 is a schematic structural diagram of a multi-dimensional high-calculation-force microclimate sensor data analysis device according to an embodiment of the present application, and as shown in FIG. 4, the device includes a processor 201, a memory 202, an input device 203 and an output device 204; the number of processors 201 in the device may be one or more, one processor 201 being taken as an example in fig. 4; the processor 201, memory 202, input devices 203, and output devices 204 in the apparatus may be connected by a bus or other means, for example in fig. 4. The memory 202 is used as a computer readable storage medium for storing software programs, computer executable programs and modules, such as program instructions/modules corresponding to the multi-dimensional high-computing-power microclimate sensor data analysis method according to the embodiments of the present application. The processor 201 executes various functional applications of the device and data processing by running software programs, instructions and modules stored in the memory 202, i.e., implementing the multi-dimensional high-power microclimate sensor data analysis method described above. The input means 203 may be used to receive entered numeric or character information and to generate key signal inputs related to user settings and function control of the device. The output device 204 may include a display device such as a display screen.
Embodiments of the present application also provide a storage medium containing computer-executable instructions for performing a multi-dimensional high-computing-power microclimate sensor data analysis method when executed by a computer processor, the method comprising: receiving a plurality of sensor data with different dimensions, which are reported by a microclimate sensor, and grouping the sensor data to obtain a plurality of grouping data, wherein each grouping data comprises sensor data with at least two dimensions;
carrying out joint analysis on each piece of grouping data to obtain grouping analysis results;
and integrating all the grouping analysis results to obtain comprehensive analysis results corresponding to the microclimate sensors.
It should be noted that, in the embodiment of the multi-dimensional high-calculation-force microclimate sensor data analysis method device, each unit and module included are only divided according to the functional logic, but not limited to the above-mentioned division, so long as the corresponding functions can be realized; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the embodiments of the present application.
Note that the above is only a preferred embodiment of the present application and the technical principle applied. It will be understood by those skilled in the art that the embodiments of the present application are not limited to the particular embodiments described herein, but are capable of numerous obvious changes, rearrangements and substitutions without departing from the scope of the embodiments of the present application. Therefore, while the embodiments of the present application have been described in connection with the above embodiments, the embodiments of the present application are not limited to the above embodiments, but may include many other equivalent embodiments without departing from the spirit of the embodiments of the present application, and the scope of the embodiments of the present application is determined by the scope of the appended claims.

Claims (10)

