CN116168480A - Remote intelligent control method, system and equipment for realizing channel gate based on Internet of things - Google Patents
Remote intelligent control method, system and equipment for realizing channel gate based on Internet of things Download PDFInfo
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Abstract
The invention relates to an intelligent control technology of the Internet of things, and discloses a remote intelligent control method, a system and equipment for realizing a channel gate based on the Internet of things, wherein the method comprises the following steps: obtaining the crowd flow velocity and the bottom surface distance of the channel gate, calculating local loss according to the crowd flow velocity and the bottom surface distance, and calculating a flow velocity coefficient according to the local loss; acquiring the width of a channel gate and a gate flow coefficient, and calculating the gate flow according to the width, the gate flow coefficient and the flow rate coefficient; monitoring the channel gate to obtain monitoring data, and analyzing the monitoring data to obtain a target flow and a target period; and setting a control mode of the channel gate according to the target flow, the target time period and the passing gate flow, and executing control on the channel gate by using the control mode. The invention can improve the remote intelligent control efficiency and the safety of the channel gate.
Description
Technical Field
The invention relates to the technical field of intelligent control of the Internet of things, in particular to a remote intelligent control method, a system and equipment for realizing a channel gate based on the Internet of things.
Background
With the comprehensive development of modern technology, various industries have formed working states taking technology as a core carrier, particularly with the development of the Internet of things, the technology of the Internet of things has comprehensively entered into various fields of production and life, and an operation mode based on the Internet of things has been established in industrial production and people's life, so that the production capacity and the quality of life are comprehensively improved. Under the trend of rapid development of automatic control technology, an automatic control method of the channel gate is gradually raised, and the channel gate can be controlled according to the dynamic change of the flow of people. However, for the traditional channel gate control, a manual control method or a control method such as a touch screen and a button are adopted in most cases, and on the one hand, the method cannot acquire the dynamic change condition of the traffic in real time, so that a great potential safety hazard exists in the construction engineering; on the other hand, the range of automatic control is limited, automatic remote control cannot be realized, when the distribution position of the channel gate exceeds the range of automatic control, the control time is longer, the control effect is not good, and therefore the efficiency of channel gate control is reduced. In summary, the existing technology has the problem of low remote intelligent control efficiency and safety of the channel gate.
Disclosure of Invention
The invention provides a method, a system and equipment for realizing remote intelligent control of a channel gate based on the Internet of things, and mainly aims to solve the problem that the remote intelligent control efficiency and the safety of the channel gate are not high.
In order to achieve the above purpose, the invention provides a remote intelligent control method for realizing a channel gate based on the internet of things, which comprises the following steps:
obtaining the crowd flow velocity and the bottom surface distance of a channel gate, calculating local loss according to the crowd flow velocity and the bottom surface distance, and calculating a flow velocity coefficient according to the local loss;
acquiring the width of a channel gate and a gate flow coefficient, and calculating the gate flow according to the width, the gate flow coefficient and the flow rate coefficient;
monitoring the channel gate to obtain monitoring data, and analyzing the monitoring data to obtain target flow and a target period;
and setting a control mode of the channel gate according to the target flow, the target time period and the passing gate flow, and executing control on the channel gate by using the control mode.
Optionally, the calculating the local loss according to the crowd flow rate and the bottom surface distance includes:
the local loss is calculated using the following formula:
wherein H represents the local loss, v represents the crowd flow rate, g represents the floor distance,
representing a preset loss factor.
Optionally, the calculating a flow rate coefficient according to the local loss includes:
acquiring a gate single outlet flow of the channel gate, and calculating a flow velocity coefficient according to the gate single outlet flow and the local loss;
the flow rate coefficient was calculated using the following formula:
wherein, xi represents the flow velocity coefficient, H represents the local loss, and eta represents the gate single-outlet flow.
Optionally, the calculating the passing flow according to the width, the gate flow coefficient and the flow rate coefficient includes:
obtaining a target opening degree and a vertical shrinkage value of the channel gate, and calculating initial gate passing flow according to the target opening degree, the vertical shrinkage value, the width and the flow velocity coefficient;
the following formula is used to calculate the passing flow:
wherein W represents the flow rate of the passing gate, v represents the flow rate of the crowd, eta represents the single outlet flow rate of the gate, H represents the local loss, iota represents the width, L represents the vertical shrinkage value, xi represents the flow rate coefficient, e represents the target opening,
representing a preset loss coefficient;
and correcting the initial passing flow by using the gate flow coefficient to obtain the passing flow.
