CN112565401A - Closed environment personnel number detection visualization method and system based on Internet of things cloud platform - Google Patents
Closed environment personnel number detection visualization method and system based on Internet of things cloud platform Download PDFInfo
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Abstract
The invention discloses a method for realizing a closed environment personnel number detection visualization system based on an Internet of things cloud platform, which comprises the following steps: a method for combining an Internet of things equipment terminal and an Internet of things cloud platform; a future personnel flow prediction method; provided is a bus regulation and control method. The method for combining the Internet of things equipment terminal and the Internet of things cloud platform is used for realizing the intelligent bus detection function; the future personnel flow prediction method combines a long-term and short-term memory network algorithm in machine learning to provide a data basis for the computation regulation and control of the OneNet cloud platform; the bus regulation and control method is characterized by combining Internet of things cloud platform prediction data with actual bus use conditions, comparing results of the prediction data with data of actual bus operation conditions, and dynamically recommending departure quantity and departure intervals according to personnel flow. The invention also discloses a system for realizing the method, which comprises the Internet of things cloud platform, the cloud server terminal and the Internet of things equipment terminal.
Description
Technical Field
The invention belongs to the technical field of computers, relates to an internet of things cloud platform, a visualization technology and a machine learning prediction algorithm, and particularly relates to a method for realizing real display of data in a virtual simulation mode, monitoring traffic running conditions and traffic flow conditions in real time based on a digital environment, and realizing a bus regulation and control function through prediction of future traffic flow.
Background
The OneNet cloud platform is an efficient, stable and safe Internet of things open platform created by China Mobile. The OneNet cloud platform supports adaptation to various network environments and protocol types, can realize quick access of various sensors and intelligent hardware, provides rich APIs (application programming interfaces) and application templates to support development of various industrial applications and intelligent hardware, effectively reduces application development and deployment cost of the Internet of things, and meets platform-level service requirements of equipment connection, protocol adaptation, data storage, data security, big data analysis and the like in the field of the Internet of things.
The development of computer networks and intelligent equipment enables the interconnection of everything to become a trend, and the intelligent vehicle-mounted terminal can dynamically manage the running of vehicles, and is combined with a Global Positioning System (GPS) to detect information such as bus positions, station-entering and station-leaving time, station names and the like in real time. The public transport passenger flow prediction in China is relatively less researched, the public transport passenger flow statistical analysis is more researched, and most of the public transport passenger flow statistical analysis is limited to theoretical research.
Even in the current re-work and re-production stage, attention is paid to keeping social distance, especially in a narrow and closed place. Therefore, information for detecting and reporting the number of people in the public closed places is a new urgent need, and it is an urgent problem to build an intelligent system capable of reporting the number of people in public closed places such as buses in real time.
Disclosure of Invention
In order to overcome the defects of the existing method, the invention provides a closed environment personnel number detection visualization method and system based on an Internet of things cloud platform. The data are truly displayed through the visualization of the cloud platform of the Internet of things, a data center is constructed based on the number of people in a closed environment, information is fed back to the visualization platform, the detection and control of all data are realized, relevant information is integrated and displayed, relevant managers can clearly and visually master effective information in operation, and the transparent and visual management is realized. The visual management can also enable the operation information and the operation condition to be more visual, so that the complicated personnel flow information becomes easy to express, understand and propagate, thereby eliminating the cognitive deviation and the supervision blind area among different roles in the operation process, further effectively improving the management and monitoring efficiency, and realizing the establishment of a three-dimensional and visual new generation data platform.
The invention discloses a method for realizing a closed environment personnel number detection visualization system based on an Internet of things cloud platform, which comprises the following steps:
step a) a method for combining an Internet of things equipment terminal with an Internet of things cloud platform:
the method for combining the extracted internet-of-things equipment terminal with the internet-of-things cloud platform is characterized in that the internet-of-things equipment terminal uploads the acquired data to the cloud server through a data interface provided by the OneNet cloud platform. The cloud server is used for receiving and storing data, calling a data prediction model for training, taking a data set as input of a future personnel flow prediction method after the cloud server receives enough data to obtain prediction data, and uploading the prediction data to the OneNet cloud platform.
The OneNET cloud platform has the main function of visually displaying data, when the number of people exceeds 80%, the notification trigger is issued, and an administrator can observe the number of people and the regulation and control recommendation of the number of buses during departure in real time through the OneNET cloud platform. The process realizes the combination of the Internet of things equipment terminal and the Internet of things cloud platform, and achieves the best experience effect.
Step b) a future personnel flow prediction method:
in order to realize the prediction of the future personnel flow on the basis of the existing data and control the bus departure interval time and the number of the buses, the invention combines a long-short term memory network (LSTM) algorithm in machine learning.
