WO2019104950A1 - 一种面向园区的物联网大数据管理和应用平台 - Google Patents

一种面向园区的物联网大数据管理和应用平台 Download PDF

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WO2019104950A1
WO2019104950A1 PCT/CN2018/087226 CN2018087226W WO2019104950A1 WO 2019104950 A1 WO2019104950 A1 WO 2019104950A1 CN 2018087226 W CN2018087226 W CN 2018087226W WO 2019104950 A1 WO2019104950 A1 WO 2019104950A1
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data
unit
real
time
module
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PCT/CN2018/087226
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French (fr)
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王坚
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特斯联（北京）科技有限公司
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass

Definitions

  • the invention relates to the field of Internet of things technology, in particular to an Internet of Things big data management and application platform for a campus.
  • Industrial science and technology parks bring together productivity factors such as land, manpower, energy, transportation, and materials to form agglomeration effects and radiation effects, and perform industrial production, logistics, and scientific research and development activities.
  • the industrial science and technology park generally collects a considerable number of enterprises and other units, and constitutes a densely populated, peripherally radiated and relatively independent urban space.
  • Industrial technology parks generally have a management agency responsible for the public affairs of the entire park. With the construction of information technology, management organizations have generally built a data platform covering the scope of industrial technology parks.
  • These data platforms can support the basic information such as business information, geographical location, registered vehicles, and personnel size of each unit in the park, and can provide assistance for the management of the park, but the data collected from the type, scope, fineness, real-time Sex and other aspects are far from meeting the requirements of current gridding, refinement, and digital management.
  • the present invention provides an Internet of Things big data management and application platform for a campus.
  • the invention is directed to a large-scale industrial science and technology park of a large scale, and provides a management platform for the park to provide an Internet of Things big data management and application platform, which is obtained by sensing and collecting big data through various types of IoT devices covered in the space of the park.
  • the data represents the distribution of traffic flow, energy consumption, air pollutant emissions, and discharge water pollution of various units within the park.
  • the platform uses big data analysis methods to calculate each unit in the park as the basic unit.
  • the invention improves the problem that the current large-scale industrial science and technology park management data is not high, the work efficiency is low, time-consuming and laborious, and the management cost is high.
  • An Internet of Things big data management and application platform for a campus comprising: a campus sensor unit, an Internet of Things unit, a real-time computing unit, a data relay unit, a storage unit, and an offline computing unit;
  • the campus sensor unit is a front-end data sensing and collecting WSN network distributed in the space of the campus, and is used for sensing real-time traffic state data, real-time human flow state data, real-time air quality data, and real-time water environment data in the campus;
  • the Internet of Things unit is configured to obtain real-time traffic state data, real-time flow state data, real-time air quality data, real-time water environment data from the campus sensor unit; and the IoT unit is remote from the park's remote water and electricity meter system
  • the data output interface obtains the resource usage data of each unit in the campus, and obtains energy consumption data from the data export interface of the infrastructure of the IoT building in the park; the IoT unit transmits the obtained data through the load balancing device. Giving a multi-way gateway to a data relay unit by a multi-way gateway;
  • the data relay unit is configured to acquire and cache the original data from the Internet of Things unit through the load balancing device and the gateway, and the original data includes the real-time traffic state data, real-time flow state data, real-time air quality data, real-time water environment data, and resource usage data.
  • the data transfer unit transfers the raw data to the real-time computing unit for analysis processing, and transfers the original data and the result data obtained and cached from the real-time computing unit to the storage unit for data storage;
  • the real-time computing unit is configured to obtain the original data from a data relay unit, and verify the original data according to a predefined data verification rule, and reject original data that is incomplete or does not meet the requirements of the verification rule, and is complete and conforms to the verification.
  • the original data required by the rules is converted into a unified data format; according to the original data after the unified data format, the flow distribution data, the traffic flow distribution data, the air state distribution data, the water state distribution data of each basic unit of the park are statistically determined; Correlation index of the above distribution data and the basic unit; according to the above distribution data and correlation index, combined with the energy consumption data and resource usage data of each basic unit, calculate the basic transportation, energy consumption and environment of each basic unit to the park Comprehensive impact degree; performing an early warning exceeding the allowable threshold according to the comprehensive influence degree, and registering an environmental state data record for each basic unit; the comprehensive influence degree and the early warning record and the environmental state data record are output data output by the real-time computing unit;
  • the storage unit is configured to save the original data transferred by the data relay unit and the result data of the real-time computing unit;
  • the offline computing unit is configured to perform report analysis and data mining according to data saved by the storage unit.
  • the data relay unit includes: an original data module, an influence data module, an alarm data module, and an environment status module; the original data module is configured to acquire and cache the original from the Internet of Things unit through the load balancing device and the gateway.
  • Data, and the above-mentioned original data buffered by itself is transmitted to the real-time computing unit, and analyzed by the real-time computing unit; the original data unit also sends the original data to the storage unit for data storage; the influence degree data module is used to obtain real-time data.
  • the comprehensive impact degree generated by the computing unit is configured to obtain an early warning record generated by the real-time computing unit;
  • the environmental state module is configured to obtain an environmental state data record generated by the real-time computing unit; and the impact degree data module, The alarm data module and the environmental status module are transferred from the data obtained by the real-time computing unit to the storage unit.
  • the data relay unit is bidirectionally connected with a data exchange interface, and the data exchange interface is used by the application layer to access the storage unit to acquire data; and the first port of the exchange interface is bidirectionally connected with a location distribution interface, and the location is available.
  • the distribution interface is used to query the circulation of personnel and vehicles in the park.
  • the second port of the data exchange interface is connected to the pollution monitoring interface in two directions.
  • the pollution monitoring interface can be used to query the pollution situation in the park.
  • the third port of the data exchange interface has two-way connection.
  • the alarm statistical interface can extract the data information of the alarm that triggers the platform platform, and the fourth port of the data exchange interface is connected to the management analysis interface in two directions, and the comprehensive influence degree of the basic unit in the big data of the campus platform can be extracted through the management analysis interface and the basic unit
  • the related data, the fifth port of the data exchange interface is bidirectionally connected with an instruction delivery interface, and the control interface can be issued to the campus platform through the instruction delivery interface.
  • the real-time computing unit includes a data cleaning module, a data parsing module, an alarm judging module, and a state processing module; the data cleaning module is configured to acquire the original data from a data relay unit, and pre-defining the original data.
  • the data validation rules are validated, the original data that is incomplete or does not meet the validation rules is rejected, and the original data that is complete and meets the validation rules is converted into a unified data format;
  • the data parsing module is for the standard data obtained from the data cleaning module, Statistically determine the flow distribution data, traffic flow distribution data, air state distribution data, and water state distribution data of each basic unit in the park; and then evaluate the correlation index between the above distribution data and the basic unit; according to the above distribution data and correlation index Combined with the energy consumption data and resource usage data of each basic unit, calculate the comprehensive influence degree of each basic unit on the traffic, energy consumption and environment of the park; the alarm judgment module performs the early warning exceeding the allowable threshold according to the comprehensive influence degree; Module for each Registration of the base unit of the combined effects of the real-time traffic and associated status data, the real-time status data flow, air quality data in real time, real-time data water environment, resource usage data, energy consumption data, environmental state data.
  • the data parsing module converts the real-time air quality data perceived by the air detecting sensor and the spatial position coordinate of the air detecting sensor according to the predetermined spatial range of the surrounding unit to the air state distribution data for each basic unit of the campus;
  • the spatial variation gradient of the air state distribution data in a plurality of predetermined directions, and then the spatial variation gradient of the air state distribution data is converted into the correlation index of the air state distribution data and the basic unit;
  • the real-time air quality data in the spatial range is counted
  • the average value, and the product of the average value and the correlation index is used as the factor of influence of the basic unit on the air quality.
  • the data parsing module converts the water body state according to the real-time water environment data sensed by the water environment sensor in the predetermined spatial range of the adjacent water body of the basic unit and the spatial position coordinate of the water environment sensor for each basic unit of the park.
  • Distributing data calculating the spatial variation gradient of each section of the water body state distribution data in the natural flow direction of the water body, converting the spatial variation gradient of the water body state distribution data into the water body state distribution data and the correlation index of the basic unit; the statistical space
  • the average of the real-time water environment data within the range, and the product of the average value and the correlation index is used as the influence factor of the basic unit on the water environment.
  • the data parsing module is configured, for each basic unit of the campus, according to the real-time flow state data or the real-time traffic state data sensed by the traffic monitor within a predetermined space range around the basic unit, and the spatial position coordinates of the traffic monitor.