Optionally, the monitoring the channel gate to obtain monitoring data includes:
acquiring historical monitoring data of the channel gate, and screening the historical monitoring data to obtain basic data;
receiving transmission data of a sensor corresponding to the channel gate, and judging whether fault alarm equipment of the channel gate sends out a notification or not;
and when the fault alarm equipment of the channel gate does not send out a notification, summarizing the transmission data and the historical monitoring data into monitoring data.
Optionally, the analyzing the monitoring data to obtain the target flow and the target period includes:
performing attribute division on the monitoring data to obtain a time data type and a flow data type;
acquiring current time, selecting a target period from data corresponding to the time data type according to the current time, and extracting historical data flow and real-time data flow from the flow data type based on the target period;
calculating a flow difference between the historical data flow and the real-time data flow, and judging whether the flow difference exceeds a preset target flow difference;
when the flow difference value does not exceed the target flow difference value, taking the historical data flow as a target flow;
when the flow difference exceeds the target flow difference, correcting the historical data flow by utilizing the real-time data flow, and taking the corrected historical data flow as the target flow.
Optionally, the setting the control mode of the channel gate according to the target flow, the target period and the passing gate flow includes:
normalizing the target flow to obtain normalized data;
calculating the health index of the channel gate by using the normalized data and the target period, and judging whether the channel gate is in a normal running state according to the health index;
when the channel gate is in a normal running state, setting the opening time and the opening frequency of the channel gate according to the target flow and the gate passing flow;
and when the channel gate is in an abnormal operation state, setting the control mode as a manual opening mode.
Optionally, the calculating the health index of the channel gate using the normalized data and the target period includes:
the health index was calculated using the following formula:
HI t ＝(1-β)HI t-1 +βx'
wherein HI t Health index, HI, representing the t target period t-1 Representing the health index of the t-1 th target period, x' representing the normalized data, and β representing a preset smoothness index.
In order to solve the above problems, the present invention further provides a remote intelligent control system for implementing a channel gate based on the internet of things, the system comprising:
the flow velocity coefficient calculation module is used for obtaining the flow velocity of the crowd and the bottom surface distance of the channel gate, calculating local loss according to the flow velocity of the crowd and the bottom surface distance, and calculating a flow velocity coefficient according to the local loss;
the gate flow calculation module is used for obtaining the width of the channel gate and the gate flow coefficient and calculating gate flow according to the width, the gate flow coefficient and the flow rate coefficient;
the monitoring data analysis module is used for monitoring the channel gate to obtain monitoring data and analyzing the monitoring data to obtain target flow and a target period;
and the control mode setting module is used for setting a control mode of the channel gate according to the target flow, the target period and the passing gate flow, and executing control on the channel gate by using the control mode.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor, so that the at least one processor can execute the remote intelligent control method for realizing the channel gate based on the internet of things.
According to the embodiment of the invention, the local loss is calculated through the crowd flow velocity and the bottom surface distance, so that the local loss in the process of controlling the channel gate can be reduced, and the safety in the process of controlling the channel gate is improved; the flow velocity coefficient is calculated through local loss, so that the flow velocity coefficient is more accurate; the gate passing flow is calculated according to the width of the channel gate, the gate flow coefficient and the flow velocity coefficient, so that the accuracy of the gate passing flow can be improved, and the efficiency of controlling the channel gate is improved; the channel gate is monitored to obtain monitoring data, so that the real-time performance of the monitoring data can be ensured, and the accuracy of calculation is ensured; the monitoring data is filtered, so that the filtered data is more accurate; the control mode of the channel gate is set by filtering data and passing gate flow, and the control mode is utilized to control the channel gate, so that the efficiency and the safety of remote intelligent control can be improved. Therefore, the method, the system and the equipment for realizing the remote intelligent control of the channel gate based on the Internet of things can solve the problem that the remote intelligent control efficiency and the safety of the channel gate are not high.