LSTM is a type of recurrent neural network with three inputs c for a single cyclet-1、ht-1And xtWherein c ist-1、ht-1Is the output of the previous cycle, xtIs the input of data in the current state. When inputting xtH after transfer and last state transfert-1Stitching training to obtain z, zf、zi、zoAs a gated state. The output y of the current cycle is then obtained by the following formulatAnd parameters passed to the next cycle.
yt＝σ(W`ht)
The z represents a result obtained by calculation after the input content is selectively memorized; z is a radical offIndicating forgetting to control the door for controlling the previous state ct-1Which need to be forgotten; z is a radical ofiRepresenting memory gates for input xtPerforming selective memory; z is a radical ofoIndicating the output gates that determine which are to be output as the current state.
There are three main stages inside the LSTM:
1) a forgetting stage: this stage is mainly for the incoming input c of the previous nodet-1Performing selective forgettingAnd (7) recording.
2) Selecting a memory stage: this stage inputs x to this stagetThe memory is selectively performed.
3) An output stage: this phase will determine which will be the output of the current state.
The LSTM is a special cyclic neural network, and each output value is related to the current input value and the last output value, so that the human flow condition in a future period of time can be well predicted according to the existing data, and a data basis is provided for the calculation regulation and control of the OneNet cloud platform.
Running on the cloud server is a data prediction model program. In the data prediction program, the LSTM model is trained by using bus on-off data, and a final prediction model is obtained through model parameter optimization. And predicting the getting-on and getting-off conditions of the next day by using the data of the getting-on and getting-off people of the bus of each day according to the prediction model, and sending the data to the OneNET cloud platform. Compared with the common recurrent neural network, the LSTM can continuously forget some knowledge and memorize some knowledge, realize that all previous inputs are considered in the output of each step, solve the problems of gradient disappearance and gradient explosion in the long sequence training process and have more excellent performance in engineering use.
Step c) a bus regulation and control method:
the internet of things cloud platform provides a filter function, in the process of data simulation visualization, when data flow is uploaded, the filter module can acquire required data according to a user-defined rule, the result of data prediction is compared with the data of the actual running condition of the bus, the bus dispatching quantity and the bus dispatching interval are recommended according to the condition of future passenger flow, the recommended result is visually displayed in an OneNet cloud platform view panel, an operation manager can adjust the bus dispatching quantity according to information, and the bus regulation and control function is achieved. For example, if the number of buses in current operation is not enough to bear future passenger flow, the operation manager can reasonably increase the number of buses and shorten the bus dispatching interval according to platform recommendation, and scientifically dispatch the buses. Meanwhile, citizens can know the number of passengers and the crowdedness of the bus in real time, a trip plan is reasonably arranged, and waiting time is shortened.
The invention also provides a system for realizing the method, which comprises the following steps: the system comprises a OneNet cloud platform, a cloud server end and an Internet of things equipment terminal. The OneNet cloud platform is used for data forwarding, data receiving and data displaying; the internet of things equipment terminal uploads data of getting on and off buses of bus passengers to the OneNET cloud platform; the cloud server is used for training data and sending the data to the OneNet cloud platform.
The beneficial effects of the invention include: a manager of the public traffic system can know the dynamic condition of the whole traffic operation at first time through the visual page, and can carry out quantitative management according to the departure quantity, the passenger quantity and the crowding degree of the buses, thereby providing data support for the real-time management of urban traffic.
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Fig. 1 is a schematic diagram of an implementation method of a closed environment personnel number detection visualization system based on an internet of things cloud platform.
Fig. 2 is a general block diagram of the system of the present invention.
Detailed Description
The related art to which the present invention relates will be explained and explained in detail based on specific embodiments and drawings. The remaining techniques and implementation conditions related to the methods of the present invention, other than those mentioned immediately below, are common knowledge and common general knowledge in the art and related fields.
The invention discloses a method for realizing a closed environment personnel number detection visualization system based on an Internet of things cloud platform, which comprises the following steps: a method for combining an Internet of things equipment terminal and an Internet of things cloud platform; a future personnel flow prediction method; provided is a bus regulation and control method. The method for combining the Internet of things equipment terminal and the Internet of things cloud platform is used for realizing the intelligent bus detection function; the future personnel flow prediction method combines a long-term and short-term memory network algorithm in machine learning to provide a data basis for the computation regulation and control of the OneNet cloud platform; the bus regulation and control method is characterized by combining Internet of things cloud platform prediction data with actual use conditions of buses, comparing results of the prediction data with data of actual operation conditions of the buses, and dynamically recommending departure quantity and departure intervals according to personnel flow. The invention also discloses a system for realizing the method, which comprises the Internet of things cloud platform, the cloud server terminal and the Internet of things equipment terminal.
