An internet of things front-end data acquisition method

Abstract

The application discloses an Internet of Things front-end data collection method, and particularly relates to the technical field of data collection, which comprises the following steps: sending data collected by an Internet of Things front-end device to a gateway, analyzing the collected data by the gateway, determining trend information and change information of the Internet of Things front-end data in a monitoring interval, adjusting the transmission frequency of the Internet of Things front-end data uploaded from the gateway to a cloud platform through comprehensive analysis of the trend information and the change information of the Internet of Things front-end data, analyzing historical data of different types of Internet of Things front-end data and security problems existing in the actual application of different types of Internet of Things front-end data, collecting uncertainty information and risk information of different types of Internet of Things front-end data, and determining the priority of sending different types of Internet of Things front-end data at the gateway. The application helps to intelligently adjust the data transmission strategy, thereby improving the security and stability of the system and effectively reducing the computing pressure of the cloud platform.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data collection, and more particularly, to a front-end data collection method for Internet of Things. BACKGROUND

[0002] With the rapid development of Internet of Things technology, various front-end devices of Internet of Things are widely used in industrial, agricultural, urban management, health monitoring and other fields. These devices collect environmental parameters, device status and other data and upload them to the cloud platform to support intelligent decision-making and real-time monitoring.

[0003] In the Internet of Things, front-end devices may be widely distributed and large in number. If each device directly sends data to the cloud platform, the cloud platform will receive a large number of independent data streams, which may cause network bandwidth tension and high load of the cloud platform. Through the gateway, data from multiple devices can be aggregated or combined into fewer data packets and then sent to the cloud platform, which significantly reduces the complexity and amount of data transmission. However, most Internet of Things systems fail to effectively evaluate and control the priority of data upload, resulting in some high-risk and important data not being processed in time, while a large amount of low-priority data occupies transmission bandwidth and computing resources, reducing the overall performance of the system.

[0004] In order to solve the above-mentioned defects, the present application provides a technical solution. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a front-end data collection method for Internet of Things to solve the problems raised in the background art.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solution:

[0007] A front-end data collection method for Internet of Things, specifically comprising the following steps:

[0008] S1: sending the data collected by the front-end device of Internet of Things to the gateway, and analyzing the collected data by the gateway to determine the trend information and change information of the front-end data of Internet of Things in the monitoring interval;

[0009] S2: adjusting the transmission frequency of the front-end data of Internet of Things from the gateway to the cloud platform through comprehensive analysis of the trend information and change information of the front-end data of Internet of Things;

[0010] S3: collecting the uncertainty information and risk information of different types of front-end data of Internet of Things by analyzing the historical data of different types of front-end data of Internet of Things and the security problems existing in the actual application of different types of front-end data of Internet of Things;

[0011] S4: comprehensive evaluation of the uncertainty information and risk information of different types of Internet of Things front-end data, and determination of the priority of sending different types of Internet of Things front-end data at the gateway.

[0012] In a preferred embodiment, the trend information of the Internet of Things front-end data in the monitoring interval is determined, including:

[0013] The change information of the Internet of Things front-end data is represented by a high-frequency filtering amplitude coefficient and a data regularization similarity coefficient.

[0014] The acquisition logic of the high-frequency filtering amplitude coefficient is: obtaining the Internet of Things front-end data in the monitoring interval, and analyzing the Internet of Things front-end data in the monitoring interval in time domain and frequency domain through wavelet transform. The expression of the Internet of Things front-end data in the monitoring interval through wavelet transform is: Wherein, WL is the data of the Internet of Things front-end data in the monitoring interval through wavelet transform, a is the scale of the wavelet function, b is the translation parameter, is the wavelet function.

[0015] The frequency difference coefficient is calculated by determining the peak frequency and the center frequency of the wavelet-transformed Internet of Things front-end data. The calculation formula is: pl CY = |argmax(WL) - WL zx |; wherein, CY pl is the frequency difference coefficient, WL zx is the center frequency of the wavelet-transformed Internet of Things front-end data.

[0016] A wavelet coefficient threshold is set, and a high-frequency filtering ratio coefficient is calculated. The calculation formula is: Wherein, BZ gp is the high-frequency filtering ratio coefficient, t c ~ t d is the time period greater than the wavelet coefficient threshold.

[0017] The high-frequency filtering amplitude coefficient is calculated. The calculation formula is: gp FZ = CY pl × BZ gp ; wherein, FZ gp is the high-frequency filtering amplitude coefficient.

[0018] In a preferred embodiment, the data regularization similarity coefficient includes:

[0019] The acquisition logic of the data regularization similarity coefficient is: obtaining the Internet of Things front-end data in the current monitoring interval, and marking the Internet of Things front-end data in the current monitoring interval as: n , n = 1, 2, 3, …, N, N is a positive integer, and n is the number of different time Internet of Things front-end data accepted by the gateway in the current monitoring interval.