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Fig. 1 is a schematic flow chart of a remote intelligent control method for realizing a channel gate based on the internet of things according to an embodiment of the invention;
FIG. 2 is a flow chart of analyzing monitoring data to obtain a target flow rate and a target period according to an embodiment of the present invention;
FIG. 3 is a flow chart illustrating a control mode of setting a channel gate according to a target flow, a target period and a gate passing flow according to an embodiment of the present invention;
FIG. 4 is a functional block diagram of a remote intelligent control system for implementing a channel gate based on the Internet of things according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device for implementing the method for implementing the remote intelligent control of the channel gate based on the internet of things according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a remote intelligent control method for realizing a channel gate based on the Internet of things. The execution main body of the remote intelligent control method for realizing the channel gate based on the Internet of things comprises at least one of a server, a terminal and the like which can be configured to execute the method provided by the embodiment of the application. In other words, the remote intelligent control method for realizing the channel gate based on the internet of things can be executed by software or hardware installed in the terminal equipment or the server equipment, and the software can be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of a remote intelligent control method for implementing a channel gate based on the internet of things according to an embodiment of the invention is shown. In this embodiment, the method for implementing remote intelligent control of a channel gate based on the internet of things includes:
s1, obtaining the crowd flow velocity and the bottom surface distance of a channel gate, calculating local loss according to the crowd flow velocity and the bottom surface distance, and calculating a flow velocity coefficient according to the local loss.
In the embodiment of the invention, the crowd flow rate refers to the crowd flow rate when the crowd passes through the channel gate; the passage gate is also called a speed gate and a pedestrian passage gate, is one of the common devices in the gate inhibition, is mainly used for shunting and assisting people and vehicles to pass, and can comprise a swing gate, a rotary gate, a full-height gate, an anti-collision swing gate, a wing gate and the like.
In the embodiment of the present invention, the calculating the local loss according to the crowd flow velocity and the bottom surface distance includes:
the local loss is calculated using the following formula:
wherein H represents the local loss, v represents the crowd flow rate, g represents the floor distance,
representing a preset loss factor.
In an embodiment of the present invention, the calculating the flow rate coefficient according to the local loss includes:
acquiring a gate single outlet flow of the channel gate, and calculating a flow velocity coefficient according to the gate single outlet flow and the local loss;
the flow rate coefficient was calculated using the following formula:
wherein, xi represents the flow velocity coefficient, H represents the local loss, and eta represents the gate single-outlet flow.
In the embodiment of the invention, the gate single outlet flow refers to the maximum number of people which can be accommodated in the gate distance of the channel gate; according to the local loss and the gate single outlet flow, the flow velocity coefficient can be calculated more accurately, so that the calculation efficiency is improved.
S2, acquiring the width of the channel gate and the gate hole flow coefficient, and calculating the gate passing flow according to the width, the gate hole flow coefficient and the flow velocity coefficient.
In the embodiment of the invention, the width of the channel gate refers to the lateral distance of the channel gate.
In the embodiment of the invention, the gate flow coefficient refers to a coefficient calculated according to an initial flow velocity coefficient of the channel gate, a ratio coefficient of a section of the channel gate to a gate opening and a relative opening, and can be expressed as follows:
wherein lambda represents the gate flow coefficient, mu represents the initial flow rate coefficient, a represents the ratio coefficient,
represents the relative opening of the channel gate at the same time (T).
In the embodiment of the invention, the initial flow velocity coefficient refers to the initial crowd flow velocity of the channel gate; the ratio coefficient refers to the ratio coefficient of the cross section of the channel gate to the opening size of the gate; the relative opening degree refers to the opening degree of the gate of the channel gate relative to the ground; the sluice flow coefficient is used for improving the accuracy of the sluice flow, so that the efficiency of remote intelligent control is higher.
In the embodiment of the present invention, the calculating the passing gate flow according to the width, the gate flow coefficient and the flow rate coefficient includes:
obtaining a target opening degree and a vertical shrinkage value of the channel gate, and calculating initial gate passing flow according to the target opening degree, the vertical shrinkage value, the width and the flow velocity coefficient;
the following formula is used to calculate the passing flow:
wherein W represents the flow rate of the passing gate, v represents the flow rate of the crowd, eta represents the single outlet flow rate of the gate, H represents the local loss, L represents the width, L represents the vertical shrinkage value, xi represents the flow rate coefficient, e represents the target opening,
representing a preset loss coefficient;
and correcting the initial passing flow by using the gate flow coefficient to obtain the passing flow.