  • Converted into human traffic distribution data or traffic flow distribution data calculate the human traffic distribution data or the spatial variation gradient of the traffic flow distribution data in each direction of the road to the basic unit in the traveling direction, and the traffic distribution data or the traffic flow distribution
  • the spatial variation gradient of the data is converted into the human flow distribution data or the correlation index of the traffic flow distribution data and the basic unit; the real-time human flow state data in the statistical space range or the average value of the real-time traffic state data, and the average value is The product of the correlation index is used as the influence factor of the basic unit on traffic.
  • the data analysis module converts each basic unit's influence factors on air quality, water environment, traffic, and energy consumption data and resource usage data of each basic unit into a comprehensive influence score; and each integrated influence The degree score is accumulated as the overall degree of influence of the basic unit.
  • the campus sensor unit specifically includes: a traffic monitor, an air detecting sensor, and a water environment sensor;
  • the traffic monitor includes a video surveillance camera, a traffic quantity sensor, and a human flow sensor; wherein the video surveillance camera can capture the park traffic and The monitoring image of the pedestrian road;
  • the traffic quantity sensor can use the ground-sensing coil buried in the road ground to measure the number of vehicles passing through the unit time;
  • the human flow sensor can be an infrared counter set in the sidewalk of the park, and the number of people passing through the unit time is measured.
  • the air detecting sensor comprises a temperature sensor, a humidity sensor, a photochemical smoke sensor and a particulate matter sensor, which can fully collect the air condition and quality data of the park;
  • the water environment sensor comprises a PH sensor, an ORP sensor, a turbidity sensor, a residual chlorine sensor and
  • the conductivity sensor can collect the water environment data of the park.
  • the storage unit includes HDFS, HBase, RDBMS, and Redis; the output end of the original data module in the data transfer unit is connected to the input end of the HDFS in the storage unit; and the output end and storage of the influence data module in the data transfer unit
  • the output end of the HBase in the unit is connected; the output end of the alarm data module in the data transfer unit is connected to the input end of the RDBMS in the storage unit; the output end of the environmental status module in the data transfer unit is connected to the input end of the Redis in the storage unit.
  • the invention provides a large data management and application platform for the Internet of Things oriented to the campus, and has the following beneficial effects: the large-scale data management and application platform for the Internet of Things oriented to the campus, which is utilized in a large-scale industrial science and technology park of a large scale.
  • Various types of monitors and sensors in the sensor unit of the park establish a widely-covered Internet of Things, and a park-level big data platform is established.
  • the park sensor unit is used to collect big data in the park area through the Internet of Things.
  • Provide analysis and customized services calculate the comprehensive impact of the traffic, energy consumption and environment on the park by using the collected traffic flow data and pollution emission data for each unit in the park to realize the intelligence of large industrial technology parks. Management, management efficiency, time and effort, and low management costs, is conducive to promotion and use.
  • FIG. 1 is a diagram of a control system for an Internet of Things big data management and application platform according to the present invention
  • FIG. 2 is a schematic diagram of types of sensors in a campus sensor unit of the present invention.
  • FIG. 3 is a schematic diagram of a sensor type of a traffic monitor of the present invention.
  • FIG. 4 is a schematic view showing the type of sensors of the air detecting sensor of the present invention.
  • FIG. 5 is a schematic view showing a sensor type of a water body environment sensor according to the present invention.
  • 6A-C are schematic diagrams showing the calculation of the influence degree of the basic unit of the campus according to the present invention.
  • the terms "connected”, “connected”, and the like shall be understood broadly, and may be, for example, electrically connected, and may be directly connected or indirectly connected through an intermediate medium, unless otherwise specifically defined and defined. It can be the internal communication of two elements or the interaction of two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood on a case-by-case basis.
  • the present invention provides an Internet of Things big data management and application platform for a campus, including: a campus sensor unit, an Internet of Things unit, a real-time computing unit, a data relay unit, a storage unit, and an offline computing unit.
  • the output end of the sensor unit of the campus is connected to the input end of the Internet of Things unit, and the output end of the IoT unit is connected to four gateways through the LVS/FS module. The output ends of the four gateways are connected to the input end of the original data module in the data transfer unit. connection.
  • the campus sensor unit is a front-end data sensing and collection WSN network distributed in the campus space. It is used to sense real-time traffic state data, real-time traffic state data, real-time air quality data, and real-time water environment data. As shown in FIG. 2 to FIG. 5, the campus sensor unit specifically includes a traffic monitor, an air detecting sensor, and a water environment sensor.
  • the traffic monitor includes a video surveillance camera, a traffic quantity sensor, and a human flow sensor; wherein the video surveillance camera can capture a surveillance image of the park vehicle and the pedestrian road; the traffic quantity sensor can use a ground-sensing coil buried in the road surface, and the unit time is measured.
  • the number of passing vehicles; the flow sensor can be an infrared counter set in the sidewalk of the park to measure the number of people passing through the unit time.
  • the air detection sensor includes a temperature sensor, a humidity sensor, a photochemical smoke sensor, and a particulate sensor to fully capture the park air status and quality data.
  • the water environment sensor includes a PH sensor, an ORP sensor, a turbidity sensor, a residual chlorine sensor, and a conductivity sensor, which can collect water environment data of the park.
  • the traffic monitor, the air detecting sensor and the water environment sensor are all connected to the IoT unit based on WSN communication means such as ZigBee, and the collected and perceived data are uploaded to the IoT unit in real time.
  • the Internet of Things unit is the data uploading interface of the ubiquitous objects of the entire park. All the devices connected to the Internet of Things in the campus can transmit the data collected, sensed or generated by the data to the wired or wireless communication link. Internet of Things unit.
  • the IoT unit can also be obtained from the data output interface of the remote water and electricity meter system of the park.
  • the data on the use of water, electricity, gas and other resources in each unit of the park; the energy consumption data can be obtained from the data export interface of the IoT infrastructure such as the intelligent lighting system and central air-conditioning system of each unit in the park.
  • the IoT unit After the IoT unit initially aggregates the above-mentioned big data, it sends the data to the gateway through the wireless/wired network through the LVS/F5 load balancing device.
  • the LVS/F5 load balancing device and the multi-channel gateway are the channels of the big data platform connected to the back end of the IoT of the front end of the system.
  • the platform side distributes the traffic evenly through soft load balancing (LVS) or hard load balancing (F5, etc.).
  • LVS soft load balancing
  • F5 hard load balancing
  • each gateway is a high-performance network access program based on netty.
  • the data access protocol is divided into two levels. At the communication level, TCP, UDP, HTTP, and WEBSOCKET are supported.
  • the protocol supports MQTT, JSON, SOAP and custom binary protocols at the data protocol level. Through the combination of these two levels, data access of any IoT terminal and any protocol can be easily realized.
  • the big data platform end of the system includes a data relay unit, a real-time computing unit, a storage unit, and an offline computing unit.
  • the entry of the data transfer unit is a raw data module, which is used to acquire and cache the original data from the IoT unit through the above LVS/F5 load balancing device and gateway, and the original data includes the real-time traffic state data, real-time flow state data, real-time air. Quality data, real-time water environment data, resource usage data, and energy consumption data.
  • the original data module transmits the above-mentioned original form of the Internet of Things big data buffered by itself to the real-time computing unit, and is analyzed and processed by the real-time computing unit; the original data unit also sends the original data to the storage unit for data storage.
  • the data relay unit further includes an influence degree data module, an alarm data module and an environment status module, wherein the influence degree data module, the alarm data module and the environment status module respectively obtain and cache the result data of the analysis processing from the real-time calculation unit, and then send the result data to the storage.
  • the unit performs data saving.
  • the real-time computing unit includes a data cleaning module, a data analysis module, an alarm determination module, and a status processing module.
  • the output end of the original data module in the data transfer unit is connected to the input end of the data cleaning module in the real-time computing unit; after the original data module receives the data from the gateway and completes the unpacking, the original form of the Internet of Things data packet is sent to the data transfer unit.
  • the data cleaning modules include a data cleaning module, a data analysis module, an alarm determination module, and a status processing module.
  • the data cleaning module verifies the original data according to the data validation rules predefined to meet the needs of the campus big data analysis; for data that is determined to be incomplete or does not conform to the rules, such as format errors, garbled characters, numerical values exceeding the limit, and data stability If the verification fails, the feedback is sent to the original data module, and the instruction regains the defective original data. If the original data that meets the requirements of the verification rule cannot be obtained through multiple feedbacks, the data cleaning module rejects the problematic ones. Raw data.
  • the data cleaning module further performs ETL (extraction, conversion, loading) processing on the original data, and converts the originalized data of the multi-sourced and isomerized data provided by the Internet of Things unit into Unified data format to generate standard data suitable for data parsing module to perform big data processing analysis.