[0020] Obtaining the Internet of Things front-end data in the last monitoring interval, marking the Internet of Things front-end data in the last monitoring interval as SJ i , i = 1, 2, 3, …, I, I is a positive integer, i is the number of the Internet of Things front-end data received by the gateway at different times in the last monitoring interval;

[0021] Constructing a cost matrix, taking the absolute difference of the Internet of Things front-end data at each time in the current monitoring interval and the last monitoring interval as the distance measure, determining each element in the cost matrix, and marking the cost matrix as C(n, i), C(n, i) = |SL n -SL i |;

[0022] Constructing a cumulative cost matrix, recording the minimum total cost from the starting point to each point, and marking the cumulative cost matrix as D(n, i), D(n, i) = C(n, i) + min(D(n-1, i), D(n, i-1), D(n-1, i-1));

[0023] Determining the DTW distance through recursive accumulation calculation, obtaining the last element of the cumulative cost matrix, calculating the data regularization similarity coefficient, and the expression is: GZ xs = D(N, I); wherein, GZ xs is the data regularization similarity coefficient.

[0024] In a preferred embodiment, the change information of the Internet of Things front-end data in the monitoring interval is determined, including:

[0025] The change information of the Internet of Things front-end data is represented by a time series prediction coefficient;

[0026] The acquisition logic of the time series prediction coefficient is: collecting historical Internet of Things front-end data, taking the Internet of Things front-end data in multiple monitoring intervals in the historical Internet of Things front-end data as training data, and dividing the training data into input training data and prediction training data, training the autoregressive moving average model according to the input training data and the prediction training data, and estimating the regression coefficient and the moving average coefficient of the autoregressive moving average model through the least squares method;

[0027] Based on the trained autoregressive moving average model, the Internet of Things front-end data input into the autoregressive moving average model in the monitoring interval is determined, the predicted value of the Internet of Things front-end data is obtained, and the predicted value of the Internet of Things front-end data is marked as YC m , wherein m = 1, 2, 3, …, M, M is a positive integer, and m is the number of the predicted Internet of Things front-end data;

[0028] The actual value of the Internet of Things front-end data in the current monitoring interval is determined, and the actual value of the Internet of Things front-end data in the current monitoring interval is marked as SJ m ;

[0029] The time series prediction coefficient is calculated, and the calculation formula is: XL yc is the time series prediction coefficient.

[0030] In a preferred embodiment, the transmission frequency of the Internet of Things front-end data uploaded from the gateway to the cloud platform is adjusted, including:

[0031] Through comprehensive analysis of the change information and trend information of the Internet of Things front-end data, the high-frequency filtering amplitude coefficient, the data regularization similarity coefficient and the time series prediction coefficient are weighted and calculated, a transmission evaluation model is constructed, and a transmission evaluation coefficient is generated, and the calculation formula is: pg cs is the transmission evaluation coefficient, and α1, α2 and α3 are proportional coefficients of the high-frequency filtering amplitude coefficient, the data regularization similarity coefficient and the time series prediction coefficient, respectively, and α1, α2 and α3 are all greater than 0.

[0032] The transmission evaluation coefficient threshold is set, the transmission evaluation coefficient threshold is compared with the transmission evaluation coefficient, if the transmission evaluation coefficient threshold is greater than the transmission evaluation coefficient, the acquisition signal is generated, the pre-set mode of collecting the Internet of Things front-end data is used to collect the Internet of Things front-end data, and transmit to the cloud platform, if the transmission evaluation coefficient threshold is less than the transmission evaluation coefficient, the acquisition signal is not generated, and the current transmission rate of the Internet of Things front-end data to the cloud platform is maintained.

[0033] In a preferred embodiment, the uncertainty information of different kinds of Internet of Things front-end data is collected, including:

[0034] The uncertainty information of the Internet of Things front-end data is represented by an entropy instability coefficient;

[0035] The acquisition logic of the entropy instability coefficient is: according to the historical data of the Internet of Things front-end data, the difference value of adjacent data points of the Internet of Things front-end data is determined, and the difference value of adjacent data points of the Internet of Things front-end data is marked as CZ j , wherein j=1, 2, 3, …, J, J is a positive integer, and j is the number of adjacent data points in the historical data of the Internet of Things front-end data;

[0036] According to the difference value of adjacent data points of the Internet of Things front-end data, the probability distribution of the difference value of adjacent data points is determined by using kernel density estimation, and the calculation formula is: P(CZ) is the probability distribution of the difference value of adjacent data points, K is the kernel function, and h is the smoothing parameter.

[0037] The entropy value instability coefficient is calculated according to the following formula: Wherein, SZ wd is the entropy value instability coefficient.

