CN117459398B - Data processing method, data processing device and elevator based on Internet of things - Google Patents
Data processing method, data processing device and elevator based on Internet of things Download PDFInfo
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Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/08—Configuration management of networks or network elements
- H04L41/0896—Bandwidth or capacity management, i.e. automatically increasing or decreasing capacities
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B1/00—Control systems of elevators in general
- B66B1/34—Details, e.g. call counting devices, data transmission from car to control system, devices giving information to the control system
- B66B1/3415—Control system configuration and the data transmission or communication within the control system
- B66B1/3446—Data transmission or communication within the control system
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B1/00—Control systems of elevators in general
- B66B1/34—Details, e.g. call counting devices, data transmission from car to control system, devices giving information to the control system
- B66B1/3415—Control system configuration and the data transmission or communication within the control system
- B66B1/3446—Data transmission or communication within the control system
- B66B1/3453—Procedure or protocol for the data transmission or communication
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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
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
The application provides a data processing method, a data processing device and an elevator based on the Internet of things, which are characterized in that a historical operation data set of a selected node is obtained, the historical operation data set of the selected node is subjected to polynomial interpolation to obtain a historical operation data track, the active characteristic of the historical operation data track is detected, the active characteristic value of the historical operation data of the selected node is obtained, the corresponding communication scheduling parameters of the selected node in a communication scheduling matrix are updated according to the active characteristic value of the historical operation data of the selected node, the communication occupied bandwidth of each node in the elevator Internet of things system is adjusted according to the updated communication scheduling matrix, and the occupied bandwidth of the node can be adjusted according to the data transmitted by different nodes in the elevator Internet of things system, so that the situation that the communication bandwidth is occupied when abnormal communication data exists in the node is avoided, the abnormal communication data can be found in time, and the safety of elevator operation is improved.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a data processing method, a data processing device and an elevator based on the Internet of things.
Background
The system of the Internet of things is a system for monitoring the operation of each component in the system through the Internet, the state and the performance of the system are monitored in real time by using a sensor, a data collection and a remote control technology, after the sensor detects the operation parameters and the state of a system node, the data are transmitted to the center of the Internet of things through the Internet, and then the center of the Internet of things remotely sends out a control instruction to adjust the operation state of the system of the Internet of things; the elevator internet of things technology can improve reliability, safety and efficiency of an elevator, and meanwhile provides more accurate data and information for maintenance.
However, in the prior art, the elevator internet of things system often needs to keep high-frequency communication with a large number of nodes in the system, the transmission of communication data needs to occupy a large bandwidth, if communication scheduling is not performed on each node of the elevator internet of things system, when abnormal communication data needing to be transmitted exists in the nodes in the elevator internet of things, the transmission bandwidth of the abnormal communication data is possibly occupied and waits, so that a remote control center of the internet of things system cannot timely send a control instruction, and elevator safety accidents are caused.
Disclosure of Invention
The application provides a data processing method, a data processing device and a system based on the Internet of things, which are used for solving the technical problem that when abnormal communication data to be transmitted exists in a node in the Internet of things of an elevator, the transmission bandwidth of the abnormal communication data is occupied and waits, so that a remote control center of the Internet of things system cannot timely send a control instruction.
In order to solve the technical problems, the application adopts the following technical scheme:
in a first aspect, the present application provides a data processing method based on the internet of things, including the following steps:
Taking an initial node in the elevator Internet of things system as a selected node, acquiring historical operation data of the selected node, and acquiring a historical operation data set corresponding to the selected node according to the historical operation data of the selected node;
Performing polynomial interpolation on the historical operation data set of the selected node to obtain a historical operation data track;
Detecting the active characteristic of the historical operation data track, and acquiring an active characteristic value of the historical operation data of the selected node;
Updating the corresponding communication scheduling parameters of the selected node in the communication scheduling matrix according to the active characteristic value of the historical operation data of the selected node;
And taking the next node in the elevator Internet of things system as a selected node, repeating the steps until the communication scheduling parameters in the communication scheduling matrix are updated, and adjusting the communication occupied bandwidth of each node in the elevator Internet of things system according to the updated communication scheduling matrix.
In some embodiments, obtaining the active characteristic value of the historical operating data of the selected node may specifically include:
determining a preamble trend of the historical running data track;
and determining the active characteristic value of the historical operation data of the selected node according to the preamble trend of the historical operation data track and the historical operation data set.
In some embodiments, determining the active characteristic value of the historical operational data of the selected node based on the preamble trend of the historical operational data trace and the set of historical operational data specifically includes:
determining the median of the historical operation data in the historical operation data set;
acquiring a preamble trend of a historical running data track and a standard deviation of the historical running data track;
Determining a number of historical operational data within the set of historical operational data;
determining an active characteristic value of the historical operation data according to the median of the historical operation data in the historical operation data set, the preamble trend of the historical operation data track, the standard deviation of the historical operation data track and the number of the historical operation data in the historical operation data set, wherein the active characteristic value of the historical operation data is determined according to the following formula:
wherein Q is an active characteristic value of the historical operating data, For the median of the historical operational data in the set of historical operational data, X i is the ith historical operational data of the set of historical operational data,
It should be noted that, the order of the historical operation data is based on the time sequence of the data transmission,S is the standard deviation of the historical operation data track, i is the summed intermediate variable, and n is the number of the historical operation data in the historical operation data set.
In some embodiments, the leading trend of the historical running data trace is determined by iterative calculation of standard deviations of the historical running data trace.
In some embodiments, the standard deviation of the historical running data trace may be determined using the following formula:
wherein, S [ t, t+n.sigma ] is the standard deviation of the historical operation data track in the [ t, t+n.sigma ] time period, t, t+n.sigma ] is the time period corresponding to the historical operation data track, n is the number of individuals in a sample obtained by sampling the historical operation data track, the calibration is constant, i is the intermediate variable of summation, And X (t+i.sigma) is a sample individual value of the historical running data track obtained by sampling the historical running data track at a sampling time t+i.sigma, wherein t is an initial sampling time of a sample, and sigma is a sampling interval.
In some embodiments, updating the communication scheduling parameters corresponding to the selected node in the communication scheduling matrix according to the active characteristic value of the historical operation data of the selected node may specifically include:
And taking the ratio of the active characteristic value of the historical operation data of the selected node to the standard active characteristic value of the selected node as a weight coefficient for updating the communication scheduling parameter corresponding to the selected node, and further updating the communication scheduling parameter corresponding to the selected node in the communication scheduling matrix according to the weight coefficient.
In some embodiments, the data type of the historical operating data is discrete data.
In some embodiments, the communication scheduling parameters corresponding to each node in the communication scheduling matrix are used to control the bandwidth occupied by the node when transmitting data to the internet of things communication scheduling center.
In a second aspect, the present application provides a data processing device based on the internet of things, including:
The historical operation data set acquisition module is used for taking an initial node in the elevator Internet of things system as a selected node, acquiring historical operation data of the selected node, and acquiring a historical operation data set corresponding to the selected node according to the historical operation data of the selected node;
The historical running data track acquisition module is used for performing polynomial interpolation on the historical running data set of the selected node to obtain a historical running data track;
the active characteristic value determining module is used for detecting the active characteristic of the historical operation data track and acquiring the active characteristic value of the historical operation data of the selected node;
the communication scheduling parameter updating module is used for updating the corresponding communication scheduling parameters of the selected node in the communication scheduling matrix according to the active characteristic value of the historical operation data of the selected node;
And the bandwidth adjusting module is used for taking the next node in the elevator Internet of things system as a selected node, repeating the steps until the communication scheduling parameters in the communication scheduling matrix are updated, and adjusting the communication occupied bandwidth of each node in the elevator Internet of things system according to the updated communication scheduling matrix.
In a third aspect, the application provides an elevator, which comprises the data processing device based on the internet of things.
In a fourth aspect, the present application provides a computer device, the computer device including a memory and a processor, the memory storing code, the processor being configured to obtain the code and perform the data processing method based on the internet of things.
In a fifth aspect, the present application provides a computer readable storage medium storing a computer program, which when executed by a processor, implements the data processing method based on the internet of things described above.
The technical scheme provided by the embodiment of the application has the following beneficial effects:
According to the data processing method and device based on the Internet of things, firstly, the historical operation data set of the selected node is obtained, the historical operation data set of the selected node is subjected to polynomial interpolation to obtain the historical operation data track, then the active characteristic of the historical operation data track is detected, the active characteristic value of the historical operation data of the selected node is obtained, the corresponding communication scheduling parameters of the selected node in the communication scheduling matrix are updated according to the active characteristic value of the historical operation data of the selected node, the communication occupied bandwidth of each node in the elevator Internet of things system is adjusted according to the updated communication scheduling matrix, and the occupied bandwidth of the node can be adjusted according to the data transmitted by different nodes in the elevator Internet of things system, so that the situation that the communication bandwidth is occupied when abnormal communication data exists in the node is avoided, the abnormal communication data can be found in time, and the safety of elevator operation is improved.
Drawings
FIG. 1 is an exemplary flow chart of a data processing method based on the Internet of things, according to some embodiments of the application;
FIG. 2 is a schematic diagram of exemplary hardware and/or software of an Internet of things-based data processing apparatus according to some embodiments of the present application;
Fig. 3 is a schematic structural diagram of a computer device according to some embodiments of the present application, in which a data processing method based on the internet of things is applied.
Detailed Description
Firstly, acquiring a history operation data set of a selected node, and performing polynomial interpolation on the history operation data set of the selected node to obtain a history operation data track; further detecting the active characteristic of the historical operation data track, and acquiring an active characteristic value of the historical operation data of the selected node; updating the corresponding communication scheduling parameters of the selected node in the communication scheduling matrix according to the active characteristic value of the historical operation data of the selected node; according to the updated communication scheduling matrix, the communication occupied bandwidth of each node in the elevator Internet of things system is adjusted, and the bandwidth occupied by the node can be adjusted according to data transmitted by different nodes in the elevator Internet of things system, so that the condition that the communication bandwidth is occupied when abnormal communication data exists in the node is avoided, the abnormal communication data can be found in time, and the safety of elevator operation is improved.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments. Referring to fig. 1, which is an exemplary flowchart of a data processing method based on the internet of things according to some embodiments of the present application, the data processing method 100 based on the internet of things mainly includes the following steps:
In step 101, taking an initial node in the elevator internet of things system as a selected node, and acquiring historical operation data of the selected node; and obtaining a historical operation data set corresponding to the selected node according to the historical operation data of the selected node.
The elevator internet of things system is a system for transmitting information through the Internet so as to monitor the running state of each node in the elevator, and uses the sensor, data collection and remote control technology to monitor the state and performance of each node in the elevator in real time, so that the reliability, safety and efficiency of the elevator can be improved, and more accurate data and information can be provided for maintenance and service; the internet of things communication dispatching center is a core component in the elevator internet of things system and is responsible for managing and dispatching data flow and communication among the internet of things nodes, sensors and devices, so that overall management and control of the internet of things system are realized.
In some embodiments, the node in the elevator internet of things system may be a physical device or a virtual device that needs to perform data transmission to an internet of things communication scheduling center, for example, a temperature sensor, a displacement sensor, a weighing sensor and the like on an elevator, and in specific implementation, the node in the elevator internet of things system may also be other devices or devices that need to perform data transmission in the elevator, which is not limited herein, for example, an internal band-type brake of the elevator needs to send its braking state to the internet of things center at regular time, and it is to be noted that the internet of things center is the communication scheduling center of the internet of things system, and the communication scheduling center performs remote monitoring and fault diagnosis according to the states of the nodes in the internet of things system and adopts corresponding processing measures according to different elevator fault conditions.
Preferably, the historical operation data set is a set formed by the historical operation data of the current node; it should be noted that, the historical operation data is communication data transmitted from the selected node to the internet of things center in a period of time in the past, in order to avoid occupying too much bandwidth, the selected node transmits the communication data to the internet of things dispatching center at different transmission moments at equal intervals, and partial data between the transmission moments is missing, so that the missing value needs to be estimated through the existing historical operation data, that is, the historical operation data set is interpolated, and accordingly, the historical operation data is repaired, fitted and predicted, the integrity and precision of the data are improved, and the communication dispatching of the subsequent elevator internet of things system is facilitated.
It should be noted that in some embodiments, the data type of the historical operating data of the current node is discrete data.
In step 102, polynomial interpolation is performed on the historical running data set of the selected node, so as to obtain a historical running data track.
In particular, in some preferred embodiments of the present application, the method for performing polynomial interpolation on the historical operating data set of the selected node is as follows:
Firstly, a historical operation data set of a selected node, wherein the historical operation data set is a set formed by historical operation data transmitted to an internet of things communication dispatching center by the selected node in a past preset time period, and the data type of the historical operation data is discrete data, for example, a set formed by temperature data transmitted to the internet of things communication dispatching center by an elevator temperature sensor in the past time period is discrete data type of temperature; in a specific implementation, the relationship between the historical operation data in the historical operation data set and the corresponding transmission time can be expressed as follows:
{(t1,ρ1),(t2,ρ2),(t2,ρ2)…(tn,ρn)}
The historical operation data set comprises n pieces of historical operation data, wherein t n is the nth transmission time, ρ n is the historical operation data corresponding to the nth transmission time, and for example, the historical operation data is temperature information acquired by a temperature sensor in an elevator;
secondly, interpolating the historical operation data set of the selected node to obtain a historical operation data track, wherein the historical operation data track can be obtained by interpolation according to the following mode, namely:
Wherein X (t) is a historical running data track, i, j are intermediate variables of summation and product, ρ j is historical running data corresponding to the j-th transmission moment, l j (t) is a basis function of the historical running data track, t is a time independent variable of the historical running data track, t j is the j-th transmission moment based on time sequence, and t i is the i-th transmission moment based on time sequence.
In step 103, the active characteristic of the historical operation data track is detected, and the active characteristic value of the historical operation data of the selected node is obtained.
It should be noted that, the active characteristic value of the historical operation data reflects the active degree of the historical operation data of the selected node in a past preset time period, and because the communication data transmitted by the node tends to change gently in a short time in the normal operation process of the elevator, the active characteristic of the historical operation data track is detected, so that the occupation ratio condition of the abnormal communication data of the selected node in the past period in the historical operation data can be known, and further the corresponding communication scheduling parameters of the selected node in the communication scheduling matrix are updated according to the quantity of the abnormal communication data, so that more communication bandwidths are provided for the nodes with more abnormal communication data, the data transmission precision of the node is improved, and therefore whether safety problems and problem types exist in the node or not is determined more quickly, the operation safety of the elevator is ensured, and the communication scheduling of the elevator internet of things system is realized.
In some embodiments, the active feature values of the historical operating data of the selected node may be obtained by:
determining a preamble trend of the historical running data track;
and determining the active characteristic value of the historical operation data of the selected node according to the preamble trend of the historical operation data track and the historical operation data set.
In addition, in some embodiments, the preamble trend of the historical running data track may be determined by iterative calculation of standard deviation of the historical running data track, and in a specific implementation, the following manner may be adopted, namely:
Firstly, acquiring standard deviation of the historical running data track;
Secondly, carrying out iterative computation according to the standard deviation of the historical operation data track and the information entropy of the historical operation data of the current node, and determining the preamble trend of the historical operation data track, wherein the initial moment of the historical operation data track is taken as a zero moment value, and the iterative computation is realized according to the following formula, namely:
wherein, For the trend of the preamble of the historical running data track over the [0, t ] time period, t is the time length of the historical running data track of the corresponding time period, sigma is the time period for iteration, S [0, t-sigma ] is the standard deviation of the historical running data track over the [0, t-sigma ] time period,/>For the precursor trend of the historical operation data track in the [0, t-sigma ] time period, S [0, t ] is the standard deviation of the historical operation data track in the [0, t ] time period, k is the information entropy of the historical operation data set of the selected node, and e is the natural logarithm.
It should be noted that, in some embodiments, the information entropy of the historical operation data set for the selected node may be determined by: firstly, probability distribution of different historical operation data in a historical operation data set is obtained, and then information entropy of the historical operation data set is obtained according to the probability distribution.
In addition, in some embodiments, determining the standard deviation of the historical running data track may further include: sampling the historical running data track at equal intervals, wherein the sampling interval is sigma, a sample of the historical running data track is obtained, and the sample capacity is n; the standard deviation of the historical running data track can be calculated by using a sample standard deviation formula of continuous variables, for example, for a continuous period of the historical running data track with the time length of n.sigma from the time t, the standard deviation can be calculated by using the following formula:
wherein, S [ t, t+n.sigma ] is the standard deviation of the historical operation data track in the [ t, t+n.sigma ] time period, t, t+n.sigma ] is the time period corresponding to the historical operation data track, n is the number of individuals in a sample obtained by sampling the historical operation data track, the calibration is constant, i is the intermediate variable of summation, And X (t+i.sigma) is a sample individual value of the historical running data track obtained by sampling the historical running data track at a sampling time t+i.sigma, wherein t is an initial sampling time of a sample, and sigma is a sampling interval.
It should be noted that, in some embodiments, the standard deviation of the historical operation data track is mainly used to reflect the discrete degree of the historical operation data of the selected node, but the standard deviation is too large if the selected node only outputs an abnormal communication data with a larger deviation degree (jump), and in some embodiments, the weight ratio occupied by the number of the abnormal communication data in the historical operation data in the calculation of the active characteristic value needs to be increased, so that after the historical operation data track is iterated, the larger jump type abnormal communication data is filtered through continuous average iteration to obtain a preamble trend, and then the active characteristic value is determined according to the preamble trend of the historical operation data track.
In addition, the trend of the preamble of the historical operation data track is the standard deviation obtained by filtering out larger jump type error information through continuous iterative averaging after the historical operation data track is iterated, so that the change trend of the discrete degree of the historical operation data track in the time period of the preamble can be reflected more accurately, and the weight coefficient in the process of calculating the active characteristic value of the historical operation data is adjusted according to the trend of the preamble, so that the activity degree of the historical operation data track is determined more accurately.
In some embodiments, the determination of the active characteristic value of the historical operating data in the present application may be implemented in the following manner, that is:
determining the median of the historical operation data in the historical operation data set;
acquiring a preamble trend of a historical running data track and a standard deviation of the historical running data track;
Determining a number of historical operational data within the set of historical operational data;
determining an active characteristic value of the historical operation data according to the median of the historical operation data in the historical operation data set, the preamble trend of the historical operation data track, the standard deviation of the historical operation data track and the number of the historical operation data in the historical operation data set, wherein the active characteristic value of the historical operation data is determined according to the following formula:
wherein Q is an active characteristic value of the historical operating data, For the median of the historical operational data in the set of historical operational data, X i is the ith historical operational data of the set of historical operational data,
It should be noted that, the order of the historical operation data is based on the time sequence of the data transmission,S is the standard deviation of the historical operation data track, i is the summed intermediate variable, and n is the number of the historical operation data in the historical operation data set.
It should be noted that the standard deviation describes the fluctuation of discrete communication data, and if the fluctuation of the historical operation data is severe, the standard deviation is higher, and the sum of squares term of the relative difference of the same historical operation dataThe corresponding active characteristic value is pulled down, the adaptability to the history running data with intense fluctuation is improved, in addition, the active characteristic value of the history running data is calculated by adopting a piecewise function, the precursor trend can replace the standard deviation to calculate the active characteristic value when the standard deviation of the history running data is zero or extremely low, the meaningless calculation result is prevented, and the square sum term/>, of the small-amplitude relative difference value when the active characteristic value is obtained, can be avoidedThe problem of too sensitive variation.
In addition, since the median of the historical operating data of the selected node is less affected by the data fluctuation amplitude than the average value, the median of the historical operating data in the set of historical operating data may be selected as a comparison benchmark in the determination of the active characteristic value of the historical operating data in some embodiments because the median of the historical operating data is not pulled up by the larger data amplitude.
In step 104, according to the active characteristic value of the historical operation data of the selected node, the corresponding communication scheduling parameter of the selected node in the communication scheduling matrix is updated.
The communication scheduling matrix plays an important role in the elevator Internet of things system, helps a scheduling center to effectively manage communication traffic among nodes, improves communication efficiency and resource utilization rate, and ensures timely transmission of key communication, so that reliability and performance of the system are improved.
In some embodiments, the communication scheduling matrix is a control matrix for controlling occupied bandwidth and priority of each node in the elevator internet of things system, and each node has a corresponding communication scheduling parameter in the communication scheduling matrix, where the communication scheduling parameter is used to control occupied bandwidth when the corresponding node transmits data to the internet of things communication scheduling center, so as to implement effective communication scheduling and resource management. Through the communication scheduling matrix, the communication scheduling center of the elevator Internet of things system can perform bandwidth allocation and scheduling according to the communication requirements and priorities among the nodes, and can judge which communication requests should be processed preferentially according to the communication scheduling parameters in the matrix, so that real-time transmission of key data and communication with lower priority can not cause interference to the key communication.
In specific implementation, the ratio of the active characteristic value of the selected node to the standard active characteristic value of the selected node can be used as a weight coefficient for updating the communication scheduling parameter corresponding to the selected node; furthermore, the communication scheduling parameters corresponding to the selected node in the communication scheduling matrix are updated according to the weight coefficient, and an embodiment is listed below to describe a process of updating the communication scheduling parameters in the communication scheduling matrix, for example, in an elevator internet of things system with 25 nodes, the communication scheduling matrix of the elevator internet of things system may be expressed as follows:
A is a communication scheduling matrix of the elevator Internet of things system, a 11 is a first row and first column element in the communication scheduling matrix, and the element corresponds to a communication scheduling parameter of a node in the elevator Internet of things system and is used for controlling bandwidth occupied by the node when transmitting data to an Internet of things communication scheduling center.
In a specific implementation, for example, the temperature sensor in the elevator is a node in the elevator internet of things system, and the communication scheduling parameter corresponding to the node in the communication scheduling matrix is the second row and fourth column element a 24, then the process of updating the communication scheduling parameter corresponding to the node in the communication scheduling matrix can be implemented by adopting the following formula, namely:
wherein a is a communication scheduling matrix before updating, a * is a communication scheduling matrix after updating communication scheduling parameters of a node corresponding to a temperature sensor in an elevator, Q is an active characteristic value of historical operation data of the temperature sensor in the elevator, Q 0 is a standard characteristic active value of the selected node, Q/Q 0 is a weight coefficient updated by the communication scheduling parameters corresponding to the selected node, and in some embodiments, the active characteristic value of historical communication data of a previous round of scheduling period of the selected node may also be used as a standard characteristic active value of the node.
In step 105, taking the next node in the elevator internet of things system as a selected node, repeating the above steps until the communication scheduling parameters in the communication scheduling matrix are updated; and according to the updated communication scheduling matrix, adjusting the communication occupied bandwidth of each node in the elevator Internet of things system.
It should be noted that, in this step, the next node is the next node based on the node sequence in the elevator internet of things system, in some embodiments, the node sequence in the elevator internet of things system is preset, when the communication scheduling parameters in the communication scheduling matrix are updated, the internet of things system controls the bandwidth occupied by each node in the elevator internet of things system when communicating according to the communication scheduling matrix, at this time, a round of scheduling period is completed, when the communication scheduling center of the elevator internet of things system enters the next round of scheduling period, for example, in particular implementation, after the corresponding communication scheduling parameters of a certain node in the communication scheduling matrix are reduced, the communication interval when the node transmits data is increased, thereby reducing the bandwidth occupied by the node in the internet of things system, so that the elevator internet of things has more bandwidth margin, improving the transmission speed and the accuracy of abnormal communication data of the elevator internet of things, avoiding the situation that the communication bandwidth is occupied when the node has abnormal communication data, so that the abnormal communication data can be found in time, and improving the safety of elevator operation.
Additionally, with reference to FIG. 2, which is a schematic diagram of exemplary hardware and/or software of an Internet of things-based data processing apparatus 200 according to some embodiments of the present application, the Internet of things-based data processing apparatus 200 includes: the historical operation data set acquisition module 201, the historical operation data track acquisition module 202, the activity characteristic value determination module 203, the communication scheduling parameter updating module 204 and the bandwidth adjustment module 205 are respectively described as follows:
The historical operation data set acquisition module 201 is mainly used for taking an initial node in the elevator internet of things system as a selected node to acquire historical operation data of the selected node; according to the historical operation data of the selected node, obtaining a historical operation data set corresponding to the selected node;
The historical running data track acquisition module 202 is mainly used for performing polynomial interpolation on the historical running data set of the selected node to obtain a historical running data track;
active feature value determination module 203: the active characteristic value determining module 203 is mainly used for detecting the active characteristic of the historical operation data track and acquiring the active characteristic value of the historical operation data of the selected node;
the communication scheduling parameter updating module 204 is mainly used for updating the corresponding communication scheduling parameters of the selected node in the communication scheduling matrix according to the active characteristic value of the historical operation data of the selected node;
The bandwidth adjusting module 205, in the present application, the bandwidth adjusting module 205 is mainly configured to take a next node in the elevator internet of things system as a selected node, and repeat the above steps until the communication scheduling parameters in the communication scheduling matrix are updated; and according to the updated communication scheduling matrix, adjusting the communication occupied bandwidth of each node in the elevator Internet of things system.