The closed environment personnel number detection visualization method based on the Internet of things cloud platform comprises the following steps:
step a) combining an Internet of things equipment terminal with an Internet of things cloud platform:
after the internet of things equipment terminal is accessed to a network, the equipment and the equipment need to communicate with each other, and the equipment and the cloud end need to communicate with each other, which requires heavy work. The OneNet cloud platform belongs to a PaaS Internet of things open platform, supports adaptation to various network environments and protocol types, and has the advantages of being low in development cost, strong in stability, short in period and the like.
The internet of things equipment terminal uploads the acquired data to the cloud server through the data interface provided by the OneNet cloud platform. After receiving enough data, the cloud server terminal takes the data set as input of a future person flow prediction method to obtain prediction data, and uploads the prediction data to the OneNet cloud platform. The OneNET cloud platform can be used for visualizing data in real time, so that the purpose of combining the Internet of things equipment terminal with the OneNET cloud platform is achieved, the intelligent bus detection function is achieved, and the intelligent bus detection system has the advantages of being low in cost, simple in structure, high in detection precision and the like.
Step b) a future personnel flow prediction method:
the method has important significance for controlling the bus departure quantity and epidemic situation prevention and control by accurately predicting the flow of future personnel, and combines a long-short term memory network (LSTM) algorithm in machine learning to effectively realize the function. Compared with the common recurrent neural network, the LSTM can continuously forget some knowledge and memorize some knowledge, realize that all previous inputs are considered in the output of each step, can solve the problems of gradient disappearance and gradient explosion in the long sequence training process, and has more excellent performance in engineering. Therefore, the time sequence information can be fully utilized, and the method has considerable prediction accuracy, can predict the people flow condition in a future period of time according to the existing data, and provides a data basis for the computation regulation and control of the OneNET cloud platform. The long-short term memory network algorithm LSTM is operated on a cloud server.
Step c) a bus regulation and control method:
the internet of things cloud platform provides a filter function, and in the process of data simulation visualization, when data flow is uploaded, the filter module can acquire own data according to a user-defined rule and combines predicted data with the actual use condition of the bus. According to the condition of future passenger flow, the number and the intervals of departure of the buses are recommended, the recommended results are visually displayed on the OneNet cloud platform view panel, an operation manager can reasonably adjust the number and the intervals of departure of the buses according to the platform recommended information, vehicles can be scientifically scheduled, and the bus regulation and control function is achieved. Meanwhile, citizens can know the number of passengers and the crowdedness of the bus in real time, a trip plan is reasonably arranged, and waiting time is shortened.
The invention also provides a system for realizing the method, which comprises the OneNet cloud platform, the cloud server terminal and the Internet of things equipment terminal. The OneNet cloud platform is used for data forwarding, data receiving and data displaying; data of getting on and off buses of the Internet of things equipment terminal are uploaded to the OneNet cloud platform; the cloud server is used for training data and sending the data to the OneNet cloud platform.
And a data prediction program is operated on the cloud server, and compared with the common recurrent neural network, the LSTM can continuously forget some knowledge and memorize some knowledge, so that all previous inputs are considered in the output of each step. The problems of gradient extinction and gradient explosion in the long sequence training process can be solved. Has more excellent performance in engineering.
Examples
The invention provides a method for realizing a closed environment personnel number detection visualization system based on an Internet of things cloud platform, which comprises the following code realization parts (important interception):
1) the logic that thing networking device terminal and OneNET cloud platform combined has realized the uploading of data with Python language according to OneNET cloud platform data point upload interface, and specific code is as follows:
baseurl is defined in http request class as OneNET api interface public path, and _init _ function is used to receive OneNET platform device number and key.
The addDatapoints method defines the relevant code for data submission.
The getcurenttime method in the Time class realizes the function of acquiring the current uploading Time. When the data collected by the terminal of the Internet of things equipment needs to be sent to the OneNet terminal, the Time class and the HttpRequest class are called.
2) The future personnel flow prediction method is realized based on an LSTM algorithm model, and the specific codes are as follows:
first, a dependency package necessary for the program is imported, and the linear modal class includes common variables such as seed (random seed) and batch _ size (each training batch size).
The build _ model function is used to create a model that defines the LSTM as the initial algorithm, output dimensions, and loss functions.
The train method in the Main comprises the processes of data loading, data standardization, data conversion, model definition training, model verification and the like. And when the data are updated, retraining to obtain the prediction data and sending the prediction data to the cloud platform.
3) The bus regulation and control method gives the logic of bus regulation and control, and the specific codes are as follows:
the code obtains the prediction data through an interface from the internet of things cloud platform. After the prediction data is obtained, it is compared with the passenger capacity of the currently running vehicle. And if the predicted value is larger than the current passenger capacity, increasing the number of departure vehicles. If the predicted value is near the current passenger capacity, no adjustment is made. And if the predicted value is smaller than the current passenger capacity, reducing the number of departure vehicles.