[0038] In a preferred embodiment, the risk information of different types of Internet of Things front-end data is collected, including:

[0039] The risk information of the Internet of Things front-end data is represented by a risk concealment evaluation coefficient;

[0040] The risk concealment evaluation coefficient is obtained by determining the risk events that may be triggered by each type of Internet of Things front-end data after uploading to the cloud platform and determining the risk events triggered by different types of Internet of Things front-end data based on the historical records of different types of Internet of Things front-end data, and then scoring different risk events based on different risk events through the expert equal weight principle to determine the risk score of each type of Internet of Things front-end data.

[0041] A logistic regression model is constructed based on the risk score of the risk events triggered by the Internet of Things front-end data and the proportion of the risk events triggered by the Internet of Things front-end data in all risk events, and the risk concealment evaluation coefficient is obtained, and the calculation formula of the risk concealment evaluation coefficient is:

[0042]

[0043] Wherein, FX yn is the risk concealment evaluation coefficient, q = 1, 2, 3, …, Q, Q is a positive integer, q is the number of the risk events triggered by the Internet of Things front-end data, PF1, PF2, PF3, …, PF Q is the risk score of the risk events triggered by the Internet of Things front-end data, BZ1, BZ2, BZ3, …, BZ Q is the proportion of the risk events triggered by the Internet of Things front-end data in all risk events, and e is a natural number.

[0044] In a preferred embodiment, the priority of sending different types of Internet of Things front-end data at the gateway is determined, including:

[0045] The entropy value instability coefficient and the risk concealment evaluation coefficient are weighted and calculated based on the uncertainty information and the risk information of different types of Internet of Things front-end data, a priority evaluation model is constructed, and a priority evaluation coefficient is generated, and the calculation formula is: PG yx = β1SZ wd + β2FX yn ; wherein, PG yxThe priority evaluation coefficients of different types of Internet of Things front-end data are sorted from large to small, and the front-end data of the Internet of Things with high priority is uploaded first.

[0046] The priority evaluation coefficients of different types of Internet of Things front-end data are sorted from large to small, and the front-end data of the Internet of Things with high priority is uploaded first.

[0047] The technical effects and advantages of the present application are as follows:

[0048] The present application analyzes the Internet of Things front-end data at the gateway, extracts the trend information and change information of the Internet of Things front-end data in the monitoring interval, dynamically adjusts the transmission frequency of the Internet of Things front-end data uploaded to the cloud platform based on the comprehensive analysis of the trend information and change information, and allocates priority to different types of Internet of Things front-end data by analyzing their historical data and security problems in actual application. The present application helps to intelligently adjust the data transmission strategy, thereby improving the security and stability of the system, and effectively reducing the computing pressure of the cloud platform. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to facilitate the understanding of those skilled in the art, the present application will be further described below with reference to the accompanying drawings;

[0050] Figure 1 The flowchart of the present application is a method for collecting Internet of Things front-end data. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0052] Embodiment 1

[0053] Figure 1 The flowchart of the present application is a method for collecting Internet of Things front-end data, which specifically includes the following steps:

[0054] S1: The data collected by the Internet of Things front-end device is sent to the gateway, and the collected data is analyzed by the gateway to determine the trend information and change information of the Internet of Things front-end data in the monitoring interval;

[0055] S2: Adjust the transmission frequency of the Internet of Things front-end data uploaded from the gateway to the cloud platform through comprehensive analysis of the trend information and change information of the Internet of Things front-end data;

[0056] S3: Collect the uncertainty information and risk information of different types of Internet of Things front-end data by analyzing the historical data of different types of Internet of Things front-end data and the security problems existing in the actual application of different types of Internet of Things front-end data.

[0057] S4: Comprehensive evaluation of the uncertainty information and risk information of different types of Internet of Things front-end data to determine the priority of sending different types of Internet of Things front-end data at the gateway.

[0058] The architecture of the Internet of Things system usually includes multiple levels of devices, sensors, gateways and cloud platforms, the interaction and data flow between each level is the core of the Internet of Things data transmission, the collection of Internet of Things front-end data is usually carried out through sensors, devices or terminals, these devices are usually installed in the physical environment to monitor specific physical quantities, after the collection of Internet of Things front-end data, it needs to be transmitted to the downstream cloud platform through appropriate transmission methods.

[0059] Internet of Things front-end data collection: Internet of Things front-end data is raw data generated by various sensors, intelligent devices, machines and other devices, the type of front-end data can be temperature, humidity, location, light, acceleration, etc., sensors continuously collect data, generate digitized signals, and transmit them to the gateway through certain communication protocols;

[0060] Gateway layer: The gateway is mainly responsible for collecting, processing, filtering, compressing and forwarding the data collected by distributed front-end devices, and also serves as a bridge between Internet of Things devices and cloud platforms or other downstream systems;

[0061] Cloud platform layer: The cloud platform is the core of Internet of Things data storage and processing, Internet of Things front-end devices upload data to the cloud platform through the gateway, the cloud platform receives, stores and further processes and analyzes the data, the cloud platform usually has powerful data processing, storage and analysis capabilities, and can handle massive data generated by thousands of devices.

[0062] Among them, the gateway is responsible for receiving data from Internet of Things front-end devices and performing preliminary analysis and processing, controlling the transmission frequency of different types of Internet of Things front-end data, analyzing the Internet of Things front-end data in the monitoring interval, collecting the change information and trend information of the Internet of Things front-end data, representing the change information of the Internet of Things front-end data through high-frequency filtering amplitude coefficient and data regularization similarity coefficient, and representing the trend information of the Internet of Things front-end data through time series prediction coefficient.

[0063] It should be noted that the monitoring interval is a specific time period, which is set by professional staff, and the sending frequency of the Internet of Things front-end data is usually high, so there are multiple time points of the Internet of Things front-end data in the monitoring interval.

[0064] The acquisition logic of the high-frequency filtering amplitude coefficient is: obtaining the Internet of Things front-end data in the monitoring interval, analyzing the Internet of Things front-end data in the monitoring interval in time domain and frequency domain through wavelet transform, and expressing the Internet of Things front-end data in the monitoring interval through wavelet transform expression as: Wherein, WL is the data of the Internet of Things front-end data in the monitoring interval through wavelet transform, a is the scale of the wavelet function, b is the translation parameter, is the wavelet function.

[0065] It should be noted that the translation parameter b of the wavelet function is determined according to the length of the monitoring interval, and the wavelet function and the scale a of the wavelet function are determined by professional staff. Based on the characteristics of different types of Internet of Things front-end data, if the Internet of Things front-end data is usually stable in historical data, a larger scale is selected, and if the Internet of Things front-end data is usually fluctuating in historical data, a smaller scale is selected.

[0066] By determining the peak frequency and center frequency of the wavelet transform of the Internet of Things front-end data, the frequency difference coefficient is calculated, and the calculation formula is: pl CY = |argmax(WL) - WL zx |; wherein, CY pl is the frequency difference coefficient, WL zx is the center frequency of the wavelet transform of the Internet of Things front-end data.

[0067] Set the wavelet coefficient threshold, calculate the high-frequency filtering ratio coefficient, and the calculation formula is: Wherein, BZ gp is the high-frequency filtering ratio coefficient, t c ~ t d is the time period greater than the wavelet coefficient threshold.

[0068] It should be noted that the wavelet coefficient threshold is set by professional staff and adjusted based on different types of Internet of Things front-end data.

[0069] Calculate the high-frequency filtering amplitude coefficient, and the calculation formula is: FZ gp = CY pl × BZ gp ; wherein, FZ gp is the high-frequency filtering amplitude coefficient.

[0070] From the formula, the greater the high-frequency filtering amplitude coefficient, the greater the possible change of the Internet of Things front-end data, thus, the transmission frequency of the Internet of Things front-end data can be increased, which is helpful for the cloud platform to analyze the transmitted Internet of Things front-end data in time and make corresponding decisions.

[0071] The acquisition logic of the data regularization similarity coefficient is: acquiring the Internet of Things front-end data in the current monitoring interval, marking the Internet of Things front-end data in the current monitoring interval as SJ n , n = 1, 2, 3, …, N, N is a positive integer, and n is the number of the Internet of Things front-end data received by the gateway at different times in the current monitoring interval;

[0072] Obtaining the Internet of Things front-end data in the last monitoring interval, marking the Internet of Things front-end data in the last monitoring interval as SJ i , i = 1, 2, 3, …, I, I is a positive integer, and i is the number of the Internet of Things front-end data received by the gateway at different times in the last monitoring interval;

[0073] Building a cost matrix, taking the absolute difference of the Internet of Things front-end data at each time in the current monitoring interval and the last monitoring interval as a distance measure, determining each element in the cost matrix, and marking the cost matrix as C(n, i), C(n, i) = |SL n -SL i |;

[0074] Building a cumulative cost matrix, which records the minimum total cost from the starting point to each point, marking the cumulative cost matrix as D(n, i), D(n, i) = C(n, i) + min(D(n-1, i), D(n, i-1), D(n-1, i-1));

[0075] Through recursive accumulation calculation, determining the DTW distance, obtaining the last element of the cumulative cost matrix, calculating the data regularization similarity coefficient, and the expression is: GZ xs = D(N, I); wherein, GZ xs is the data regularization similarity coefficient.

[0076] From the formula, the greater the data regularization similarity coefficient, the greater the difference between the Internet of Things front-end data in the current monitoring interval and the Internet of Things front-end data in the last monitoring interval, thus, the transmission frequency of the Internet of Things front-end data can be increased, and the Internet of Things front-end data in the current monitoring interval can be uploaded to the cloud platform at a higher frequency.

[0077] The acquisition logic of the time series prediction coefficient is: collecting historical Internet of Things front-end data, taking the Internet of Things front-end data in multiple monitoring intervals in the historical Internet of Things front-end data as training data, and dividing the training data into input training data and prediction training data, training the autoregressive moving average model according to the input training data and the prediction training data, and estimating the regression coefficient and the moving average coefficient of the autoregressive moving average model by the least square method;

[0078] Based on the trained autoregressive moving average model, the Internet of Things front-end data input into the autoregressive moving average model in the monitoring interval is determined, the predicted value of the Internet of Things front-end data is obtained, and the predicted value of the Internet of Things front-end data is marked as YC m , wherein m = 1, 2, 3, …, M, M is a positive integer, and m is the number of predicted Internet of Things front-end data;

[0079] The actual value of the Internet of Things front-end data in the current monitoring interval is determined, and the actual value of the Internet of Things front-end data in the current monitoring interval is marked as SJ m ;

[0080] The time series prediction coefficient is calculated, and the calculation formula is: , wherein XL yc is the time series prediction coefficient.

[0081] As can be seen from the formula, the larger the time series prediction coefficient, the greater the difference between the Internet of Things front-end data in the monitoring interval and the historical data, and the more unstable, therefore, the transmission frequency of the Internet of Things front-end data can be increased, and the Internet of Things front-end data in the current monitoring interval is uploaded to the cloud platform at a higher frequency.

[0082] Through comprehensive analysis of the change information and trend information of the Internet of Things front-end data, the high-frequency filtering amplitude coefficient, the data regularization similarity coefficient and the time series prediction coefficient are weighted and calculated, a transmission evaluation model is constructed, and a transmission evaluation coefficient is generated, and the calculation formula is: , wherein pg cs is the transmission evaluation coefficient, and α1, α2 and α3 are proportional coefficients of the high-frequency filtering amplitude coefficient, the data regularization similarity coefficient and the time series prediction coefficient respectively, and α1, α2 and α3 are all greater than 0.

[0083] From the formula, the greater the high-frequency filtering amplitude coefficient, data regularization similarity coefficient and time series prediction coefficient, the greater the transmission evaluation coefficient, indicating that the Internet of Things front-end data received at the gateway may have irregular significant changes, which may need to be transmitted to the cloud platform more frequently in order to update the data in the cloud platform in a timely manner. Conversely, the smaller the high-frequency filtering amplitude coefficient, data regularization similarity coefficient and time series prediction coefficient, the smaller the transmission evaluation coefficient, indicating that the Internet of Things front-end data received at the gateway may need to be compressed, selectively transmitted or have a lower transmission frequency, which helps to save bandwidth and storage resources and reduce the computing pressure of the cloud platform, ensuring that only key data is transmitted and processed to achieve the goal of efficient use of resources.

[0084] The transmission evaluation coefficient threshold is set, and the transmission evaluation coefficient threshold is compared with the transmission evaluation coefficient. If the transmission evaluation coefficient threshold is greater than the transmission evaluation coefficient, a collection signal is generated, the Internet of Things front-end data is collected through a pre-set mode of collecting Internet of Things front-end data, and transmitted to the cloud platform. If the transmission evaluation coefficient threshold is less than the transmission evaluation coefficient, no collection signal is generated, and the current transmission rate of the Internet of Things front-end data to the cloud platform is maintained.

[0085] Embodiment 2

[0086] The above embodiment comprehensively analyzes the change information and trend information of the Internet of Things front-end data to determine the transmission behavior of different types of Internet of Things front-end data, reduces unnecessary data transmission, and reduces the computing pressure of the cloud platform. The present embodiment considers the priority of different types of Internet of Things front-end data, and the gateway can determine the data that needs to be transmitted preferentially and the data that needs to be uploaded delayed.

[0087] By analyzing the historical data of different types of Internet of Things front-end data, and by analyzing the security problems existing in the actual application of different types of Internet of Things front-end data, the uncertainty information and risk information of different types of Internet of Things front-end data are collected. The uncertainty information of different types of Internet of Things front-end data is represented by an entropy instability coefficient, and the risk information of different types of Internet of Things front-end data is represented by a risk concealment evaluation coefficient.

[0088] The advantages of the entropy instability coefficient are:

[0089] Because the Internet of Things front-end data may be affected by various environmental factors and equipment changes, the entropy instability coefficient is suitable for more complex and irregular data distribution situations.

[0090] The entropy value can measure the randomness or uncertainty of data. In the Internet of Things data, high entropy means that the change of data is complex, which may be caused by sudden events or abnormal behavior, while low entropy indicates that the data is relatively stable, and the system is running normally. By analyzing the changes in entropy, it is helpful to optimize the data uploading strategy as a whole, reduce unnecessary transmission and delay;

[0091] For Internet of Things front-end data, the fluctuation and trend change of time series data often have strong dependence, and the entropy instability coefficient can help capture and quantify the mutation or fluctuation in time dependence, thereby playing an important role in time series-based analysis.