In other aspects of the application, an elevator is further provided, which comprises the data processing device based on the internet of things, so that the bandwidth occupied by the node can be adjusted according to the data transmitted by different nodes in the system of the internet of things of the elevator, thereby avoiding the condition that the communication bandwidth is occupied when abnormal communication data exists in the node, enabling the abnormal communication data to be found in time, and improving the operation safety of the elevator.
In addition, the application also provides a computer device, which comprises a memory and a processor; the memory stores codes, and the processor is configured to acquire the codes and execute the data processing method based on the Internet of things.
In some embodiments, reference is made to fig. 3, which is a schematic structural diagram of a computer device implementing a data processing method based on the internet of things according to some embodiments of the present application. The data processing method based on the internet of things in the above embodiment may be implemented by a computer device shown in fig. 3, where the computer device includes at least one processor 301, a communication bus 302, a memory 303, and at least one communication interface 304.
Processor 301 may be a general purpose central processing unit (central processing unit, CPU), application-specific integrated circuit (ASIC), or one or more of the data processing methods for controlling the internet of things in the present application.
Communication bus 302 may include a path to transfer information between the above components.
The Memory 303 may be, but is not limited to, a read-only Memory (ROM) or other type of static storage device that can store static information and instructions, a random access Memory (random access Memory, RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only Memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ-only Memory, EEPROM), a compact disc (compact disc read-only Memory) or other optical disk storage, optical disk storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory 303 may be stand alone and be coupled to the processor 301 via the communication bus 302. Memory 303 may also be integrated with processor 301.
The memory 303 is used for storing program codes for executing the scheme of the present application, and the processor 301 controls the execution. The processor 301 is configured to execute program code stored in the memory 303. One or more software modules may be included in the program code. The determination of the active characteristic values in the above embodiments may be implemented by one or more software modules in the processor 301 and in the program code in the memory 303.
Communication interface 304, using any transceiver-like device for communicating with other devices or communication networks, such as ethernet, radio access network (radio access network, RAN), wireless local area network (wireless local area networks, WLAN), etc.
In a specific implementation, as an embodiment, a computer device may include a plurality of processors, where each of the processors may be a single-core (single-CPU) processor or may be a multi-core (multi-CPU) processor. A processor herein may refer to one or more devices, circuits, and/or processing cores for processing data (e.g., computer program instructions).
The computer device may be a general purpose computer device or a special purpose computer device. In a specific implementation, the computer device may be a desktop, a laptop, a web server, a personal computer (PDA), a mobile handset, a tablet, a wireless terminal device, a communication device, or an embedded device. Embodiments of the application are not limited to the type of computer device.
In addition, the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the data processing method based on the Internet of things when being executed by a processor.
In summary, in the data processing method and device based on the Internet of things, firstly, a history operation data set of a selected node is acquired, and a history operation data track is obtained after polynomial interpolation is carried out on the history operation data set of the selected node; further detecting the active characteristic of the historical operation data track, and acquiring an active characteristic value of the historical operation data of the selected node; updating the corresponding communication scheduling parameters of the selected node in the communication scheduling matrix according to the active characteristic value of the historical operation data of the selected node; according to the updated communication scheduling matrix, the communication occupied bandwidth of each node in the elevator Internet of things is adjusted, and the occupied bandwidth of each node can be adjusted according to data transmitted by different nodes in the elevator Internet of things, so that the situation that the communication bandwidth is occupied when abnormal communication data exists in the nodes is avoided, the abnormal communication data can be found in time, and the safety of elevator operation is improved.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (6)
1. The data processing method based on the Internet of things is characterized by comprising the following steps of:
taking an initial node in an elevator Internet of things system as a selected node, acquiring historical operation data of the selected node, and acquiring a historical operation data set corresponding to the selected node according to the historical operation data of the selected node;
Performing polynomial interpolation on the historical operation data set of the selected node to obtain a historical operation data track;
Detecting the active characteristic of the historical operation data track, and acquiring an active characteristic value of the historical operation data of the selected node;
Updating the corresponding communication scheduling parameters of the selected node in the communication scheduling matrix according to the active characteristic value of the historical operation data of the selected node;