  • ETL extraction, conversion, loading
  • the data analysis module implements a big data parsing method adapted to the management requirements of a large industrial technology park for the standard data obtained from the data cleaning module, and each unit in the park (for example, a certain building in the park, a certain block, a certain factory)
  • a basic unit statistically determine the flow distribution data, traffic flow distribution data, air state distribution data, and water body state distribution data of each basic unit; and further evaluate the correlation index between the above distribution data and the basic unit; according to the above distribution data
  • the correlation index combined with the energy consumption data and resource usage data of each basic unit, calculate the comprehensive impact of each basic unit on the park's traffic, energy consumption and environment.
  • the operation of the data parsing module includes the following aspects:
  • R UA represents the correlation index of the air state distribution data with the basic unit U
  • D K1-K6 to D K5-K10 represent the spatial variation gradient of the air state distribution data in each predetermined direction
  • ⁇ 1 to ⁇ 5 represent each
  • the weight value of the predetermined direction can adjust the weight value of each predetermined direction according to the real-time wind direction and the wind speed factor.
  • the R UA is proportional to the spatial variation gradient of the air state distribution data for each predetermined direction, indicating that the greater the spatial variation gradient, the stronger the correlation between the air quality in the present spatial range and the basic unit U.
  • the data analysis module counts the average value A of the real-time air quality data A K1 , A K2 , ... A K10 in the present spatial range, and uses R UA *A as the influence factor of the basic unit U on the air quality.
  • D S1-S2 represents the spatial variation gradient of the water body state distribution data on the predetermined section S1 to S2, dis(S1, S2) represents the spatial distance between the water body environment sensors S1, S2;
  • FIG. 6B shows the natural flow in the water body
  • the other spatial variation gradients D S2-S3 , D S3-S4 , D S4-S5 in the direction are calculated in the same way as D S1-S2 ; the data analysis module converts the spatial gradient of the water state distribution data into the water state.
  • the correlation index between the distribution data and the basic unit U is as follows:
  • R UW represents the correlation index of the water body state distribution data and the basic unit U
  • ⁇ 1 to ⁇ 4 represent the weight values of each predetermined section
  • each predetermined section can be adjusted according to the real-time water flow speed and the water flow factor.
  • the R UW is proportional to the spatial variation gradient of the water body state distribution data of each predetermined section, indicating that the greater the spatial variation gradient, the stronger the correlation between the water body environment data and the basic unit U.
  • W S1 statistical spatial extent
  • W S2, ... W S5 average value W, and R UW * W as a basic unit U of the present degree of influence on the water environment factor.
  • U represents a basic unit of the park
  • J1-J5 represents a traffic monitor within a predetermined spatial range around the basic unit U (for example, within a distance range of 5 KM centered on U); data analysis
  • the module obtains real-time flow state data from these traffic monitors J1-J5 or real-time traffic state data T J1 , T J2 , ... T J5 , real-time flow state data or real-time traffic state data is standard data after data cleaning;
  • the data parsing module converts the real-time human flow state data or the real-time traffic state data according to the obtained real-time human flow state data or the real-time traffic state data T J1 , T J2 , ...
  • FIG. 6C shows the human traffic distribution data or the traffic flow distribution data in a graphical form; further, the data analysis module calculates that the above distributed data points to the basic unit U in the traveling direction.
  • the spatial variation gradient of each section of the road is as shown in Figure 6C where J4 is a fork in which only the sections of J4 to J5 are Pointing into the direction of the base unit U, and therefore only the calculation of the spatial gradient of the segment:
  • D J4-J5 represents the human flow distribution data or the spatial variation gradient of the traffic flow distribution data on the predetermined section of J4 to J5, and dis(J4, J5) represents the spatial distance between the traffic monitors J4 and J5;
  • the calculation method of the other individual segment spatial variation gradients D J1-J3 , D J2-J3 , D J3-J4 in the direction in which the traveling direction is directed to the basic unit U is the same as D J4-J5 ; the data analysis module will The human flow distribution data or the spatial variation gradient of the traffic flow distribution data is converted into the human traffic distribution data or the correlation index of the traffic flow distribution data and the basic unit U, as follows:
  • R UT ⁇ 1 D J1-J3 + ⁇ 2 D J2-J3 + ⁇ 3 D J3-J4 + ⁇ 4 D J4-J5
  • R UT represents the human flow distribution data or the correlation index of the traffic flow distribution data and the basic unit U
  • ⁇ 1 to ⁇ 4 represent the weight values of each predetermined section, which may be based on the space between the sections and U The distance is adjusted by the weight value, and the closer the segment distance U is, the larger the weight value is.
  • the R UT is proportional to the spatial distribution variation data of each predetermined section or the spatial variation gradient of the traffic flow distribution data, indicating that the larger the spatial variation gradient, the traffic flow distribution data or the traffic flow distribution data and the basic unit U The stronger the correlation.
  • the data analysis module counts the real-time human flow state data in the spatial range or the average value T of the real-time traffic state data T J1 , T J2 , . . . , T J5 , and uses R UT *T as the influence of the basic unit U on the traffic. Factor.
  • the data analysis module converts the influence factor of each basic unit U on the air quality, the water environment, the human flow, or the vehicle flow into a comprehensive influence score; for example, dividing a threshold interval for the traffic influence factor, each threshold The interval corresponds to a comprehensive influence degree score.
  • the threshold interval score with the largest influence degree factor corresponds to 100 points, and the threshold interval corresponding to the least influence degree corresponds to 20 points; according to which threshold interval the influence factor R UT *T of the basic unit U is in In the middle, the influence factor of the basic unit U on the traffic is converted into the comprehensive influence score corresponding to the threshold interval.
  • the energy consumption data and resource consumption data of each basic unit are also converted into comprehensive influence scores, and the comprehensive influence scores of all aspects are accumulated, and the comprehensive influence degree of each basic unit on the traffic, energy consumption and environment of the park is calculated.
  • the data parsing module obtains the comprehensive influence degree for each basic unit U in the campus; and provides the comprehensive influence degree to the alarm judging module and the state processing module of the real-time computing unit.
  • the alarm status module performs an early warning based on the existing rules when the comprehensive influence of a unit exceeds the allowable threshold of the existing rules.
  • the state processing module registers the comprehensive influence degree and related real-time traffic flow state data, real-time flow state data, real-time air quality data, real-time water environment data, resource usage data, energy consumption data and other state data for each basic unit U in the campus. Establish an environmental status data record indexed by each basic unit U.
  • Each module of the real-time computing unit selects the Storm streaming big data processing architecture. The real-time performance of the Storm architecture is good. In the scenario of the Internet of Things, global grouping of terminal data can be supported, and the cleaning and parsing of the Internet of Things data can be easily realized. Real-time processing such as alarms.
  • the data analysis module of the real-time computing unit is connected with the influence degree data module in the data transfer unit, and the alarm judgment module in the real-time calculation unit is connected with the input end of the alarm data module, and the state processing module and the data transfer unit internal environment in the real-time calculation unit
  • the status module is connected.
  • the data relay unit obtains the result data of the real-time big data analysis from the real-time computing unit, including the influence degree factor and the comprehensive influence degree generated by the data parsing module, the warning record generated by the alarm judging module, the state record of the state processing module, and Transfer these data to the storage unit as a permanent park IoT big data for storage.
  • the storage unit includes HDFS, HBase, RDBMS and Redis.
  • HDFS is very suitable for the storage of unstructured data. It supports data backup, recovery and migration. It is mainly used to store raw data and data that needs to be analyzed offline.
  • HBase is suitable. In the storage of semi-structured data, it can well support the query of historical data of massive IoT terminals. In the system, it is mainly used to store data such as historical trajectories and states of the terminal, and the raw data in the data transfer unit.
  • the output end of the module is connected to the input end of the HDFS in the storage unit, and the output end of the influence data module in the data transfer unit is connected to the output end of the HBase in the storage unit.
  • the output of the alarm data module in the data transfer unit is connected to the input end of the RDBMS in the storage unit.
  • the RDBMS is suitable for storing structured data, and usually adopts different high-availability deployment schemes according to a specific database, and is mainly used for storing terminals in the system.
  • Basic data, dictionary data and data analysis results, etc. the output of the environment status module in the data transfer unit is connected with the input of the Redis in the storage unit.
  • Redis is a memory-based KV database, which is usually used in the system for caching needs to be updated frequently. And access to data, such as the current state of the Internet of Things terminals, Redis provides good embedded support for a variety of data types and a variety of data operations.
  • the offline computing unit includes a report analysis module and a big data mining module.
  • the output end of the HDFS in the storage unit is connected to the first input end of the offline computing unit, and the output end of the HBase in the storage unit is connected to the second input end of the offline computing unit, and is offline.
  • the computing unit supports MapReduce and Hive, which is mainly used for report analysis and data mining of IOT data in multiple time dimensions such as day/week/month/year, and outputs the result to the relational database, the first output of the offline computing unit.
  • the end is connected to the second input end of the RDBMS in the storage unit, and the second output end of the offline computing unit is connected to the second input end of the Redis in the storage unit.
  • the data relay unit has a data exchange interface in two-way connection.
  • the data exchange interface mainly abstracts a layer of access interface to simplify data access between the application layer and the platform layer. With this layer interface, the application layer does not need to directly call HDFS. Native APIs such as HBase can quickly access the storage unit to obtain data for application development.
  • the data exchange interface supports: SQL, Restful, Thrift and Java API. The data exchange method can be flexibly selected according to the actual situation.
  • the content of data exchange includes: Internet of Things
  • the first port of the data exchange interface is bidirectionally connected with a location distribution interface, and the location distribution interface can be used to query the personnel and vehicles in the park.
  • the second port of the data exchange interface is connected to the pollution monitoring interface in two directions.
  • the pollution monitoring interface can be used to query the pollution situation in the park.
  • the third port of the data exchange interface has a two-way connection with an alarm statistical interface, which can be triggered to extract the platform.
  • the fourth port of the interface is bidirectionally connected with a management analysis interface, and the comprehensive influence degree of the basic unit in the big data of the campus platform and the related data of the basic unit can be extracted through the management analysis interface, and the fifth port of the data exchange interface is bidirectionally connected with instructions.
  • the interface can issue control commands to the campus platform through the command delivery interface, and can access the processed data of the campus platform through the data exchange interface.
  • Each sensor device in the sensor unit of the park sends data to the gateway through the LVS/F5 load balancing through the LVS/F5 load balancing of the Internet of Things.
  • the gateway After receiving the data, the gateway stores the data in the original data module of the data relay unit, and the original data.
  • the module sends the data cleaning module to the real-time computing unit, and the original data module sends the data to the HDFS in the storage unit for data storage.
  • the data cleaning module receives the data information sent by the original data module, the data cleaning module retransmits the data to the data cleaning module.
  • the data analysis module analyzes the comprehensive influence degree by the data analysis module, and then sends the analyzed comprehensive influence degree data to the alarm judgment module in the real-time calculation unit and the influence degree data module in the data transfer unit, and the influence degree data module
  • the data is transmitted to the HBase in the storage unit for data storage, and the alarm judging module performs an early warning according to the existing rules, and sends the generated result to the alarm data module in the data transfer unit, and then the alarm data module forwards the data to the storage unit.
  • the fault module sends the parsed data to the state processing module, and the state processing module forwards the data to the environment state module in the data relay unit, and the state processing module analyzes the current state of each sensor parameter data, and if the state changes, the environment
  • the status module sends data to the Redis in the storage unit for data storage. Redis asynchronously imports the received data into HBase and HDFS, and the offline computing unit periodically reads data from HDFS for various report analysis and data mining.
  • the user service platform and the management platform can access the TIZA STAR platform data through the data exchange interface.
  • the park-oriented IoT big data management and application platform is aimed at large-scale industrial parks of large scale, and uses the various types of monitors and sensors of the sensor units in the park to establish a wide coverage.