[0092] The entropy instability coefficient is obtained as follows: according to the historical data of the Internet of Things front-end data, the difference between adjacent data points of the Internet of Things front-end data is determined, and the difference between adjacent data points of the Internet of Things front-end data is marked as: CZ j , wherein j = 1, 2, 3, …, J, J is a positive integer, and j is the number of adjacent data points in the historical data of the Internet of Things front-end data;

[0093] According to the difference between adjacent data points of the Internet of Things front-end data, the probability distribution of the difference between adjacent data points is determined using kernel density estimation, and the calculation formula is: , wherein P(CZ) is the probability distribution of the difference between adjacent data points, K is the kernel function, and h is the smoothing parameter;

[0094] The entropy instability coefficient is calculated, and the calculation formula is: , wherein SZ wd is the entropy instability coefficient.

[0095] As can be seen from the formula, the greater the entropy instability coefficient, the more dispersed the distribution of this kind of Internet of Things front-end data, the higher the information uncertainty, and this kind of Internet of Things front-end data may need to be uploaded in priority.

[0096] The advantages of the risk concealment evaluation coefficient are:

[0097] The risk concealment evaluation coefficient converts the risk of data into a specific numerical value by combining data change information, severity and probability of risk events, providing an objective reference index for decision makers, which can quantify and compare the risks of different types of data;

[0098] The risk concealment evaluation coefficient can quantify and evaluate the severity of the risk potential according to the danger of the risk events caused by different kinds of Internet of Things front-end data, and through the analysis of historical data and the combination of expert opinions, the evaluation of different risk events can be adjusted and optimized.

[0099] The logic for obtaining the risk concealment assessment coefficient is as follows: based on the historical records of different types of IoT front-end data, determine the risk events that may be caused after each type of IoT front-end data is uploaded to the cloud platform and the cloud platform determines that the IoT front-end data is abnormal data, obtain the risk event mapping corresponding to each type of IoT front-end data, and determine all risk events caused by different types of IoT front-end data. Based on different risk events, different risk events are scored according to the expert equal weight principle to determine the risk score of each type of IoT front-end data;

[0100] A logistic regression model is constructed based on the risk scores of risk events caused by IoT front-end data and the proportion of risk events caused by IoT front-end data in all risk events to obtain the risk concealment assessment coefficient. The calculation formula of the risk concealment assessment coefficient is:

[0101]

[0102] Among them, FX yn is the risk concealment assessment coefficient, q = 1, 2, 3, ..., Q, Q is a positive integer, q is the number of the risk event caused by the IoT front-end data, PF1, PF2, PF3, ..., PF Q Risk scores for risk events caused by IoT front-end data, BZ1, BZ2, BZ3, ..., BZ Q It is the ratio of risk events caused by IoT front-end data to all risk events, where e is a natural number.

[0103] It can be seen from the formula that the larger the risk concealment assessment coefficient, the more dangerous the risk event caused by the IoT front-end data. If it is not handled in a timely manner, the risk may be greater, and there may be a greater probability of causing risks. Therefore, it may be necessary to give priority to uploading this type of IoT front-end data.

[0104] Through the uncertainty information and risk information of different types of IoT front-end data, the entropy instability coefficient and the risk concealment assessment coefficient are weighted and calculated to build a priority assessment model and generate a priority assessment coefficient. The calculation formula is: PG yx =β1SZ wd +β2FX yn Among them, PG yx is the priority assessment coefficient, β1 and β2 are the proportional coefficients of the entropy instability coefficient and the risk concealment assessment coefficient, respectively. β1 and β2 are both greater than 0.

[0105] It can be seen from the formula that the greater the entropy value instability coefficient and the risk concealment evaluation coefficient, the greater the priority evaluation coefficient, which indicates that the priority of the kind of Internet of Things front-end data uploaded to the cloud platform for analysis is higher, so as to ensure that the system can discover problems as soon as possible and take corresponding measures, otherwise, the smaller the entropy value instability coefficient and the risk concealment evaluation coefficient, the smaller the priority evaluation coefficient, which indicates that the priority of the kind of Internet of Things front-end data uploaded to the cloud platform for analysis is lower.

[0106] The priority evaluation coefficients of different kinds of Internet of Things front-end data are obtained, and the priority evaluation coefficients of different kinds of Internet of Things front-end data are sequentially sorted from large to small, and according to the Internet of Things front-end data received by the gateway, the Internet of Things front-end data with high priority is preferentially uploaded.

[0107] The application analyzes the Internet of Things front-end data at the gateway, extracts the trend information and change information of the Internet of Things front-end data in the monitoring interval, dynamically adjusts the transmission frequency of the Internet of Things front-end data uploaded to the cloud platform based on the comprehensive analysis of the trend information and change information, and allocates priorities to different kinds of Internet of Things front-end data by analyzing the historical data and security problems in actual application, which helps to intelligently adjust the data transmission strategy, thereby improving the security and stability of the system, and effectively reducing the computing pressure of the cloud platform.

[0108] The above formula is a dimensionless value, and the formula is obtained by software simulation of a large amount of data to obtain the latest real situation, and the preset parameters in the formula are set by the person skilled in the art according to the actual situation.

[0109] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, the above-described embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center through a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0110] It should be understood that the size of the sequence number of each process described above in various embodiments of the present application does not mean the order of execution, and the execution order of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0111] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be realized in electronic hardware, or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0112] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0113] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the division of the above-described device embodiments is only a logical function division, and there can be another division manner for actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, or the among different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0114] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and various other media that can store program codes.

[0115] The above describes only the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for collecting data from the front end of the Internet of Things, characterized in that: The specific steps include: S1: Send the data collected by the IoT front-end device to the gateway, which analyzes the collected data to determine the trend information and change information of the IoT front-end data within the monitoring interval; S2: Adjust the transmission frequency of IoT front-end data uploaded from the gateway to the cloud platform through comprehensive analysis of trend information and change information of IoT front-end data; S3: Collect uncertainty and risk information of different types of IoT front-end data by analyzing historical data and security issues existing in the actual applications of different types of IoT front-end data; S4: Comprehensively evaluate the uncertainty and risk information of different types of IoT front-end data to determine the priority of sending different types of IoT front-end data at the gateway; The change information of IoT front-end data is expressed by high-frequency filtering amplitude coefficient and data regularization similarity coefficient; The logic for obtaining the high-frequency filtering amplitude coefficient is as follows: obtain the IoT front-end data within the monitoring interval, analyze the IoT front-end data within the monitoring interval in the time domain and frequency domain through wavelet transform, and express the IoT front-end data within the monitoring interval through wavelet transform as follows: Among them, WL is the data of the IoT front-end in the monitoring interval through wavelet transformation, a is the scale of the wavelet function, b is the translation parameter, is the wavelet function; By determining the peak frequency and center frequency of the IoT front-end data after wavelet transformation, the frequency difference coefficient is calculated. The calculation formula is: CY pl =|argmax(WL)-WL zx Among them, CY pl is the frequency difference coefficient, WL zx is the center frequency of the IoT front-end data after wavelet transformation; Set the wavelet coefficient threshold and calculate the high-frequency filter ratio coefficient. The calculation formula is: Among them, BZ gp is the high-frequency filter ratio coefficient, t c ~t d is the time period greater than the wavelet coefficient threshold; Calculate the high-frequency filter amplitude coefficient, the calculation formula is: FZ gp =CY pl ×BZ gp Among them, FZ gp is the high-frequency filtering amplitude coefficient; The logic for obtaining the data regularization similarity coefficient is as follows: obtain the IoT front-end data within the current monitoring interval, and mark the IoT front-end data within the current monitoring interval as: SJ n , n=1, 2, 3, ..., N, N is a positive integer, n is the number of IoT front-end data received by the gateway at different times within the current monitoring interval; Get the IoT front-end data in the last monitoring interval and mark the IoT front-end data in the last monitoring interval as: SJ i , i=1, 2, 3, ..., I, I is a positive integer, i is the number of IoT front-end data received by the gateway at different times in the previous monitoring interval; Construct a cost matrix, use the absolute difference between the IoT front-end data at each moment of the current monitoring interval and the previous monitoring interval as the distance metric, determine each element in the cost matrix, and mark the cost matrix as: C(n,i), C(n,i)=|SJ n -SJ i |; Construct a cumulative cost matrix that records the minimum total cost from the starting point to each point. Label the cumulative cost matrix as: D(n,i), D(n,i) = C(n,i) + min(D(n-1,i), D(n,i-1), D(n-1,i-1)); Through recursive cumulative calculation, the DTW distance is determined, the last element of the cumulative cost matrix is ​​obtained, and the data regularization similarity coefficient is calculated. The expression is: GZ xs =D(N,I); where GZ xs is the data regularization similarity coefficient; The trend information of IoT front-end data is expressed through time series prediction coefficients; The logic for obtaining the time series prediction coefficient is as follows: collecting historical IoT front-end data, using IoT front-end data in multiple monitoring intervals in the historical IoT front-end data as training data, dividing the training data into input training data and prediction training data, training an autoregressive moving average model based on the input training data and the prediction training data, and estimating the regression coefficient and the moving average coefficient of the autoregressive moving average model by the least squares method; Based on the trained autoregressive sliding average model, determine the IoT front-end data input into the autoregressive sliding average model within the monitoring interval, obtain the predicted value of the IoT front-end data, and mark the predicted value of the IoT front-end data as: YC m , where m = 1, 2, 3, ..., M, M is a positive integer, and m is the number of the predicted IoT front-end data; Determine the actual value of the IoT front-end data within the current monitoring interval, and mark the actual value of the IoT front-end data within the current monitoring interval as: SJ m ; Calculate the time series prediction coefficient, the calculation formula is: Among them, XL yc is the time series prediction coefficient.

