A Hub Data Intelligent Communication Method and System

Abstract

The present invention relates to the technical field of communication processing, and particularly relates to an intelligent communication method and system for hub data. The method includes: obtaining the timing state curve data to be transmitted by the hub, determining the change characteristic index according to the data change and extreme value distribution; determining the initial transmission priority according to the difference of the change characteristic index and the numerical distribution difference; performing signal time-frequency decomposition to obtain component signals, and determining the decomposition characteristic coefficient according to the distribution of extreme points and the numerical characteristics of the instantaneous frequency; determining the moment anomaly index according to the difference of the decomposition characteristic coefficient and the difference of the signal value; determining the corrected transmission priority, and performing communication transmission on all the timing state curve data at each moment according to the corrected transmission priority of all the timing state curve data at each moment. The present invention can effectively perform reasonable allocation of priorities, improve the stability and real-time performance of data transmission and the response speed to key information.

Description

Technical Field

[0001] The present invention relates to the technical field of communication processing, and particularly relates to an intelligent communication method and system for hub data. Background Art

[0002] With the continuous development and popularization of the Internet of Things technology, a large number of various sensor devices and intelligent terminals have emerged, forming a huge data network. As the central node of data communication, traditional hubs obtain real-time data from various sensors, devices or other data sources, and play a key role in the system.

[0003] In related technologies, usually, the importance of the data to be transmitted is evaluated, and then, communication transmission is carried out according to the importance. In this way, since the importance evaluation in the communication transmission process is analyzed for the data type, which represents the importance of the data itself, it is impossible to effectively allocate the specific transmission of abnormal data. As a result, the response speed to key information is slow, the allocation efficiency of transmission priorities is low, and the stability of data transmission is poor. Summary of the Invention

[0004] In order to solve the technical problems of slow response speed to key information, low allocation efficiency of transmission priorities, and poor stability of data transmission in related technologies, the present invention provides an intelligent communication method and system for hub data, and the specific technical solutions adopted are as follows:

[0005] The present invention proposes an intelligent communication method for hub data, and the method includes:

[0006] Obtain at least two types of time series state curve data to be transmitted by the hub, and use any one of the time series state curve data as the data to be measured;

[0007] According to the data change and extreme value distribution of the data to be measured at each moment, determine the change characteristic index of the data to be measured; according to the difference in the change characteristic index between the data to be measured and the change characteristic index of each other type of time series state curve data, as well as the difference in numerical distribution, determine the initial transmission priority of the data to be measured;

[0008] Perform signal time-frequency decomposition on the data to be measured to obtain component signals, use any one of the component signals as the signal to be measured, and according to the distribution of extreme points and the numerical characteristics of the instantaneous frequency in the signal to be measured, determine the decomposition characteristic coefficient of the signal to be measured; according to the difference in the decomposition characteristic coefficient of different component signals at the same moment and the difference in signal values, determine the moment anomaly index of the data to be measured at the corresponding moment;

[0009] Adjust the initial transmission priority according to the time anomaly index to obtain the corrected transmission priority, and perform communication transmission on all the time series state curve data at each moment according to the corrected transmission priority of all the time series state curve data at each moment.

[0010] Further, the method for obtaining the change characteristic index of the data to be measured includes:

[0011] Take the absolute value of the difference between the two data values that are closest in time sequence in the data to be measured as the numerical change difference between the two data values within their corresponding time intervals;

[0012] Perform an averaging process on the numerical change differences of all time intervals in the data to be measured to obtain the numerical change coefficient of the data to be measured;

[0013] Take the mean value of the data values of the data to be measured at all moments as the mean value to be measured; perform extreme point detection on the data values of the data to be measured at all moments according to the time sequence to obtain the extreme values to be measured;

[0014] Take the difference between any one of the extreme values to be measured and the mean value to be measured as the extreme value mean difference corresponding to the extreme value to be measured; calculate the mean value of the extreme value mean differences of all the extreme values to be measured as the extreme value distribution coefficient of the data to be measured;

[0015] Determine the change characteristic index of the data to be measured according to the numerical change coefficient and the extreme value distribution coefficient, wherein both the numerical change coefficient and the extreme value distribution coefficient are positively correlated with the change characteristic index, and the value of the change characteristic index is a normalized value.