1. The data analysis method of the multi-dimensional high-calculation-force microclimate sensor is characterized by comprising the following steps of:
receiving a plurality of sensor data with different dimensions, which are reported by a microclimate sensor, and grouping the sensor data to obtain a plurality of grouping data, wherein each grouping data comprises sensor data with at least two dimensions;
carrying out joint analysis on each piece of grouping data to obtain grouping analysis results;
and integrating all the grouping analysis results to obtain comprehensive analysis results corresponding to the microclimate sensors.
2. The multi-dimensional high-power microclimate sensor data analysis method of claim 1, wherein grouping the sensor data into a plurality of groups comprises:
acquiring recorded sensor association relations;
and grouping the sensor data based on the sensor association relationship to obtain a plurality of grouping data.
3. The method for analyzing data of a multi-dimensional high-computation-force microclimate sensor according to claim 2, further comprising, prior to the acquiring the recorded sensor association:
and determining and recording the association relation of the sensors according to the recorded historical fault data.
4. A multi-dimensional high-power microclimate sensor data analysis method according to any one of claims 1-3, wherein the joint analysis of each group of data to obtain a group analysis result comprises:
and carrying out association analysis on the sensor data recorded in each group of data to obtain a corresponding group analysis result.
5. The multi-dimensional high-power microclimate sensor data analysis method of claim 4, wherein the correlating the sensor data recorded in each packet data comprises:
determining a degree of change of the sensor data recorded in each packet data;
and determining a correlation analysis result corresponding to each group of data based on the change degree of each sensor data.
6. The multi-dimensional high-power microclimate sensor data analysis method according to any one of claims 1-3, wherein the association degree analysis results include numerical association and numerical non-association, and the integrating each grouping analysis result to obtain a comprehensive analysis result corresponding to the microclimate sensor comprises:
and obtaining a comprehensive analysis result corresponding to the microclimate sensor based on the analysis result of the numerical association in the association analysis result.
7. The multi-dimensional high-computation-force microclimate sensor data analysis method of claim 6, wherein the obtaining the corresponding integrated analysis result of the microclimate sensor based on the analysis result of numerical correlation in the correlation analysis result comprises:
determining a pre-recorded optional analysis conclusion corresponding to the analysis result of the numerical association in the association degree analysis result;
and combining the optional analysis results to obtain a comprehensive analysis result and displaying the comprehensive analysis result.
8. A multi-dimensional high-computing-force microclimate sensor data analysis system, comprising:
the data receiving module is configured to receive a plurality of sensor data with different dimensions reported by the microclimate sensor, and group the sensor data to obtain a plurality of group data, wherein each group data comprises sensor data with at least two dimensions;
the grouping analysis module is configured to perform joint analysis on each piece of grouping data to obtain a grouping analysis result;
and the comprehensive analysis module is configured to integrate the grouping analysis results to obtain comprehensive analysis results corresponding to the microclimate sensors.
9. A multi-dimensional high-computing-force microclimate sensor data analysis apparatus, the apparatus comprising: one or more processors; storage means for storing one or more programs that when executed by the one or more processors cause the one or more processors to implement the multi-dimensional high-power microclimate sensor data analysis method according to any one of claims 1-7.
10. A storage medium storing computer executable instructions which when executed by a computer processor are for performing the multi-dimensional high-power microclimate sensor data analysis method according to any one of claims 1 to 7.
CN202310713513.6A 2023-06-15 2023-06-15 Multi-dimensional high-calculation-force microclimate sensor data analysis system and method Pending CN116910117A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310713513.6A CN116910117A (en) 2023-06-15 2023-06-15 Multi-dimensional high-calculation-force microclimate sensor data analysis system and method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310713513.6A CN116910117A (en) 2023-06-15 2023-06-15 Multi-dimensional high-calculation-force microclimate sensor data analysis system and method

Publications (1)

Publication Number Publication Date
CN116910117A true CN116910117A (en) 2023-10-20

Family

ID=88361815

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310713513.6A Pending CN116910117A (en) 2023-06-15 2023-06-15 Multi-dimensional high-calculation-force microclimate sensor data analysis system and method

Country Status (1)

Country Link
CN (1) CN116910117A (en)

Similar Documents

Publication Publication Date Title
CN109144014B (en) System and method for detecting operation condition of industrial equipment
CN103901835B (en) It is monitored and controlled and stablize the equipment of cyclic process unit and method in factory's environment
CN111866031B (en) Equipment monitoring and analyzing method and device, server and storage medium
CN116129366A (en) Digital twinning-based park monitoring method and related device
KR20180108446A (en) System and method for management of ict infra
CN105116870A (en) Fault analysis method, device and system of air conditioning unit
CN112417141B (en) Domestic industrial control system curve data query processing method
CN115237079A (en) Intelligent control system and control method for equipment for chemical production
RU2654167C2 (en) Method for detecting hidden interactions on the internet of things
CN117518939A (en) Industrial control system based on big data
CN113611096A (en) Electric energy consumption monitoring and early warning system for air compression station
CN116910117A (en) Multi-dimensional high-calculation-force microclimate sensor data analysis system and method
CN116448234A (en) Power transformer running state voiceprint monitoring method and system
CN112115025B (en) Energy consumption determining method and device for distributed equipment
CN110971483B (en) Pressure testing method and device and computer system
CN114417741A (en) One-machine one-file equipment data management and processing system
JP7359608B2 (en) Information presentation device, information presentation method, and information presentation system
CN112612360A (en) Three-dimensional visualization equipment monitoring system and method based on VR technology
CN117522380B (en) Fault mechanism operation and maintenance period management system based on thermoelectric operation and maintenance
CN114817339B (en) Water quality monitoring method and system for multiple water areas, storage medium and electronic equipment
EP3113088A1 (en) Energy intensity variability analysis
CN117192278A (en) Parameter and environment variable-based fault prediction system and method
KR20230094560A (en) Data Processing System and Method for Real-Time Performance Analysis of Power Plant
CN117607620A (en) Cable monitoring system and method based on parameter change rate
CN117192279A (en) Cable monitoring system and method for reporting fault information

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination