CN113987013A - Management and control system and method based on time sequence data platform power generation vehicle data acquisition - Google Patents
Management and control system and method based on time sequence data platform power generation vehicle data acquisition Download PDFInfo
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Abstract
The invention discloses a management and control system and a method for power generation car data acquisition based on a time sequence data platform, wherein the system comprises a plurality of databases which are respectively constructed; the time sequence platform collects the environment, images and attribute information of a plurality of power generation cars simultaneously to form a data time sequence line, and then sends the data time sequence line to a visual analysis module of a client or a mobile terminal, and the visual analysis module can inquire multi-dimensional real-time data, historical data and prediction data of one or more power generation cars in real time in a multi-granularity mode. The time sequence-based data platform is used for storing various collected data of the generator car, the requirement of mass data storage in the digital industrial era can be met, and meanwhile, the data can be stored quickly, accurately and in a time sequence.
Description
The technical field is as follows:
the invention belongs to the field of time sequence data platform generator car data acquisition and control, and particularly relates to a time sequence data platform generator car data acquisition-based control system and method.
Background art:
in the digital industrial era, due to intensive and close connection of production processes, faults cause considerable loss to industrial production, and the equipment state can be judged in a classified manner through time sequence data collected by sensors, so that the faults are adjusted and eliminated in time. Due to the fact that the number of the sensors is large, the sensors are various and sample at high frequency, complexity of industrial big data is caused, and the method has the advantages of being high in space dimensionality, complex in dependency relationship, changeable in rule, large in data size and the like. These features present challenges to the industrial time series classification in terms of accuracy, efficiency and adaptivity.
At present, the storage and processing of time series big data are often processed in a relational database mode, but the relational database cannot be efficiently stored and queried due to the inherent disadvantage of the relational database. How to improve the time sequence data storage efficiency and the query efficiency becomes a technical problem to be solved urgently.
Disclosure of Invention
The method aims at the problems that the existing relational database cannot efficiently store and quickly query time sequence data and the management problem of the data of the generator car. The invention provides an accurate management and control system for power generation vehicle data acquisition based on a time sequence data platform, which comprises the following steps: the system comprises an electric car information account base, a gateway equipment information base, an image acquisition equipment information base and a multi-dimensional sensor equipment information base which are respectively connected with a time sequence platform, wherein the electric car information account base comprises a generator controller type sub-database and a controller type communication protocol command sub-database; the time sequence platform collects the environment, images and attribute information of a plurality of power generation cars simultaneously to form a data time sequence line, and then sends the data time sequence line to a visual analysis module of a client or a mobile terminal, and the visual analysis module can inquire multi-dimensional real-time data, historical data and prediction data of one or more power generation cars in real time in a multi-granularity mode. The time sequence data storage technology adopts a special data storage mode, greatly improves the processing capacity of time-related data, and greatly improves the query speed compared with a relational database in which the storage space is halved. The superior query performance of the time series function far exceeds that of a relational database, and the method is very suitable for analysis and application in the Internet of things.
The technical scheme adopted by the invention for solving the technical problems is as follows: the system comprises an electric car information account base, a gateway equipment information base, an image acquisition equipment information base and a multi-dimensional sensor equipment information base which are respectively connected with a time sequence platform, wherein the electric car information account base comprises a generator controller type sub-database and a controller type communication protocol command sub-database; the time sequence platform collects the environment, images and attribute information of a plurality of power generation cars simultaneously to form a data time sequence line, and then sends the data time sequence line to a visual analysis module of a client or a mobile terminal, and the visual analysis module can inquire multi-dimensional real-time data, historical data and prediction data of one or more power generation cars in real time in a multi-granularity mode.