Taking the next node in the elevator Internet of things system as a selected node, repeating the steps until the communication scheduling parameters in the communication scheduling matrix are updated, and adjusting the communication occupied bandwidth of each node in the elevator Internet of things system according to the updated communication scheduling matrix;
the step of obtaining the active characteristic value of the historical operation data of the selected node specifically comprises the following steps:
determining a preamble trend of the historical running data track;
determining an active characteristic value of the historical operation data of the selected node according to the preamble trend of the historical operation data track and the historical operation data set;
The precursor trend of the historical running data track is determined through standard deviation iterative calculation of the historical running data track;
The determining the active characteristic value of the historical operation data of the selected node according to the preamble trend of the historical operation data track and the historical operation data set specifically comprises:
determining the median of the historical operation data in the historical operation data set;
acquiring a preamble trend of a historical running data track and a standard deviation of the historical running data track;
Determining a number of historical operational data within the set of historical operational data;
determining an active characteristic value of the historical operation data according to the median of the historical operation data in the historical operation data set, the preamble trend of the historical operation data track, the standard deviation of the historical operation data track and the number of the historical operation data in the historical operation data set, wherein the active characteristic value of the historical operation data is determined according to the following formula:
wherein Q is an active characteristic value of the historical operating data, For the median of the historical operation data in the historical operation data set, X i is the ith historical operation data of the historical operation data set, and the sequence of the historical operation data is based on the time sequence of the data transmission,/>S is the standard deviation of the historical operation data track, i is a summed intermediate variable, and n is the number of the historical operation data in the historical operation data set;
The method for updating the communication scheduling parameters corresponding to the selected node in the communication scheduling matrix specifically comprises the following steps of:
And taking the ratio of the active characteristic value of the historical operation data of the selected node to the standard active characteristic value of the selected node as a weight coefficient for updating the communication scheduling parameter corresponding to the selected node, and further updating the communication scheduling parameter corresponding to the selected node in the communication scheduling matrix according to the weight coefficient.
2. The method of claim 1, wherein the standard deviation of the historical motion data trace is determined using the following formula:
wherein, S [ t, t+n.sigma ] is the standard deviation of the historical operation data track in the [ t, t+n.sigma ] time period, t, t+n.sigma ] is the time period corresponding to the historical operation data track, n is the number of individuals in a sample obtained by sampling the historical operation data track, the calibration is constant, i is the intermediate variable of summation, And X (t+i.sigma) is a sample individual value of the historical running data track obtained by sampling the historical running data track at a sampling time t+i.sigma, wherein t is an initial sampling time of a sample, and sigma is a sampling interval.
3. The method of claim 1, wherein the communication schedule parameters for each node in the communication schedule matrix are used to control bandwidth occupied by the node when transmitting data to the internet of things communication schedule center.
4. A data processing device based on the internet of things, which is processed by the method according to any one of claims 1 to 3, characterized in that the data processing device based on the internet of things comprises:
The system comprises a historical operation data set acquisition module, a control module and a control module, wherein the historical operation data set acquisition module is used for taking an initial node in an elevator Internet of things system as a selected node, acquiring historical operation data of the selected node, and acquiring a historical operation data set corresponding to the selected node according to the historical operation data of the selected node;
The historical running data track acquisition module is used for performing polynomial interpolation on the historical running data set of the selected node to obtain a historical running data track;
the active characteristic value determining module is used for detecting the active characteristic of the historical operation data track and acquiring the active characteristic value of the historical operation data of the selected node;
the communication scheduling parameter updating module is used for updating the corresponding communication scheduling parameters of the selected node in the communication scheduling matrix according to the active characteristic value of the historical operation data of the selected node;
And the bandwidth adjusting module is used for taking the next node in the elevator Internet of things system as a selected node, repeating the steps until the communication scheduling parameters in the communication scheduling matrix are updated, and adjusting the communication occupied bandwidth of each node in the elevator Internet of things system according to the updated communication scheduling matrix.
5. An elevator, characterized in that it comprises a data processing device based on the internet of things as claimed in claim 4.
6. A computer-readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the data processing method based on the internet of things as claimed in any one of claims 1 to 3.
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