In the embodiment of the invention, the target opening degree of the channel gate refers to a preset opening size of the gate of the channel gate; the vertical shrinkage value refers to the extent of shrinkage of the channel gate.
In the embodiment of the invention, the relative opening degrees of the channel gates are different at different times, so that the gate flow coefficients are different, and the gate single outlet flow of the gate flow is updated according to the gate flow coefficients, so that the initial gate flow is updated, the gate flow is obtained, the accuracy of the gate flow can be improved, and the subsequent control efficiency of the channel gate is improved.
And S3, monitoring the channel gate to obtain monitoring data, and analyzing the monitoring data to obtain target flow and target time period.
In an embodiment of the present invention, the monitoring the channel gate to obtain monitoring data includes:
acquiring historical monitoring data of the channel gate, and screening the historical monitoring data to obtain basic data;
receiving transmission data of a sensor corresponding to the channel gate, and judging whether fault alarm equipment of the channel gate sends out a notification or not;
and when the fault alarm equipment of the channel gate does not send out a notification, summarizing the transmission data and the historical monitoring data into monitoring data.
In the embodiment of the invention, the empty data in the history monitoring data and the data irrelevant to the preset key data are deleted, and the needed data, namely the basic data, are reserved, so that the calculation efficiency of the computer can be improved; the history monitoring data comprise the history running state, maintenance times, working time and the like of the channel gate; the transmission data comprise current people flow, current time and the like; when the channel gate fails, the failure alarm equipment of the channel gate can send out an early warning notice, and the channel gate cannot work normally at the moment; when the channel gate does not fail, the sensor of the channel gate can normally output transmission data, and the real-time performance of the transmission data can be ensured.
Referring to fig. 2, in the embodiment of the present invention, the analyzing the monitoring data to obtain a target flow and a target period includes:
s21, performing attribute division on the monitoring data to obtain a time data type and a flow data type;
s22, acquiring current time, selecting a target period from data corresponding to the time data type according to the current time, and extracting historical data flow and real-time data flow from the flow data type based on the target period;
s23, calculating a flow difference value between the historical data flow and the real-time data flow, and judging whether the flow difference value exceeds a preset target flow difference value;
s24, when the flow difference value does not exceed the target flow difference value, taking the historical data flow as a target flow;
and S25, when the flow difference exceeds the target flow difference, correcting the historical data flow by utilizing the real-time data flow, and taking the corrected historical data flow as the target flow.
In the embodiment of the invention, the monitoring data can be classified by using a preset K-NN classification algorithm to obtain a time data type and a flow data type; selecting a preset time period from the time data types according to the current time, for example, selecting one hour as a time period, counting the historical data flow and the real-time data flow of the channel gate within one hour, and calculating a flow difference value by utilizing four arithmetic operations; the target flow rate difference may be a preset difference, for example, the target flow rate difference may be set to 50, and when the flow rate difference does not exceed 50, the historical data flow rate may be set as a target flow rate; when the flow difference exceeds 50, replacing the historical data flow by utilizing the real-time data flow to obtain corrected historical data flow; and correcting the historical data flow by utilizing the real-time data flow, so that the data flow can be updated, and the real-time property and accuracy of the data flow are ensured.
And S4, setting a control mode of the channel gate according to the target flow, the target time period and the passing gate flow, and executing control on the channel gate by using the control mode.
Referring to fig. 3, in the embodiment of the present invention, the setting the control mode of the channel gate according to the target flow, the target period and the passing gate flow includes:
s31, carrying out normalization processing on the target flow to obtain normalized data;
s32, calculating the health index of the channel gate by using the normalized data and the target period, and judging whether the channel gate is in a normal running state according to the health index;
s33, when the channel gate is in a normal running state, setting the opening time and the opening frequency of the channel gate according to the target flow and the gate passing flow;
s34, when the channel gate is in an abnormal operation state, setting the control mode to be a manual opening mode.
In the embodiment of the invention, the target flow can be normalized by using a Euclidean distance calculation method to obtain normalized data, and the normalized data can be expressed as follows:
wherein x' represents the normalized data, x b Represents the b-th target flow rate, and x represents the target flow rate.
In the embodiment of the present invention, the calculating the health index of the channel gate by using the normalized data and the target period includes:
the health index was calculated using the following formula:
HI t ＝(1-β)HI t-1 +βx'
wherein HI t Health index, HI, representing the t target period t-1 Representing the health index of the t-1 th target period, x' representing the normalized data, and β representing a preset smoothness index.