  • the Internet of Things has established a park-level big data platform, using the campus sensor unit to collect large data within the park through the Internet of Things, providing analysis and customized services on the basis of big data, and utilizing the collected data for each unit in the park.
  • Traffic flow data, pollution discharge data, etc. calculate its comprehensive impact on the park's traffic, energy consumption and environment, realize intelligent management of large industrial science and technology parks, high management efficiency, save time and effort, and low management costs, Conducive to promotion and use.

Abstract

本发明公开了一种面向园区的物联网大数据管理和应用平台，涉及物联网技术领域。该面向园区的物联网大数据管理和应用平台，包括园区传感器单元、物联网单元、实时计算单元、数据中转单元、储存单元和离线计算单元。本发明建立了园区级大数据平台，利用园区传感器单元通过物联网来采集园区范围内的大数据，在大数据基础上提供分析和定制化服务，针对园区内每一个单位，计算它对园区交通、能耗和环境的综合影响度，实现对大型工业科技园区的智能化管理，管理工作效率高，省时省力，且管理成本较低。

Description

一种面向园区的物联网大数据管理和应用平台 技术领域
本发明涉及物联网技术领域，具体为一种面向园区的物联网大数据管理和应用平台。
背景技术
大型的工业科技园区将土地、人力、能源、交通、物资等生产力要素聚集在一起，形成集聚效应和辐射效应，执行工业生产、物流运输、科技研发等活动。工业科技园区内一般汇集了相当数量的企业等单位，构成了中心密集、周边辐射而又相对独立的城市空间。工业科技园区一般都具有负责整个园区公共事务的管理机构。随着信息化的建设，管理机构普遍都搭建了覆盖工业科技园区范围的数据平台。这些数据平台可以支持对园区内各个单位的工商信息、地理位置、登记车辆、人员规模等基本信息予以查询，能够为园区的管理提供辅助，但是所汇聚的数据从类型、范围、精细度、实时性等方面都远远无法满足当前网格化、精细化、数字化管理的要求。
当前，万物互联的时代正逐步到来，据权威报告预测，2020年全球物联网连接的终端数将达到500亿。随着物联网终端的数量激增以及分布的泛在化，物联网大数据也呈现爆发式增长。物联网的大数据中蕴含的价值是非常高的。具体到人类生存空间的维持与管理来说，通过汇聚、分析应用来自物联网的大数据，能够对环境、交通、能耗、人流等因素的管理应用提供量化指标和依据。
但是，目前基于物联网的大数据平台一般均为单一类型数据平台，或者是在家庭、单一建筑等小规模空间范围内的数据平台。针对规模化的大型工业科技园区，现有技术中没有面向园区的物联网大数据管理和应用平台。特别是，在大数据平台的后端，不能适应园区综合管理的需要，将园区范围内各个单位作为一个基本单元，采集数据并分析每个基本单元的人流车流量分布、能耗状况、空气污染物排放情况、排放水体的污染情况等。在没有相应的物联网大数据管理和应用平台下，园区的管理机构也无法以量化数据为依托来提供定制化服务。管理机构也无法计算各个单位的人流车流量分布、污染物排放、排放水体的污染等对园区交通、能耗和环境的综合影响度，管理起来工作效率较低，费时费力，管理成本较高。
发明内容
(一)解决的技术问题
针对现有技术的不足，本发明提供了一种面向园区的物联网大数据管理和应用平台。本发明针对规模化的大型工业科技园区，为园区的管理机构提供了一种物联网大数据管理和应用平台，通过在园区空间范围内覆盖的多类型物联网设备感知和采集大数据，获得的数据表示园区范围内各个单位的人流车流量分布、能耗状况、空气污染物排放情况、排放水体污染情况等，进 而本平台利用大数据分析手段，以园区内的每个单位作为基本单元，计算每个基本单元的人流车流量分布、能耗、污染物排放、排放水体污染等对园区交通、能耗和环境的综合影响度；进而，基于分析结论提供定制化服务。本发明改善了当前大型工业科技园区管理数据化程度不高、工作效率较低，费时费力，管理成本较高的问题。
(二)技术方案
为实现以上目的，本发明通过以下技术方案予以实现：
一种面向园区的物联网大数据管理和应用平台，其特征在于，包括：园区传感器单元、物联网单元、实时计算单元、数据中转单元、储存单元和离线计算单元；
所述园区传感器单元是分布在园区空间范围的前端数据感知与采集WSN网络，用于感知园区内的实时车流状态数据、实时人流状态数据、实时空气质量数据、实时水体环境数据；
所述物联网单元用于从所述园区传感器单元获得实时车流状态数据、实时人流状态数据、实时空气质量数据、实时水体环境数据；并且，所述物联网单元从园区的远程水电气表系统的数据输出接口获取园区各个单位的资源用量数据，从园区各个单位建筑的接入物联网的基础设施的数据导出接口获得其能耗数据；所述物联网单元通过负载均衡设备将所获得的数据传送给多路网关，再由多路网关传输给数据中转单元；
所述数据中转单元用于通过负载均衡设备和网关从物联网单元获取并缓存原始数据，原始数据包括上述实时车流状态数据、实时人流状态数据、实时空气质量数据、实时水体环境数据、资源用量数据、能耗数据；所述数据中转单元将原始数据中转到实时计算单元进行分析处理，并且将所述原始数据以及从实时计算单元获取并缓存的结果数据中转到储存单元进行数据保存；
所述实时计算单元用于从数据中转单元获取所述原始数据，并且对原始数据按照预定义的数据验证规则进行验证，剔除不完整或者不符合验证规则要求的原始数据，并且将完整且符合验证规则要求的原始数据转换为统一数据格式；根据统一数据格式后的原始数据，统计确定园区每个基本单元的人流量分布数据、车流量分布数据、空气状态分布数据、水体状态分布数据；进而评估以上分布数据与该基本单元的相关性指数；根据以上分布数据和相关性指数，再结合每个基本单元的能耗数据和资源用量数据，计算每个基本单元对园区交通、能耗和环境的综合影响度；根据综合影响度进行超出允许阈值的预警，以及为每个基本单元登记环境状态数据记录；所述综合影响度以及预警记录、环境状态数据记录作为实时计算单元输出的结果数据；
所述储存单元用于对由所述数据中转单元中转的原始数据以及实时计算单元的结果数据进行保存；
所述离线计算单元用于根据所述储存单元所保存的数据进行报表分析和数据挖掘。
优选的是，所述数据中转单元包括：原始数据模块、影响度数据模块、 报警数据模块、环境状态模块；所述原始数据模块用于通过上述负载均衡设备和网关从物联网单元获取并缓存原始数据，并将自身缓存的上述原始数据传输给实时计算单元，由实时计算单元进行分析处理；原始数据单元也将上述原始数据发送给储存单元进行数据保存；所述影响度数据模块用于获得实时计算单元产生的综合影响度；所述报警数据模块用于获得实时计算单元产生的预警记录；所述环境状态模块用于获得实时计算单元产生的环境状态数据记录；并且所述影响度数据模块、报警数据模块、环境状态模块将从实时计算单元所获得的数据中转到储存单元。
优选的是，所述数据中转单元双向连接有数据交换接口，数据交换接口用于应用层访问储存单元获取数据；并且，所述据交换接口的第一端口双向连接有位置分布接口，可通过位置分布接口来查询园区内人员以及车辆的流通情况，数据交换接口的第二端口双向连接有污染监控接口，可通过污染监控接口来查询园区内的污染情况，数据交换接口的第三端口双向连接有报警统计接口，可通过提取触发园区平台报警的数据信息，数据交换接口的第四端口双向连接有管理分析接口，可通过管理分析接口提取园区平台大数据中基本单元的综合影响度及该基本单元的相关数据，数据交换接口的第五端口双向连接有指令下发接口，可通过指令下发接口对园区平台发出控制指令。
优选的是，所述实时计算单元包括数据清洗模块、数据解析模块、报警判断模块和状态处理模块；所述数据清洗模块用于从数据中转单元获取所述原始数据，并且对原始数据按照预定义的数据验证规则进行验证，剔除不完整或者不符合验证规则要求的原始数据，并且将完整且符合验证规则要求的原始数据转换为统一数据格式；数据解析模块针对从数据清洗模块获得的标准数据，统计确定园区每个基本单元的人流量分布数据、车流量分布数据、空气状态分布数据、水体状态分布数据；进而评估以上分布数据与该基本单元的相关性指数；根据以上分布数据和相关性指数，再结合每个基本单元的能耗数据和资源用量数据，计算每个基本单元对园区交通、能耗和环境的综合影响度；报警判断模块根据综合影响度进行超出允许阈值的预警；状态处理模块为每个基本单元登记所述综合影响度以及相关的实时车流状态数据、实时人流状态数据、实时空气质量数据、实时水体环境数据、资源用量数据、能耗数据等环境状态数据。
优选的是，数据解析模块针对园区的每个基本单元，根据该基本单元周边预定空间范围的空气检测传感器感知的实时空气质量数据以及空气检测传感器的空间位置坐标，转化为空气状态分布数据；计算空气状态分布数据在多个预定方向上的空间变化梯度，再将空气状态分布数据的空间变化梯度转化为空气状态分布数据与该基本单元的相关度指数；统计本空间范围内的实时空气质量数据的平均值，并且将该平均值与相关度指数的乘积作为该基本单元对空气质量的影响度因数。
优选的是，数据解析模块针对园区的每个基本单元，根据该基本单元相邻水体的预定空间范围内的水体环境传感器感知的实时水体环境数据以及水 体环境传感器的空间位置坐标，转化为水体状态分布数据；计算水体状态分布数据在水体自然流动方向上的各个区段的空间变化梯度，将水体状态分布数据的空间变化梯度转化为水体状态分布数据与该基本单元的相关度指数；统计本空间范围内的实时水体环境数据的平均值，并且将该平均值与相关度指数的乘积作为该基本单元对水体环境的影响度因数。
优选的是，数据解析模块针对园区的每个基本单元，根据该基本单元周边预定空间范围内的交通监控器感知的实时人流状态数据或者是实时车流状态数据，以及交通监控器的空间位置坐标，转化为人流量分布数据或者是车流量分布数据，计算人流量分布数据或者是车流量分布数据在行进方向指向该基本单元的道路各个区段的空间变化梯度，将人流量分布数据或者是车流量分布数据的空间变化梯度转化为人流量分布数据或者是车流量分布数据与该基本单元的相关度指数；统计空间范围内的实时人流状态数据或者是实时车流状态数据的平均值，并且将该平均值与相关度指数的乘积作为该基本单元对交通的影响度因数。
优选的是，数据解析模块将每个基本单元对空气质量、水体环境、交通的影响度因数以及每个基本单元的的能耗数据和资源用量数据换算为综合影响度得分；并且将各个综合影响度得分累加，作为该基本单元的综合影响度。
优选的是，所述园区传感器单元具体包括：交通监控器、空气检测传感器和水体环境传感器；交通监控器包括视频监视摄像机、车流数量传感器、人流量传感器；其中视频监视摄像机可以拍摄园区车行和人行道路的监视画面；车流数量传感器可以采用埋入公路地面的地感线圈，计量单位时间内的通过车辆数量；人流量传感器可以是设置在园区人行道的红外计数器，计量单位时间内的人员通行数量；所述空气检测传感器包括温度传感器、湿度传感器、光化学烟雾传感器和颗粒物传感器，可充分采集园区空气状态和质量数据；所述水体环境传感器包括PH传感器、ORP传感器、浊度传感器、余氯传感器和电导率传感器，可采集园区水体环境数据。
优选的是，所述储存单元包括HDFS、HBase、RDBMS和Redis；数据中转单元内原始数据模块的输出端与储存单元内HDFS的输入端连接；数据中转单元内影响度数据模块的输出端与储存单元内HBase的输出端连接；数据中转单元内报警数据模块的输出端与储存单元内RDBMS的输入端连接；数据中转单元内环境状态模块的输出端与储存单元内Redis的输入端连接。
(三)有益效果
本发明提供了一种面向园区的物联网大数据管理和应用平台，具备以下有益效果：该面向园区的物联网大数据管理和应用平台，针对规模化的大型工业科技园区，在园区范围内利用园区传感器单元的各种类型的监视器以及传感器等建立一个广泛覆盖的物联网，建立了园区级大数据平台，利用园区传感器单元通过物联网来采集园区范围内的大数据，在大数据基础上提供分析和定制化服务，针对园区内每一个单位，利用所采集的交通流量数据、污染排放数据等，计算它对园区交通、能耗和环境的综合影响度，实现对大型 工业科技园区的智能化管理，管理工作效率高，省时省力，且管理成本较低，利于推广使用。
附图说明
图1为本发明物联网大数据管理和应用平台控制系统图；
图2为本发明园区传感器单元内的传感器类型示意图；
图3为本发明交通监控器的传感器类型示意图；
图4为本发明空气检测传感器的传感器类型示意图；
图5为本发明水体环境传感器的传感器类型示意图；
图6A-C为本发明对园区基本单元影响度计算的示意图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。
在本发明中，除非另有明确的规定和限定，术语“相连”、“连接”、等术语应做广义理解，例如，可以是电连接；可以是直接相连，也可以通过中间媒介间接相连，可以是两个元件内部的连通或两个元件的相互作用关系。对于本领域的普通技术人员而言，可以根据具体情况理解上述术语在本发明中的具体含义。
请参阅图1，本发明提供一种面向园区的物联网大数据管理和应用平台，包括：园区传感器单元、物联网单元、实时计算单元、数据中转单元、储存单元和离线计算单元。
园区传感器单元的输出端与物联网单元的输入端连接，物联网单元的输出端通过LVS/FS模块连接有四个网关，四个网关的输出端均与数据中转单元内原始数据模块的输入端连接。园区传感器单元是分布在园区空间范围的前端数据感知与采集WSN网络，用于感知园区内的实时车流状态数据、实时人流状态数据、实时空气质量数据、实时水体环境数据。如图2-图5，园区传感器单元具体包括交通监控器、空气检测传感器和水体环境传感器。交通监控器包括视频监视摄像机、车流数量传感器、人流量传感器；其中视频监视摄像机可以拍摄园区车行和人行道路的监视画面；车流数量传感器可以采用埋入公路地面的地感线圈，计量单位时间内的通过车辆数量；人流量传感器可以是设置在园区人行道的红外计数器，计量单位时间内的人员通行数量。空气检测传感器包括温度传感器、湿度传感器、光化学烟雾传感器和颗粒物传感器，可充分采集园区空气状态和质量数据。水体环境传感器包括PH传感器、ORP传感器、浊度传感器、余氯传感器和电导率传感器，可采集园区水体环境数据。交通监控器、空气检测传感器和水体环境传感器均基于ZigBee等WSN通信手段连接所述物联网单元，并且将采集和感知的数据实时上传给该物联网单元。物联网单元是整个园区泛在化的物物互相的数据上传接口，园区 内所有接入物联网的设备均可以将其采集、感应或自身生成的数据通过有线或者无线的通信链路传输给该物联网单元。除了如上所述从园区传感器单元获得实时车流状态数据、实时人流状态数据、实时空气质量数据、实时水体环境数据以外，例如，物联网单元还可以从园区的远程水电气表系统的数据输出接口获取园区各个单位的用水、用电、用气等资源用量数据；可以从园区各个单位建筑的智能照明系统、中央空调系统等接入物联网的基础设施的数据导出接口获得其能耗数据。物联网单元初步汇聚以上原始形式的大数据之后，又通过无线/有线网络，经过LVS/F5负载均衡设备将数据发送至网关。LVS/F5负载均衡设备以及多路网关是大数据由本系统前端的物联网接入后端的大数据平台端的通道，平台端通过软负载均衡(LVS)或者硬负载均衡(F5等)将流量均匀的负载到各个可水平扩展的网关，每个网关都是基于netty实现的高性能的网络接入程序，数据接入协议分两个层次，在通讯层次上，支持TCP、UDP、HTTP和WEBSOCKET等通讯协议，在数据协议层次上，支持MQTT、JSON、SOAP和自定义二进制协议，通过这两个层次的互相搭配，可以轻松实现任何物联网终端、任何协议的数据接入。
本系统的大数据平台端包括数据中转单元、实时计算单元、储存单元、离线计算单元。
数据中转单元的入口是原始数据模块，该模块用于通过上述LVS/F5负载均衡设备和网关从物联网单元获取并缓存原始数据，原始数据包括上述实时车流状态数据、实时人流状态数据、实时空气质量数据、实时水体环境数据、资源用量数据、能耗数据。原始数据模块将自身缓存的上述原始形式的物联网大数据传输给实时计算单元，由实时计算单元进行分析处理；原始数据单元也将上述原始数据发送给储存单元进行数据保存。数据中转单元还包括影响度数据模块、报警数据模块和环境状态模块，所述影响度数据模块、报警数据模块和环境状态模块分别从实时计算单元获得并缓存分析处理的结果数据，然后发送给储存单元进行数据保存。
实时计算单元包括数据清洗模块、数据解析模块、报警判断模块和状态处理模块。数据中转单元内原始数据模块的输出端与实时计算单元内数据清洗模块的输入端连接；原始数据模块从网关接收到数据并完成解包之后，将原始形式的物联网数据包发送到数据中转单元的该数据清洗模块之中。数据清洗模块对原始数据按照为了适应园区大数据分析的需求而预定义的数据验证规则进行验证；对于被判定不完整或不符合规则的数据，例如格式错误、乱码、数值超出限值、数据稳定性验证不达标等，向原始数据模块发送反馈，指令重新获得这些有缺陷的原始数据，如果经多次反馈仍然不能获得完整且符合验证规则要求的原始数据，则数据清洗模块剔除这些有问题的原始数据。对于经验证完整且符合规则要求的原始数据，数据清洗模块进而对这些原始数据进行ETL(抽取、转换、加载)处理，由物联网单元提供的本身多源化、异构化的原始数据转换为统一数据格式，生成适于数据解析模块执行大数据处理分析的标准数据。数据解析模块针对从数据清洗模块获得的标准数据， 执行适应大型工业科技园区管理需求的大数据解析方法，以园区内的每个单位(例如园区的某个建筑、某个区块、某个工厂)作为基本单元，统计确定每个基本单元的人流量分布数据、车流量分布数据、空气状态分布数据、水体状态分布数据；进而评估以上分布数据与本基本单元的相关性指数；根据以上分布数据和相关性指数，再结合每个基本单元的能耗数据和资源用量数据，计算每个基本单元对园区交通、能耗和环境的综合影响度。具体来说，数据解析模块的运算包括以下方面：
(1)如图6A所示，其中U表示园区某个基本单元，K1-K10表示该基本单元周边预定空间范围(例如以U为中心5KM的距离范围内)内的空气检测传感器；数据解析模块获取来自这些空气检测传感器K1-K10的实时空气质量数据A K1，A K2，…A K10，实时空气质量数据是经过数据清洗之后的标准数据；所述数据解析模块根据所获得的实时空气质量数据A K1，A K2，…A K10以及K1-K10的空间位置坐标，将这些实时空气质量数据转化为空气状态分布数据，图6A以图形化的形式示出了空气状态分布数据；进而，数据解析模块计算空气状态分布数据在多个预定方向上的空间变化梯度，如图6A中
Figure PCTCN2018087226-appb-000001
D K1-K6表示空气状态分布数据在K1至K6这一预定方向上的空间变化梯度，dis(K1,K6)表示空气检测传感器K1、K6之间的空间距离；图6A中其它预定方向上的空间变化梯度计算方法与K1至K6这一预定方向相同；数据解析模块再将空气状态分布数据的空间变化梯度转化为空气状态分布数据与基本单元U的相关度指数，如下：
R U-A＝α 1D K1-K6+α 2D K2-K7+α 3D K3-K8+α 4D K4-K9+α 5D K5-K10
其中R U-A表示空气状态分布数据与基本单元U的相关度指数，D K1-K6至D K5-K10表示在每个预定方向的空气状态分布数据的空间变化梯度，而α 1至α 5表示每个预定方向的权重值，可以根据实时风向、风速的因素调节每个预定方向的权重值。R U-A与每个预定方向的空气状态分布数据的空间变化梯度成正比，表明空间变化梯度越大，则本空间范围内的空气质量与本基本单元U的相关性越强。进而，数据解析模块统计本空间范围内的实时空气质量数据A K1，A K2，…A K10的平均值A，并且将R U-A*A作为本基本单元U对空气质量的影响度因数。
(2)如图6B所示，相类似的，其中U表示园区某个基本单元，S1-S5表示该基本单元相邻水体的预定空间范围(例如以U为中心3KM的距离范围内)内的水体环境传感器；数据解析模块获取来自这些水体环境传感器S1-S5的实时水体环境数据W S1，W S2，…W S5，实时水体环境数据是经过数据清洗之后的标准数据；所述数据解析模块根据所获得的实时水体环境数据W S1，W S2，…W S5以及S1-S5的空间位置坐标，将这些实时水体环境数据转化为水体状态分布数据，图6B以图形化的形式示出了水体状态分布数据；进 而，数据解析模块计算水体状态分布数据在水体自然流动方向上的各个区段的空间变化梯度，如图6B中
Figure PCTCN2018087226-appb-000002
D S1-S2表示水体状态分布数据在S1至S2这一预定区段上的空间变化梯度，dis(S1,S2)表示水体环境传感器S1、S2之间的空间距离；图6B中在水体自然流动方向上的其它各个区段空间变化梯度D S2-S3、D S3-S4、D S4-S5计算方法与D S1-S2相同；数据解析模块再将水体状态分布数据的空间变化梯度转化为水体状态分布数据与基本单元U的相关度指数，如下：
R U-W＝β 1D S1-S2+β 2D S2-S3+β 3D S3-S4+β 4D S4-S5
其中R U-W表示水体状态分布数据与基本单元U的相关度指数，而β 1至β 4表示每个预定区段的权重值，可以根据实时水流速度、水流量的因素调节每个预定区段的权重值。R U-W与每个预定区段的水体状态分布数据的空间变化梯度成正比，表明空间变化梯度越大，则水体环境数据与本基本单元U的相关性越强。进而，数据解析模块统计空间范围内的实时水体环境数据W S1，W S2，…W S5的平均值W，并且将R U-W*W作为本基本单元U对水体环境的影响度因数。
(3)如图6C所示，U表示园区某个基本单元，J1-J5表示在该基本单元U周边预定空间范围(例如以U为中心5KM的距离范围内)内的交通监控器；数据解析模块获取来自这些交通监控器J1-J5的实时人流状态数据或者是实时车流状态数据T J1，T J2，…T J5，实时人流状态数据或者是实时车流状态数据是经过数据清洗之后的标准数据；所述数据解析模块根据所获得的实时人流状态数据或者是实时车流状态数据T J1，T J2，…T J5以及J1-J5的空间位置坐标，将这些实时人流状态数据或者是实时车流状态数据转化为人流量分布数据或者是车流量分布数据，图6C以图形化的形式示出了人流量分布数据或者是车流量分布数据；进而，数据解析模块计算以上分布数据在行进方向指向该基本单元U的道路各个区段的空间变化梯度，如图6C中J4所在处为一个岔路口，其中仅J4至J5的区段的行进方向是指向该基本单元U的，因此只计算该区段的空间变化梯度：
Figure PCTCN2018087226-appb-000003
D J4-J5表示人流量分布数据或者是车流量分布数据在J4至J5这一预定区段上的空间变化梯度，dis(J4,J5)表示交通监控器J4、J5之间的空间距离；图6C中在行进方向是指向该基本单元U的方向上的其它各个区段空间变化梯度D J1-J3、D J2-J3、D J3-J4计算方法与D J4-J5相同；数据解析模块再将人流量分布数据或者是车流量分布数据的空间变化梯度转化为人流量分布数据或者是车流量分布数据与基本单元U的相关度指数，如下：
R U-T＝δ 1D J1-J3+δ 2D J2-J3+δ 3D J3-J4+δ 4D J4-J5
其中R U-T表示人流量分布数据或者是车流量分布数据与基本单元U的相关度指数，而δ 1至δ 4表示每个预定区段的权重值，可以根据各个区段与U之间的空间距离调节该权重值，区段距离U越近则权重值越大。R U-T与每个预定区段的人流量分布数据或者是车流量分布数据的空间变化梯度成正比，表明空间变化梯度越大，则人流量分布数据或者是车流量分布数据与本基本单元U的相关性越强。进而，数据解析模块统计空间范围内的实时人流状态数据或者是实时车流状态数据T J1，T J2，…T J5的平均值T，并且将R U-T*T作为本基本单元U对交通的影响度因数。
进而，数据解析模块将每个基本单元U对空气质量、水体环境、人流量或者车流量的影响度因数换算为综合影响度得分；例如，为交通的影响度因数划分若干阈值区间，每个阈值区间对应一个综合影响度得分，例如影响度因数最大的阈值区间得分对应100分、影响度最小的阈值区间对应得分为20分；根据基本单元U的影响度因数R U-T*T在哪个阈值区间之中，而将基本单元U对交通的影响度因数换算为该阈值区间对应的综合影响度得分。再将每个基本单元的能耗数据和资源用量数据也换算为综合影响度得分，将各方面的综合影响度得分累加，计算每个基本单元对园区交通、能耗和环境的综合影响度。
数据解析模块针对园区中的每一个基本单元U获得其综合影响度；并且将综合影响度提供给实时计算单元的报警判断模块和状态处理模块。报警状态模块根据已有规则，当某个单元的综合影响度超出已有规则的允许阈值，则进行预警。状态处理模块为园区中的每一个基本单元U登记其综合影响度以及相关的实时车流状态数据、实时人流状态数据、实时空气质量数据、实时水体环境数据、资源用量数据、能耗数据等状态数据，建立以每一个基本单元U为索引的环境状态数据记录。实时计算单元的各个模块选择Storm流式大数据处理架构，Storm架构的实时性好，在物联网的场景中可支持对终端数据的全局分组，可以很容易实现对物联网数据的清洗、解析、报警等实时的处理。
实时计算单元的数据解析模块与数据中转单元内的影响度数据模块连接，实时计算单元内的报警判断模块与报警数据模块的输入端连接，实时计算单元内的状态处理模块与数据中转单元内环境状态模块连接。数据中转单元从实时计算单元获取上述实时大数据分析的结果数据，包括数据解析模块产生的各方面的影响度因数以及综合影响度，报警判断模块产生的预警记录，状态处理模块的状态记录，并且将这些数据中转到储存单元作为永久性的园区物联网大数据进行保存。
储存单元包括HDFS、HBase、RDBMS和Redis，HDFS非常适合于非结构化数据的存储，支持数据的备份、恢复和迁移，在系统中主要用于存储原始数据和需要进行离线分析的数据，HBase适合于存储半结构化的数据，可以很好的支持海量物联网终端的历史数据的查询，在系统中主要用于存储终端的历史轨迹和状态等体量比较大的数据，数据中转单元内原始数据模块的输出端 与储存单元内HDFS的输入端连接，数据中转单元内影响度数据模块的输出端与储存单元内HBase的输出端连接。数据中转单元内报警数据模块的输出端与储存单元内RDBMS的输入端连接，RDBMS适合于存储结构化的数据，通常根据具体的数据库采用不同的高可用部署方案，在系统中主要用来存储终端基础数据、字典数据和数据分析的结果等，数据中转单元内环境状态模块的输出端与储存单元内Redis的输入端连接，Redis是基于内存的KV数据库，在系统中通常用来缓存需要频繁更新和访问的数据，比如物联网终端的当前状态等，Redis为多种数据类型以及多种数据操作提供了很好的内嵌支持。
离线计算单元包括报表分析模块和大数据挖掘模块，储存单元内HDFS的输出端与离线计算单元的第一输入端连接，储存单元内HBase的输出端与离线计算单元的第二输入端连接，离线计算单元支持MapReduce和Hive，主要用于对物联网数据做日/周/月/年等多个时间维度做报表分析和数据挖掘，并将结果输出到关系数据库中，离线计算单元的第一输出端与储存单元内RDBMS的第二输入端连接，离线计算单元的第二输出端与储存单元内Redis的第二输入端连接。
数据中转单元双向连接有数据交换接口，数据交换接口主要是为了简化应用层与平台层之间的数据访问而抽象了一层访问接口，有了这层接口，应用层就不需要直接调用HDFS、HBase等原生API，可以快速地访问储存单元获取数据进行应用开发，数据交换接口支持：SQL、Restful、Thrift和Java API，可以根据实际情况灵活选择数据交换的方式，数据交换的内容包括：物联网终端的当前状态、物联网终端的历史状态、指令下发、数据订阅与发布等等，数据交换接口的第一端口双向连接有位置分布接口，可通过位置分布接口来查询园区内人员以及车辆的流通情况，数据交换接口的第二端口双向连接有污染监控接口，可通过污染监控接口来查询园区内的污染情况，数据交换接口的第三端口双向连接有报警统计接口，可通过提取触发园区平台报警的数据信息，数据交换接口的第四端口双向连接有管理分析接口，可通过管理分析接口提取园区平台大数据中基本单元的综合影响度及该基本单元的相关数据，数据交换接口的第五端口双向连接有指令下发接口，可通过指令下发接口对园区平台发出控制指令，可以通过数据交换接口来访问园区平台处理后的数据。