When data are updated, the data filter of the cloud platform of the internet of things in the tomorrow plan can be executed again, wherein the number of dispatched buses is assumed to be 33 normally, and when the peak value of the predicted number of people is higher or lower, the number of dispatched buses can fluctuate up and down. The manager can adjust the departure quantity according to the prediction data.
According to the method for realizing the closed environment personnel number detection visualization system based on the cloud platform of the Internet of things, the data are really displayed through visualization of the cloud platform of the Internet of things, the data center is built based on the personnel number of the closed environment, information is fed back to the visualization platform, detection and control over all data are realized, relevant information is integrally displayed, relevant managers can clearly and intuitively master effective information in operation, and transparent and visual management is realized. The visual management can also enable the operation information and the operation condition to be more visual, so that the complicated personnel flow information becomes easy to express, understand and propagate, thereby eliminating the cognitive deviation and the supervision blind area among different roles in the operation process, further effectively improving the management and monitoring efficiency, and realizing the establishment of a three-dimensional visual new generation data platform.
The protection of the present invention is not limited to the above embodiments. Variations and advantages that may occur to those skilled in the art may be incorporated into the invention without departing from the spirit and scope of the inventive concept, and the scope of the appended claims is intended to be protected.
Claims (10)
1. The closed environment personnel number detection visualization method based on the Internet of things cloud platform is characterized by comprising the following steps:
step a) a method for combining an internet of things equipment terminal with a OneNet cloud platform: the intelligent bus detection function is realized;
step b) a future personnel flow prediction method: combining a long-short term memory network algorithm LSTM in machine learning to provide a data basis for the computation regulation and control of the OneNet cloud platform;
step c) a bus regulation and control method: the Internet of things cloud platform prediction data and the actual use condition of the bus are combined, the result of the prediction data is compared with the data of the actual operation condition of the bus, and the number of departure and the departure interval are dynamically recommended according to the flow of people.
2. The method for detecting and visualizing the number of people in the closed environment based on the cloud platform of the internet of things according to claim 1, wherein in the step a, the internet of things equipment terminal uploads the acquired data to the cloud server through a data interface provided by the OneNET cloud platform.
3. The method for detecting and visualizing the number of people in the closed environment based on the cloud platform of the internet of things according to claim 2, wherein in the step a, the cloud server receives and stores data, trains by calling a data prediction model and obtains enough prediction data, and uploads the prediction data to the OneNET cloud platform.
4. The method for detecting and visualizing the number of people in the closed environment based on the cloud platform of the internet of things according to claim 1, wherein in the step a, the OneNET cloud platform displays data, and when the number of people exceeds 80%, a notification trigger is issued; people flow quantity and bus departure quantity regulation and control recommendation are observed in real time through the OneNet cloud platform.
5. The method for detecting and visualizing the number of people in the closed environment based on the cloud platform of the internet of things as claimed in claim 1, wherein in the step b, the long-short term memory network algorithm LSTM is run on a cloud server.
6. The method for detecting and visualizing the number of people in the closed environment based on the cloud platform of the internet of things as claimed in claim 1, wherein in the step c, the predicted data is filtered by a filter in advance, and desired data is obtained according to a customized rule.
7. The method for detecting and visualizing the number of people in the closed environment based on the cloud platform of the internet of things according to claim 1, wherein in the step c, the recommendation result is directly displayed in a view panel of the OneNET cloud platform.
8. The utility model provides a visual system of closed environment personnel quantity detection based on thing networking cloud platform which characterized in that, the system includes: the system comprises an Internet of things cloud platform, a cloud server side and an Internet of things equipment terminal; wherein the content of the first and second substances,
the Internet of things cloud platform is a OneNet cloud platform and is used for data forwarding, data receiving and data displaying; further, the internet of things cloud platform is also provided with a filter module;
the internet of things equipment terminal uploads data of getting on and off buses of bus passengers to the OneNET cloud platform;
the cloud server is used for training data and sending the data to the OneNet cloud platform.
9. The system for detecting and visualizing the number of people in the closed environment based on the cloud platform of the internet of things as claimed in claim 8, wherein a data prediction program is run on the cloud server, the data prediction program is based on a long-short term memory network algorithm (LSTM), and compared with a common recurrent neural network, the system can continuously forget some knowledge and memorize some knowledge, so that all previous inputs are considered in the output of each step.
10. The closed environment personnel number detection visualization system based on the cloud platform of the internet of things as claimed in claim 8, wherein in the cloud platform of the internet of things, the filter module obtains desired data according to a custom rule, and combines predicted data with actual use conditions of buses to recommend the number of departure and departure intervals of the buses.
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