In the embodiment of the invention, the health index refers to the deviation degree between the current running state and the normal running state of the channel gate, and when the health index is smaller, the channel gate is closer to the normal running state; and when the health index is 0, indicating that the channel gate is in a normal operation state.
In the embodiment of the invention, when the channel gate is in a normal running state, under the maximum limit of the passing gate flow, the opening time and the opening frequency of the channel gate are set according to the target flow; setting the opening time of the channel gate longer and the opening frequency higher when the target flow in the target period is larger without exceeding the maximum value of the passing flow, for example, when the passing flow is set to pass 6 people at a time at most, at 11 pm: setting the opening time of the channel gate to be 3 minutes and the opening frequency to be 20 times in one hour when the people flow rate reaches 300 people in the time period of 30-12:30, so that the channel gate is in an automatic opening mode, and ensuring that the maximum people flow rate can pass through the time period under the setting condition; when the channel gate is not in a normal running state, the control mode is set to be a manual open mode, namely, an incumbent staff performs manual control in a button and other modes, so that the open time and the open frequency of the channel gate are set, and intelligent management of the channel gate is realized.
In the embodiment of the invention, the control mode is set according to the target flow and the passing flow by using the control mode to control the channel gate, so that the channel gate operates according to the control mode, the remote intelligent control efficiency can be improved, the situation that the channel gate cannot be controlled due to the distance can be reduced, and the safety of remote intelligent control is improved.
According to the embodiment of the invention, the local loss is calculated through the crowd flow velocity and the bottom surface distance, so that the local loss in the process of controlling the channel gate can be reduced, and the safety in the process of controlling the channel gate is improved; the flow velocity coefficient is calculated through local loss, so that the flow velocity coefficient is more accurate; the gate passing flow is calculated according to the width of the channel gate, the gate flow coefficient and the flow velocity coefficient, so that the accuracy of the gate passing flow can be improved, and the efficiency of controlling the channel gate is improved; the channel gate is monitored to obtain monitoring data, so that the real-time performance of the monitoring data can be ensured, and the accuracy of calculation is ensured; the monitoring data is filtered, so that the filtered data is more accurate; the control mode of the channel gate is set by filtering data and passing gate flow, and the control mode is utilized to control the channel gate, so that the efficiency and the safety of remote intelligent control can be improved. Therefore, the remote intelligent control method for realizing the channel gate based on the Internet of things can solve the problem that the remote intelligent control efficiency and the safety of the channel gate are not high.
Fig. 4 is a functional block diagram of a remote intelligent control system for implementing a channel gate based on the internet of things according to an embodiment of the present invention.
The remote intelligent control system 400 for realizing the channel gate based on the Internet of things can be installed in electronic equipment. According to the implemented functions, the remote intelligent control system 400 for implementing the channel gate based on the internet of things may include a flow rate coefficient calculation module 401, a gate flow calculation module 402, a monitoring data analysis module 403, and a control mode setting module 404. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the flow velocity coefficient calculation module 401 is configured to obtain a crowd flow velocity and a bottom surface distance of a channel gate, calculate a local loss according to the crowd flow velocity and the bottom surface distance, and calculate a flow velocity coefficient according to the local loss;
the gate flow calculation module 402 is configured to obtain a width of a channel gate and a gate flow coefficient, and calculate a gate flow according to the width, the gate flow coefficient and the flow rate coefficient;
the monitoring data analysis module 403 is configured to monitor the channel gate to obtain monitoring data, and analyze the monitoring data to obtain a target flow and a target period;
the control mode setting module 404 is configured to set a control mode of the channel gate according to the target flow, the target period, and the passing gate flow, and perform control on the channel gate using the control mode.
In detail, each module in the remote intelligent control system 400 for implementing the channel gate based on the internet of things in the embodiment of the present invention adopts the same technical means as the remote intelligent control method for implementing the channel gate based on the internet of things in the drawings, and can produce the same technical effects, which are not described herein.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a method for implementing a remote intelligent control of a channel gate based on the internet of things according to an embodiment of the present invention.
The electronic device 500 may comprise a processor 501, a memory 502, a communication bus 503 and a communication interface 504, and may further comprise a computer program stored in the memory 502 and executable on the processor 501, such as a remote intelligent control program implementing a channel gate based on the internet of things.
The processor 501 may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing Unit, CPU), a microprocessor, a digital processing chip, a graphics processor, a combination of various control chips, and so on. The processor 501 is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, executes or executes programs or modules stored in the memory 502 (e.g., executes a remote intelligent Control program for implementing a channel gate based on the internet of things, etc.), and invokes data stored in the memory 502 to perform various functions of the electronic device and process data.
The memory 502 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 502 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 502 may also be an external storage device of the electronic device in other embodiments, for example, a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like. Further, the memory 502 may also include both internal storage units and external storage devices of the electronic device. The memory 502 may be used to store not only application software installed in an electronic device and various data, such as codes of a remote intelligent control program for implementing a channel gate based on the internet of things, but also temporarily store data that has been output or is to be output.
The communication bus 503 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable connected communication between the memory 502 and the at least one processor 501 etc.
The communication interface 504 is used for communication between the electronic device and other devices, including network interfaces and user interfaces. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
Fig. 5 illustrates only an electronic device having components, and it will be appreciated by those skilled in the art that the configuration illustrated in fig. 5 is not limiting of the electronic device 500 and may include fewer or more components than illustrated, or may combine certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for powering the respective components, and the power source may be logically connected to the at least one processor 501 through a power management system, so as to perform functions of charge management, discharge management, and power consumption management through the power management system. The power supply may also include one or more of any of a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The remote intelligent control program stored in the memory 502 of the electronic device 500 and implemented on the basis of the internet of things is a combination of a plurality of instructions, which when executed in the processor 501, can implement:
obtaining the crowd flow velocity and the bottom surface distance of a channel gate, calculating local loss according to the crowd flow velocity and the bottom surface distance, and calculating a flow velocity coefficient according to the local loss;
acquiring the width of a channel gate and a gate flow coefficient, and calculating the gate flow according to the width, the gate flow coefficient and the flow rate coefficient;
monitoring the channel gate to obtain monitoring data, and analyzing the monitoring data to obtain target flow and a target period;
and setting a control mode of the channel gate according to the target flow, the target time period and the passing gate flow, and executing control on the channel gate by using the control mode.
In particular, the specific implementation method of the above instruction by the processor 501 may refer to the description of the relevant steps in the corresponding embodiment of the drawings, which is not repeated herein.
Further, the modules/units integrated with the electronic device 500 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
obtaining the crowd flow velocity and the bottom surface distance of a channel gate, calculating local loss according to the crowd flow velocity and the bottom surface distance, and calculating a flow velocity coefficient according to the local loss;
acquiring the width of a channel gate and a gate flow coefficient, and calculating the gate flow according to the width, the gate flow coefficient and the flow rate coefficient;
monitoring the channel gate to obtain monitoring data, and analyzing the monitoring data to obtain target flow and a target period;
and setting a control mode of the channel gate according to the target flow, the target time period and the passing gate flow, and executing control on the channel gate by using the control mode.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus, system and method may be implemented in other manners. For example, the system embodiments described above are merely illustrative, e.g., the division of the modules is merely a logical function division, and other manners of division may be implemented in practice.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. Multiple units or systems as set forth in the system claims may also be implemented by means of one unit or system in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. The method for realizing the remote intelligent control of the channel gate based on the Internet of things is characterized by comprising the following steps:
obtaining the crowd flow velocity and the bottom surface distance of a channel gate, calculating local loss according to the crowd flow velocity and the bottom surface distance, and calculating a flow velocity coefficient according to the local loss;
acquiring the width of a channel gate and a gate flow coefficient, and calculating the gate flow according to the width, the gate flow coefficient and the flow rate coefficient;
monitoring the channel gate to obtain monitoring data, and analyzing the monitoring data to obtain target flow and a target period;
and setting a control mode of the channel gate according to the target flow, the target time period and the passing gate flow, and executing control on the channel gate by using the control mode.
2. The method for remotely and intelligently controlling a gate based on the internet of things according to claim 1, wherein the calculating the local loss according to the crowd flow rate and the bottom surface distance comprises:
the local loss is calculated using the following formula:
wherein H represents the local loss, v represents the crowd flow rate, g represents the floor distance,
representing a preset loss factor.