Management and control system based on chronogenesis data platform power-generating cars data acquisition, this system includes: the system comprises an electric car information account database, a gateway equipment information database, an image acquisition equipment information database and a multidimensional sensor equipment information database, wherein the electric car information account database is respectively connected with a time sequence platform and comprises a generator controller type sub-database and a controller type communication protocol command sub-database; the time sequence platform collects the environment, images and attribute information of a plurality of power generation cars simultaneously to form a data time sequence line, and then sends the data time sequence line to a visual analysis module of a client or a mobile terminal, and the visual analysis module can inquire multi-dimensional real-time data, historical data and prediction data of one or more power generation cars in real time in a multi-granularity mode.
Further, the trolley bus information account database comprises interfaces for increasing, scrapping, modifying and inquiring vehicle information, and the generator controller type sub-database adopts a tree structure to store controller information and is stored in association with the controller type communication protocol command sub-database.
Further, the gateway device data information data is uniquely associated with the generator car in a one-to-one mode, and the gateway device data information data comprises a mac address.
Further, the time sequence platform simultaneously collects the environment and images of a plurality of power generation cars, the attribute information comprises collection intervals of 10 seconds, 5 seconds, 2 seconds, 1 second and 500 milliseconds, and the optimal parameters are set through the performance optimization model.
Furthermore, the visual analysis module can display real-time data, historical data and prediction data according to the time granularity selected by the user; the real-time data, the historical data, the predicted data may be displayed separately in the same graph in the same window at the same time granularity or in separate windows at different time granularities.
The control method based on the time sequence data platform generator car data acquisition comprises the following steps:
step 1, establishing a power generation car information ledger database by using a power generation car data item;
step 2, establishing a gateway equipment information database by using the gateway equipment data information; establishing an image acquisition equipment database by utilizing the information of the image acquisition equipment; establishing a multidimensional sensor equipment information database by using multidimensional sensor equipment information;
step 3, establishing an association relationship between a gateway and a multidimensional sensor, a generator car set and a camera according to a generator car information ledger database, a gateway equipment information database, an image acquisition equipment database and a multidimensional sensor equipment information database, wherein the gateway equipment information is associated and unique with a generator car ID;
step 4, based on the incidence relation between the generator car and the gateway and a communication protocol corresponding to the incidence control type, adopting different time granularities to sequentially carry out data acquisition tests on the unit equipment of the generator car, the multi-dimensional sensor of the generator car and the image acquisition equipment, and verifying the correctness and stability of the acquired data;
step 5, after the gateway verification, sending the acquired unit information, image information and environment information to a time sequence data platform through service according to an acquisition time sequence to guarantee the time sequence of the data, wherein the time sequence data platform obtains optimal configuration parameters according to the analysis of an optimization model, and configures the acquisition parameters according to the optimal configuration parameters; and simultaneously, after the time sequence data platform is authorized by the management platform, the latest data is transmitted to the real-time data service module through real-time service, and the real-time data service module pushes the latest data to the computer terminal or the mobile terminal.
And 6, establishing a visual system through the time sequence data platform and the real-time data service module, wherein the visual system can display real-time data, historical data, forecast data and corresponding data security level analysis, and can log in the visual system by using a computer terminal or a mobile terminal to perform data query with multiple time granularities.
Further, the power generation car information ledger database includes: a generator controller type library is established by utilizing the control type of the generator of each generator car; establishing a controller type communication protocol command library by utilizing the communication protocol of each controller; the power generation car information standing book database, the gateway equipment information database, the image acquisition equipment database and the multidimensional sensor equipment information database all comprise data interfaces, and the data interfaces can be newly added, scrapped, modified and inquired.
Further, the multi-dimensional sensor device comprises a GPS sensor, a temperature sensor, a humidity sensor and an inertia sensor.
Further, the time series data platform analyzing and obtaining the optimal configuration parameters according to the optimization model comprises:
s51, establishing multi-dimensional time sequence data,wherein d isi,jThe value of the jth sensor of the ith vehicle is represented, M represents the number of times of collection, and N represents the dimension of collection;
s52, constructing a performance prediction model according to the throughput, the average delay and the parameter characteristic value,
S=throughput(c1,c2...cN)
D=delay(c1,c2...cN)
P=w1×S+w2×D
w1+w2=1
wherein S represents a throughput prediction model, D represents an average delay prediction model, cNRepresenting a parameter characteristic value; p denotes a data transmission performance value, w1,w2Represents a weight value;
and S53, obtaining the optimal configuration parameters by using a genetic algorithm.
Further, the configuration parameters include a reading ratio of the time series data, a scanning ratio of the time series data, a memory occupancy rate, and a query index block value.
The invention has the following beneficial effects:
the system has the advantages that various collected data of the generator car are stored on the basis of the time sequence data platform, the requirement for mass data storage in the digital industrial era can be met, the stored data can be stored quickly, accurately and timely, accurate auxiliary reference and analysis of data are provided for real-time running conditions, historical analysis and analysis reports of the generator car, and the system has the advantages of being massive, quick, convenient, accurate and the like in data management and control.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood, the present invention may be implemented in accordance with the content of the description, and in order to make the above description and other objects, features, and advantages of the present invention more clearly understandable, preferred embodiments are specifically described below.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a flow chart of a management and control method for data acquisition of a time sequence data platform generator car
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be connected or detachably connected or integrated; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Example 1
Management and control system based on chronogenesis data platform power-generating cars data acquisition, this system includes: the system comprises an electric car information account database, a gateway equipment information database, an image acquisition equipment information database and a multidimensional sensor equipment information database, wherein the electric car information account database is respectively connected with a time sequence platform and comprises a generator controller type sub-database and a controller type communication protocol command sub-database; the time sequence platform collects the environment, images and attribute information of a plurality of power generation cars simultaneously to form a data time sequence line, and then sends the data time sequence line to a visual analysis module of a client or a mobile terminal, and the visual analysis module can inquire multi-dimensional real-time data, historical data and prediction data of one or more power generation cars in real time in a multi-granularity mode.
Further, the trolley bus information account database comprises interfaces for increasing, scrapping, modifying and inquiring vehicle information, and the generator controller type sub-database adopts a tree structure to store controller information and is stored in association with the controller type communication protocol command sub-database.
Further, the gateway device data information data is uniquely associated with the generator car in a one-to-one mode, and the gateway device data information data comprises a mac address.
Further, the time sequence platform simultaneously collects the environment and images of a plurality of power generation cars, the attribute information comprises collection intervals of 10 seconds, 5 seconds, 2 seconds, 1 second and 500 milliseconds, and the optimal parameters are set through the performance optimization model.
Furthermore, the visual analysis module can display real-time data, historical data and prediction data according to the time granularity selected by the user; the real-time data, the historical data, the predicted data may be displayed separately in the same graph in the same window at the same time granularity or in separate windows at different time granularities.
Example 2
The control method based on the time sequence data platform generator car data acquisition comprises the following steps:
step 1, establishing a power generation car information ledger database by using a power generation car data item;
step 2, establishing a gateway equipment information database by using the gateway equipment data information; establishing an image acquisition equipment database by utilizing the information of the image acquisition equipment; establishing a multidimensional sensor equipment information database by using multidimensional sensor equipment information;
step 3, establishing an association relationship between a gateway and a multidimensional sensor, a generator car set and a camera according to a generator car information ledger database, a gateway equipment information database, an image acquisition equipment database and a multidimensional sensor equipment information database, wherein the gateway equipment information is associated and unique with a generator car ID;
step 4, based on the incidence relation between the generator car and the gateway and a communication protocol corresponding to the incidence control type, adopting different time granularities to sequentially carry out data acquisition tests on the unit equipment of the generator car, the multi-dimensional sensor of the generator car and the image acquisition equipment, and verifying the correctness and stability of the acquired data;
step 5, after the gateway verification, sending the acquired unit information, image information and environment information to a time sequence data platform through service according to an acquisition time sequence to guarantee the time sequence of the data, wherein the time sequence data platform obtains optimal configuration parameters according to the analysis of an optimization model, and configures the acquisition parameters according to the optimal configuration parameters; and simultaneously, after the time sequence data platform is authorized by the management platform, the latest data is transmitted to the real-time data service module through real-time service, and the real-time data service module pushes the latest data to the computer terminal or the mobile terminal.
And 6, establishing a visual system through the time sequence data platform and the real-time data service module, wherein the visual system can display real-time data, historical data, forecast data and corresponding data security level analysis, and can log in the visual system by using a computer terminal or a mobile terminal to perform data query with multiple time granularities.
Further, the power generation car information ledger database includes: a generator controller type library is established by utilizing the control type of the generator of each generator car; establishing a controller type communication protocol command library by utilizing the communication protocol of each controller; the power generation car information standing book database, the gateway equipment information database, the image acquisition equipment database and the multidimensional sensor equipment information database all comprise data interfaces, and the data interfaces can be newly added, scrapped, modified and inquired.
Further, the multi-dimensional sensor device comprises a GPS sensor, a temperature sensor, a humidity sensor and an inertia sensor.
Further, the time series data platform analyzing and obtaining the optimal configuration parameters according to the optimization model comprises:
s51, establishing multi-dimensional time sequence data,wherein d isi,jThe value of the jth sensor of the ith vehicle is represented, M represents the number of times of collection, and N represents the dimension of collection;
s52, constructing a performance prediction model according to the throughput, the average delay and the parameter characteristic value,
S=throughput(c1,c2...cN)
D=delay(c1,c2...cN)
P=w1×S+w2×D
w1+w2=1
wherein S represents a throughput prediction model, D represents an average delay prediction model, cNRepresenting a parameter characteristic value; p denotes a data transmission performance value, w1,w2Represents a weight value;
further, the configuration parameters include a reading ratio of the time sequence data, a scanning ratio of the time sequence data, a memory occupancy rate, and a query index block value;
and S53, obtaining the optimal configuration parameters by using a genetic algorithm.
Further, the configuration parameter optimizing process includes: initial value of configuration parameter, throughput prediction model, average delay prediction model and w1,w2As the input of the genetic algorithm, calculating by using a data transmission performance model to obtain a corresponding fitness value of each configuration parameter; then, selecting, crossing and mutating the population to generate a next generation population; the selection operator adopts an elite reservation tournament strategy, the crossover operator adopts two-point crossover, and the mutation operator adopts real-value mutation.
Furthermore, the predicted data in the text type time sequence is obtained by adopting a convolution long-short term memory network model or a time sequence algorithm; the prediction data in the image type sequence is obtained by adopting a recurrent neural network model; the recurrent neural network model specifically comprises:
wherein x ═ { x ═ x0,x1,...,xM},xtDenotes the input at time t, M denotes the number of acquisitions, stIs the state function of the hidden layer at the time t, and f is an excitation function;
the time sequence algorithm model is as follows:
wherein N (mu, sigma)2) Is a obedient mean of mu and variance of sigma2The normal distribution of (c),φlfor each type of parameter of the model, yt-jFor acquiring sensor parameter values.
The invention has the following advantages:
the time sequence-based data platform is used for storing various acquired data of the generator car, the requirement of mass data storage in the digital industrial era can be met, the data can be stored quickly, accurately and time sequence, accurate auxiliary reference and analysis of the data are provided for real-time running conditions, historical analysis and analysis reports of the generator car, and the management and control of the data have the characteristics of mass, quickness, convenience, accuracy and the like; meanwhile, the data transmission of the time sequence platform is effectively optimized, and the throughput and the delay of the data can be improved; meanwhile, data prediction is carried out in multiple modes, and the data prediction effect is improved.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. The utility model provides a management and control system based on chronogenesis data platform power-generating cars data acquisition which characterized in that, the system includes: the system comprises an electric car information account database, a gateway equipment information database, an image acquisition equipment information database and a multidimensional sensor equipment information database, wherein the electric car information account database is respectively connected with a time sequence platform and comprises a generator controller type sub-database and a controller type communication protocol command sub-database; the time sequence platform collects the environment, images and attribute information of a plurality of power generation cars simultaneously to form a data time sequence line, and then sends the data time sequence line to a visual analysis module of a client or a mobile terminal, and the visual analysis module can inquire multi-dimensional real-time data, historical data and prediction data of one or more power generation cars in real time in a multi-granularity mode.
2. The management and control system for data acquisition of the power generation car based on the time sequence data platform as claimed in claim 1, wherein: the electric car information standing book database comprises a data interface for vehicle information increase, scrap, modification and query, and the generator controller type sub-database adopts a tree structure to store controller information and is stored in association with the controller type communication protocol command sub-database.
3. The management and control system for data acquisition of the power generation car based on the time sequence data platform as claimed in claim 1, wherein: the gateway equipment data information data is uniquely associated with the generator car in a one-to-one mode, and the gateway equipment data information data comprises a mac address.
4. The management and control system for data acquisition of the power generation car based on the time sequence data platform as claimed in claim 1, wherein: the time sequence platform simultaneously collects the environment, images and attribute information of a plurality of generator cars, the collection intervals of 10 seconds, 5 seconds, 2 seconds, 1 second and 500 milliseconds are set, and optimal parameters are set through a performance optimization model.
5. The management and control system for data acquisition of the power generation car based on the time sequence data platform as claimed in claim 1, wherein: the visual analysis module can display real-time data, historical data and prediction data according to the time granularity selected by the user; the real-time data, the historical data, the predicted data may be displayed separately in the same graph in the same window at the same time granularity or in separate windows at different time granularities.
6. A management and control method based on time sequence data platform generator car data acquisition is characterized in that: the method comprises the following steps:
step 1, establishing a power generation car information ledger database by using a power generation car data item;
step 2, establishing a gateway equipment information database by using the gateway equipment data information; establishing an image acquisition equipment database by utilizing the information of the image acquisition equipment; establishing a multidimensional sensor equipment information database by using multidimensional sensor equipment information;
step 3, establishing an association relationship between a gateway and a multidimensional sensor, a generator car set and a camera according to a generator car information ledger database, a gateway equipment information database, an image acquisition equipment database and a multidimensional sensor equipment information database, wherein the gateway equipment information is associated and unique with a generator car ID;
step 4, based on the incidence relation between the generator car and the gateway and a communication protocol corresponding to the incidence control type, adopting different time granularities to sequentially carry out data acquisition tests on the unit equipment of the generator car, the multi-dimensional sensor of the generator car and the image acquisition equipment, and verifying the correctness and stability of the acquired data;
step 5, after the gateway verification, sending the acquired unit information, image information and environment information to a time sequence data platform through service according to an acquisition time sequence to guarantee the time sequence of the data, wherein the time sequence data platform obtains optimal configuration parameters according to the analysis of an optimization model, and configures the acquisition parameters according to the optimal configuration parameters; and simultaneously, after the time sequence data platform is authorized by the management platform, the latest data is transmitted to the real-time data service module through real-time service, and the real-time data service module pushes the latest data to the computer terminal or the mobile terminal.
And 6, establishing a visual system through the time sequence data platform and the real-time data service module, wherein the visual system can display real-time data, historical data, forecast data and corresponding data security level analysis, and can log in the visual system by using a computer terminal or a mobile terminal to perform data query with multiple time granularities.
7. The time-series data platform-based power generation vehicle data acquisition control method according to claim 6, characterized in that: the power generation car information ledger database comprises: a generator controller type library is established by utilizing the control type of the generator of each generator car; establishing a controller type communication protocol command library by utilizing the communication protocol of each controller; the power generation car information standing book database, the gateway equipment information database, the image acquisition equipment database and the multidimensional sensor equipment information database all comprise data interfaces, and the data interfaces can be newly added, scrapped, modified and inquired.
8. The time-series data platform-based power generation vehicle data acquisition control method according to claim 6, characterized in that: the multi-dimensional sensor device comprises a GPS sensor, a temperature sensor, a humidity sensor and an inertia sensor.
9. The time-series data platform-based power generation vehicle data acquisition control method according to claim 6, characterized in that: the time sequence data platform obtains the optimal configuration parameters according to the optimization model analysis, and the method comprises the following steps:
s51, establishing multi-dimensional time sequence data,wherein d isi,jThe value of the jth sensor of the ith vehicle is represented, M represents the number of times of collection, and N represents the dimension of collection;
s52, constructing a performance prediction model according to the throughput, the average delay and the parameter characteristic value,
S=throughput(c1,c2...cN)
D=delay(c1,c2...cN)
P=w1×S+w2×D
w1+w2=1
wherein S represents a throughput prediction model, D represents an average delay prediction model, cNRepresenting a parameter characteristic value; p denotes a data transmission performance value, w1,w2Represents a weight value;
and S53, obtaining the optimal configuration parameters by using a genetic algorithm.
10. The time-series data platform-based power generation vehicle data acquisition control method according to claim 9, characterized in that: the configuration parameters comprise the reading proportion of the time sequence data, the scanning proportion of the time sequence data, the memory occupancy rate and the numerical value of the query index block.
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Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN202144257U (en) * | 2011-06-07 | 2012-02-15 | 北京清网华科技有限公司 | Passenger train inspection operation management and midway vehicle malfunction remote diagnosis system |
CN104091439A (en) * | 2014-06-11 | 2014-10-08 | 中国科学技术大学苏州研究院 | Area vehicle management control system based on vehicle network |
CN105721343A (en) * | 2016-03-29 | 2016-06-29 | 联想(北京)有限公司 | Network resource allocation method and network controller |
CN110793576A (en) * | 2019-11-27 | 2020-02-14 | 广州供电局有限公司 | Monitoring device and system for medium-low voltage generator car in distribution network |
-
2021
- 2021-10-20 CN CN202111219571.0A patent/CN113987013A/en active Pending
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN202144257U (en) * | 2011-06-07 | 2012-02-15 | 北京清网华科技有限公司 | Passenger train inspection operation management and midway vehicle malfunction remote diagnosis system |
CN104091439A (en) * | 2014-06-11 | 2014-10-08 | 中国科学技术大学苏州研究院 | Area vehicle management control system based on vehicle network |
CN105721343A (en) * | 2016-03-29 | 2016-06-29 | 联想(北京)有限公司 | Network resource allocation method and network controller |
CN110793576A (en) * | 2019-11-27 | 2020-02-14 | 广州供电局有限公司 | Monitoring device and system for medium-low voltage generator car in distribution network |
Non-Patent Citations (5)
Title |
---|
张洁;高亮;秦威;吕佑龙;李新宇;: "大数据驱动的智能车间运行分析与决策方法体系", 计算机集成制造系统, no. 05, 15 May 2016 (2016-05-15), pages 1220 - 1227 * |
徐江峰等: ""基于机器学习的HBase配置参数优化研究"", 《计算机科学》, vol. 47, no. 1, 15 June 2016 (2016-06-15), pages 474 - 479 * |
王焕涛等: ""基于时序数据库的氢能源动力监测系统的设计"", 《电子技术与软件工程》, no. 16, 15 August 2021 (2021-08-15), pages 174 - 175 * |
申建利;李纪欣;袁兵;: "空调发电车发电机机组实时监控系统", 铁路计算机应用, no. 12, 28 December 2013 (2013-12-28), pages 34 - 38 * |
蒋文燕: ""有轨电车综合监控系统设计与实现"", 《中国优秀硕士论文全文数据库(电子期刊)》, 15 September 2018 (2018-09-15), pages 19 - 59 * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN117829381A (en) * | 2024-03-05 | 2024-04-05 | 成都农业科技职业学院 | Agricultural greenhouse data optimization acquisition system based on Internet of things |
CN117829381B (en) * | 2024-03-05 | 2024-05-14 | 成都农业科技职业学院 | Agricultural greenhouse data optimization acquisition system based on Internet of things |
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