工作原理：园区传感器单元内的各传感器设备通过物联网的无线/有线网络经过LVS/F5负载均衡将数据发送至网关，网关接收到数据后存入数据中转单元的原始数据模块内，由原始数据模块发送到实时计算单元内的数据清洗模块，由原始数据模块发送到储存单元内的HDFS进行数据储存，数据清洗模块在接收原始数据模块所发的数据信息后，数据清洗模块将数据再传输给数据解析模块，由数据解析模块进行综合影响度解析，然后把解析后的综合影响度数据分别发给实时计算单元内的报警判断模块和数据中转单元内的影响度数据模块，由影响度数据模块将数据传给储存单元内的HBase进行数据储存，由报警判断模块根据已有规则进行预警，并将产生的结果发送至数据中 转单元内的报警数据模块，由报警数据模块再转发给储存单元内的RDBMS进行数据储存，报警判断模块把解析后的数据发送至状态处理模块，由状态处理模块转发给数据中转单元内的环境状态模块，状态处理模块各项传感参数数据当前状态进行分析，如果状态有变化则，由环境状态模块将数据发给储存单元内的Redis进行数据储存，Redis异步的将所接收数据分别导入HBase和HDFS，离线计算单元则周期性地从HDFS中读取数据进行各种报表分析和数据挖掘，用户业务平台和管理平台可通过数据交换接口访问TIZA STAR平台数据。
综上所述，该面向园区的物联网大数据管理和应用平台，针对规模化的大型工业科技园区，在园区范围内利用园区传感器单元的各种类型的监视器以及传感器等建立一个广泛覆盖的物联网，建立了园区级大数据平台，利用园区传感器单元通过物联网来采集园区范围内的大数据，在大数据基础上提供分析和定制化服务，针对园区内每一个单位，利用所采集的交通流量数据、污染排放数据等，计算它对园区交通、能耗和环境的综合影响度，实现对大型工业科技园区的智能化管理，管理工作效率高，省时省力，且管理成本较低，利于推广使用。
以上所述，仅为本发明较佳的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，根据本发明的技术方案及其发明构思加以等同替换或改变，都应涵盖在本发明的保护范围之内。

Claims (10)

  1. 一种面向园区的物联网大数据管理和应用平台，其特征在于，包括：园区传感器单元、物联网单元、实时计算单元、数据中转单元、储存单元和离线计算单元；
    所述园区传感器单元是分布在园区空间范围的前端数据感知与采集WSN网络，用于感知园区内的实时车流状态数据、实时人流状态数据、实时空气质量数据、实时水体环境数据；
    所述物联网单元用于从所述园区传感器单元获得实时车流状态数据、实时人流状态数据、实时空气质量数据、实时水体环境数据；并且，所述物联网单元从园区的远程水电气表系统的数据输出接口获取园区各个单位的资源用量数据，从园区各个单位建筑的接入物联网的基础设施的数据导出接口获得其能耗数据；所述物联网单元通过负载均衡设备将所获得的数据传送给多路网关，再由多路网关传输给数据中转单元；
    所述数据中转单元用于通过负载均衡设备和网关从物联网单元获取并缓存原始数据，原始数据包括上述实时车流状态数据、实时人流状态数据、实时空气质量数据、实时水体环境数据、资源用量数据、能耗数据；所述数据中转单元将原始数据中转到实时计算单元进行分析处理，并且将所述原始数据以及从实时计算单元获取并缓存的结果数据中转到储存单元进行数据保存；
    所述实时计算单元用于从数据中转单元获取所述原始数据，并且对原始数据按照预定义的数据验证规则进行验证，剔除不完整或者不符合验证规则要求的原始数据，并且将完整且符合验证规则要求的原始数据转换为统一数据格式；根据统一数据格式后的原始数据，统计确定园区每个基本单元的人流量分布数据、车流量分布数据、空气状态分布数据、水体状态分布数据；进而评估以上分布数据与该基本单元的相关性指数；根据以上分布数据和相关性指数，再结合每个基本单元的能耗数据和资源用量数据，计算每个基本单元对园区交通、能耗和环境的综合影响度；根据综合影响度进行超出允许阈值的预警，以及为每个基本单元登记环境状态数据记录；所述综合影响度以及预警记录、环境状态数据记录作为实时计算单元输出的结果数据；
    所述储存单元用于对由所述数据中转单元中转的原始数据以及实时计算单元的结果数据进行保存；
    所述离线计算单元用于根据所述储存单元所保存的数据进行报表分析和数据挖掘。
  2. 根据权利要求1所述的一种面向园区的物联网大数据管理和应用平台，其特征在于：所述数据中转单元包括：原始数据模块、影响度数据模块、报警数据模块、环境状态模块；所述原始数据模块用于通过上述负载均衡设备和网关从物联网单元获取并缓存原始数据，并将自身缓存的上述原始数据传输给实时计算单元，由实时计算单元进行分析处理；原始数据单元也将上述原始数据发送给储存单元进行数据保存；所述影响度数据模块用于获得实时计算单元产生的综合影响度；所述报警数据模块用于获得实时计算单元产生的预警记录；所述环境状态模块用于获得实时计算单元产生的环境状态数据 记录；并且所述影响度数据模块、报警数据模块、环境状态模块将从实时计算单元所获得的数据中转到储存单元。
  3. 根据权利要求2所述的一种面向园区的物联网大数据管理和应用平台，其特征在于：所述数据中转单元双向连接有数据交换接口，数据交换接口用于应用层访问储存单元获取数据；并且，所述据交换接口的第一端口双向连接有位置分布接口，可通过位置分布接口来查询园区内人员以及车辆的流通情况，数据交换接口的第二端口双向连接有污染监控接口，可通过污染监控接口来查询园区内的污染情况，数据交换接口的第三端口双向连接有报警统计接口，可通过提取触发园区平台报警的数据信息，数据交换接口的第四端口双向连接有管理分析接口，可通过管理分析接口提取园区平台大数据中基本单元的综合影响度及该基本单元的相关数据，数据交换接口的第五端口双向连接有指令下发接口，可通过指令下发接口对园区平台发出控制指令。
  4. 根据权利要求3所述的一种面向园区的物联网大数据管理和应用平台，其特征在于：所述实时计算单元包括数据清洗模块、数据解析模块、报警判断模块和状态处理模块；所述数据清洗模块用于从数据中转单元获取所述原始数据，并且对原始数据按照预定义的数据验证规则进行验证，剔除不完整或者不符合验证规则要求的原始数据，并且将完整且符合验证规则要求的原始数据转换为统一数据格式；数据解析模块针对从数据清洗模块获得的标准数据，统计确定园区每个基本单元的人流量分布数据、车流量分布数据、空气状态分布数据、水体状态分布数据；进而评估以上分布数据与该基本单元的相关性指数；根据以上分布数据和相关性指数，再结合每个基本单元的能耗数据和资源用量数据，计算每个基本单元对园区交通、能耗和环境的综合影响度；报警判断模块根据综合影响度进行超出允许阈值的预警；状态处理模块为每个基本单元登记所述综合影响度以及相关的实时车流状态数据、实时人流状态数据、实时空气质量数据、实时水体环境数据、资源用量数据、能耗数据等环境状态数据。
  5. 根据权利要求4所述的一种面向园区的物联网大数据管理和应用平台，其特征在于：数据解析模块针对园区的每个基本单元，根据该基本单元周边预定空间范围的空气检测传感器感知的实时空气质量数据以及空气检测传感器的空间位置坐标，转化为空气状态分布数据；计算空气状态分布数据在多个预定方向上的空间变化梯度，再将空气状态分布数据的空间变化梯度转化为空气状态分布数据与该基本单元的相关度指数；统计本空间范围内的实时空气质量数据的平均值，并且将该平均值与相关度指数的乘积作为该基本单元对空气质量的影响度因数。
  6. 根据权利要求5所述的一种面向园区的物联网大数据管理和应用平台，其特征在于：数据解析模块针对园区的每个基本单元，根据该基本单元相邻水体的预定空间范围内的水体环境传感器感知的实时水体环境数据以及水体环境传感器的空间位置坐标，转化为水体状态分布数据；计算水体状态分布数据在水体自然流动方向上的各个区段的空间变化梯度，将水体状态分布数 据的空间变化梯度转化为水体状态分布数据与该基本单元的相关度指数；统计本空间范围内的实时水体环境数据的平均值，并且将该平均值与相关度指数的乘积作为该基本单元对水体环境的影响度因数。
  7. 根据权利要求6所述的一种面向园区的物联网大数据管理和应用平台，其特征在于：数据解析模块针对园区的每个基本单元，根据该基本单元周边预定空间范围内的交通监控器感知的实时人流状态数据或者是实时车流状态数据，以及交通监控器的空间位置坐标，转化为人流量分布数据或者是车流量分布数据，计算人流量分布数据或者是车流量分布数据在行进方向指向该基本单元的道路各个区段的空间变化梯度，将人流量分布数据或者是车流量分布数据的空间变化梯度转化为人流量分布数据或者是车流量分布数据与该基本单元的相关度指数；统计空间范围内的实时人流状态数据或者是实时车流状态数据的平均值，并且将该平均值与相关度指数的乘积作为该基本单元对交通的影响度因数。
  8. 根据权利要求7所述的一种面向园区的物联网大数据管理和应用平台，其特征在于：数据解析模块将每个基本单元对空气质量、水体环境、交通的影响度因数以及每个基本单元的的能耗数据和资源用量数据换算为综合影响度得分；并且将各个综合影响度得分累加，作为该基本单元的综合影响度。
  9. 根据权利要求8所述的一种面向园区的物联网大数据管理和应用平台，其特征在于：所述园区传感器单元具体包括：交通监控器、空气检测传感器和水体环境传感器；交通监控器包括视频监视摄像机、车流数量传感器、人流量传感器；其中视频监视摄像机可以拍摄园区车行和人行道路的监视画面；车流数量传感器可以采用埋入公路地面的地感线圈，计量单位时间内的通过车辆数量；人流量传感器可以是设置在园区人行道的红外计数器，计量单位时间内的人员通行数量；所述空气检测传感器包括温度传感器、湿度传感器、光化学烟雾传感器和颗粒物传感器，可充分采集园区空气状态和质量数据；所述水体环境传感器包括PH传感器、ORP传感器、浊度传感器、余氯传感器和电导率传感器，可采集园区水体环境数据。
  10. 根据权利要求9所述的一种面向园区的物联网大数据管理和应用平台，其特征在于：所述储存单元包括HDFS、HBase、RDBMS和Redis；数据中转单元内原始数据模块的输出端与储存单元内HDFS的输入端连接；数据中转单元内影响度数据模块的输出端与储存单元内HBase的输出端连接；数据中转单元内报警数据模块的输出端与储存单元内RDBMS的输入端连接；数据中转单元内环境状态模块的输出端与储存单元内Redis的输入端连接。
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