3. The method for remotely and intelligently controlling a channel gate based on the internet of things according to claim 1, wherein the calculating the flow rate coefficient according to the local loss comprises:
acquiring a gate single outlet flow of the channel gate, and calculating a flow velocity coefficient according to the gate single outlet flow and the local loss;
the flow rate coefficient was calculated using the following formula:
wherein, xi represents the flow velocity coefficient, H represents the local loss, and eta represents the gate single-outlet flow.
4. The method for remotely and intelligently controlling a channel gate based on the internet of things according to claim 1, wherein the calculating the gate flow according to the width, the gate flow coefficient and the flow rate coefficient comprises:
obtaining a target opening degree and a vertical shrinkage value of the channel gate, and calculating initial gate passing flow according to the target opening degree, the vertical shrinkage value, the width and the flow velocity coefficient;
the following formula is used to calculate the passing flow:
wherein W represents the flow rate of the passing gate, v represents the flow rate of the crowd, eta represents the single outlet flow rate of the gate, H represents the local loss, iota represents the width, L represents the vertical shrinkage value, xi represents the flow rate coefficient, e represents the target opening,
representing a preset loss coefficient;
and correcting the initial passing flow by using the gate flow coefficient to obtain the passing flow.
5. The method for remotely and intelligently controlling the channel gate based on the internet of things according to claim 1, wherein the monitoring the channel gate to obtain the monitoring data comprises the following steps:
acquiring historical monitoring data of the channel gate, and screening the historical monitoring data to obtain basic data;
receiving transmission data of a sensor corresponding to the channel gate, and judging whether fault alarm equipment of the channel gate sends out a notification or not;
and when the fault alarm equipment of the channel gate does not send out a notification, summarizing the transmission data and the historical monitoring data into monitoring data.
6. The method for remotely and intelligently controlling a channel gate based on the internet of things according to claim 1, wherein the analyzing the monitoring data to obtain the target flow and the target time period comprises:
performing attribute division on the monitoring data to obtain a time data type and a flow data type;
acquiring current time, selecting a target period from data corresponding to the time data type according to the current time, and extracting historical data flow and real-time data flow from the flow data type based on the target period;
calculating a flow difference between the historical data flow and the real-time data flow, and judging whether the flow difference exceeds a preset target flow difference;
when the flow difference value does not exceed the target flow difference value, taking the historical data flow as a target flow;
when the flow difference exceeds the target flow difference, correcting the historical data flow by utilizing the real-time data flow, and taking the corrected historical data flow as the target flow.
7. The method for remotely and intelligently controlling a channel gate based on the internet of things according to claim 1, wherein the setting the control mode of the channel gate according to the target flow, the target period and the passing gate flow comprises:
normalizing the target flow to obtain normalized data;
calculating the health index of the channel gate by using the normalized data and the target period, and judging whether the channel gate is in a normal running state according to the health index;
when the channel gate is in a normal running state, setting the opening time and the opening frequency of the channel gate according to the target flow and the gate passing flow;
and when the channel gate is in an abnormal operation state, setting the control mode as a manual opening mode.
8. The method for remotely and intelligently controlling a channel gate based on the internet of things according to claim 7, wherein the calculating the health index of the channel gate using the normalized data and the target period of time comprises:
the health index was calculated using the following formula:
HI t ＝(1-β)HI t-1 +βx'
wherein HI t Health index, HI, representing the t target period t-1 Representing the health index of the t-1 th target period, x' representing the normalized data, and β representing a preset smoothness index.
9. Remote intelligent control system based on thing networking realizes channel floodgate, its characterized in that, the system includes:
the flow velocity coefficient calculation module is used for obtaining the flow velocity of the crowd and the bottom surface distance of the channel gate, calculating local loss according to the flow velocity of the crowd and the bottom surface distance, and calculating a flow velocity coefficient according to the local loss;
the gate flow calculation module is used for obtaining the width of the channel gate and the gate flow coefficient and calculating gate flow according to the width, the gate flow coefficient and the flow rate coefficient;
the monitoring data analysis module is used for monitoring the channel gate to obtain monitoring data and analyzing the monitoring data to obtain target flow and a target period;
and the control mode setting module is used for setting a control mode of the channel gate according to the target flow, the target period and the passing gate flow, and executing control on the channel gate by using the control mode.
10. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the remote intelligent control method for implementing a channel gate based on the internet of things as claimed in any one of claims 1 to 8.
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