2. The method for collecting data from an Internet of Things front end according to claim 1, wherein: Adjust the transmission frequency of IoT front-end data uploaded from the gateway to the cloud platform, including: Through comprehensive analysis of the change information and trend information of the IoT front-end data, the high-frequency filtering amplitude coefficient, data regularization similarity coefficient, and time series prediction coefficient are weighted and calculated to build a transmission evaluation model and generate a transmission evaluation coefficient. The calculation formula is: Among them, pg cs is the transmission evaluation coefficient, α1, α2, and α3 are the proportional coefficients of the high-frequency filtering amplitude coefficient, the data regularization similarity coefficient, and the time series prediction coefficient, respectively. α1, α2, and α3 are all greater than 0; Set a transmission evaluation coefficient threshold and compare the transmission evaluation coefficient threshold with the transmission evaluation coefficient. If the transmission evaluation coefficient threshold is greater than the transmission evaluation coefficient, generate a collection signal, collect IoT front-end data through the pre-set mode of collecting IoT front-end data, and transmit it to the cloud platform. If the transmission evaluation coefficient threshold is less than the transmission evaluation coefficient, do not generate a collection signal and maintain the current rate of IoT front-end data transmission to the cloud platform.

3. The method for collecting data from an Internet of Things front end according to claim 2, wherein: Collect uncertainty information of different types of IoT front-end data, including: The uncertainty information of IoT front-end data is expressed by the entropy instability coefficient; The logic for obtaining the entropy instability coefficient is as follows: according to the historical data of the IoT front-end data, the difference between adjacent data points of the IoT front-end data is determined, and the difference between adjacent data points of the IoT front-end data is marked as: CZ j , where j = 1, 2, 3, ..., J, J is a positive integer, and j is the number of the adjacent data point in the historical data of the IoT front-end data; According to the difference between adjacent data points of IoT front-end data, kernel density estimation is used to determine the probability distribution of the difference between adjacent data points. The calculation formula is: Where P(CZ) is the probability distribution of the difference between adjacent data points, K is the kernel function, and h is the smoothing parameter; Calculate the entropy instability coefficient, the calculation formula is: Among them, SZ wd is the entropy instability coefficient.

4. The method for collecting data from an Internet of Things front end according to claim 3, wherein: Collect risk information on different types of IoT front-end data, including: The risk information of IoT front-end data is expressed through risk concealment assessment coefficient; The logic for obtaining the risk concealment assessment coefficient is as follows: based on the historical records of different types of IoT front-end data, determine the risk events that may be caused after each type of IoT front-end data is uploaded to the cloud platform and the cloud platform determines that the IoT front-end data is abnormal data, obtain the risk event mapping corresponding to each type of IoT front-end data, and determine all risk events caused by different types of IoT front-end data. Based on different risk events, different risk events are scored according to the expert equal weight principle to determine the risk score of each type of IoT front-end data; A logistic regression model is constructed based on the risk scores of risk events caused by IoT front-end data and the proportion of risk events caused by IoT front-end data in all risk events to obtain the risk concealment assessment coefficient. The calculation formula of the risk concealment assessment coefficient is: Among them, FX yn is the risk concealment assessment coefficient, q = 1, 2, 3, ..., Q, Q is a positive integer, q is the number of the risk event caused by the IoT front-end data, PF1, PF2, PF3, ..., PF Q Risk scores for risk events caused by IoT front-end data, BZ1, BZ2, BZ3, ..., BZ Q It is the ratio of risk events caused by IoT front-end data to all risk events, where e is a natural number.

5. The method for collecting data from an Internet of Things front end according to claim 4, wherein: Determine the priority of sending different types of IoT front-end data at the gateway, including: Through the uncertainty information and risk information of different types of IoT front-end data, the entropy instability coefficient and the risk concealment assessment coefficient are weighted and calculated to build a priority assessment model and generate a priority assessment coefficient. The calculation formula is: PG yx =β1SZ wd +β2FX yn Among them, PG yx is the priority evaluation coefficient, β1 and β2 are the proportional coefficients of the entropy instability coefficient and the risk concealment evaluation coefficient, respectively, and β1 and β2 are both greater than 0; Obtain priority evaluation coefficients of different types of IoT front-end data, sort the priority evaluation coefficients of different types of IoT front-end data from large to small, and upload the IoT front-end data with the highest ranking first according to the IoT front-end data received by the gateway.

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