[0016] Further, the method for obtaining the initial transmission priority of the data to be measured includes:

[0017] Take the other time series state curve data except the data to be measured as the comparison data, select any one of the comparison data as the data to be analyzed, and determine the difference between the change characteristic index of the data to be measured and the data to be analyzed as the first transmission weight;

[0018] Take the mean square error between the data to be measured and the data to be analyzed as the second transmission weight;

[0019] Fuse the first transmission weight and the second transmission weight to obtain the transmission influence index between the data to be measured and the data to be analyzed;

[0020] Take the mean value of the transmission influence indexes between the data to be measured and each kind of comparison data, and after normalization, use it as the initial transmission priority of the data to be measured.

[0021] Further, the signal time-frequency decomposition of the data to be measured to obtain component signals includes:

[0022] Based on the EMD decomposition algorithm, perform signal decomposition on the data to be measured to obtain IMF components, and use all IMF components as component signals.

[0023] Further, the method for obtaining the decomposition characteristic coefficient of the signal to be measured includes:

[0024] Determine the time interval between two adjacent extreme points in the signal to be measured as the extreme value time interval; take the reciprocal of the mean of all extreme value time intervals as the time change rate of the signal to be measured;

[0025] Determine the decomposition characteristic coefficient of the signal to be measured according to the instantaneous frequency and the time change rate of the signal to be measured, where both the instantaneous frequency and the time change rate are positively correlated with the decomposition characteristic coefficient.

[0026] Further, the method for obtaining the moment anomaly index of the data to be measured at the corresponding moment includes:

[0027] Take the component signals other than the signal to be measured as other component signals. At any moment, calculate the absolute value of the difference between the decomposition characteristic coefficients of the signal to be measured and any other component signal to obtain the first anomaly index of the signal to be measured and the corresponding other component signal;

[0028] Take the absolute value of the difference between the signal values of the signal to be measured and any other component signal as the second anomaly index of the signal to be measured and the corresponding other component signal;

[0029] Determine the moment anomaly degree of the signal to be measured at the corresponding moment according to the first anomaly index and the second anomaly index of the signal to be measured and all other component signals;

[0030] Normalize the mean of the moment anomaly degrees of all component signals at the same moment as the moment anomaly index of the data to be measured at the corresponding moment.

[0031] Further, the method for obtaining the moment anomaly degree of the signal to be measured includes:

[0032] Fuse the first anomaly index and the second anomaly index of the signal to be measured and any other component signal to obtain the comparison anomaly coefficient of the signal to be measured and the corresponding other component signal, where both the first anomaly index and the second anomaly index are negatively correlated with the comparison anomaly coefficient;

[0033] Take the mean of the comparison anomaly coefficients of the signal to be measured and all other component signals as the moment anomaly degree of the signal to be measured.

[0034] Further, the method for obtaining the corrected transmission priority includes:

[0035] Calculate the product of the anomaly index at the moment and the initial transmission priority, and normalize it as the corrected transmission priority of the data to be measured at the corresponding moment.

[0036] Further, the communication transmission of all the time-series state curve data at each moment according to the corrected transmission priority of all the time-series state curve data at each moment includes:

[0037] Sort all the time-series state curve data at the same moment in descending order according to the corrected transmission priority, and transmit all the time-series state curve data at that moment according to the sorting order.

[0038] The present invention also provides a hub data intelligent communication system, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a hub data intelligent communication method as described above are implemented.

[0039] The present invention has the following beneficial effects:

[0040] The present invention obtains a variety of time-series state curve data to be transmitted by the hub, determines the change characteristic index of each time-series state curve data according to the data change and extreme value distribution, combines the differences in the change characteristic indexes and the numerical distribution differences of different time-series state curve data, and determines the initial transmission priority. Then the initial transmission priority can effectively characterize the transmission situation of each time-series state curve data; since anomaly analysis needs to be performed during the transmission process at each moment, the embodiment of the present invention combines the signal time-frequency decomposition technology to realize the signal analysis of each data to be measured, determines the decomposition characteristic coefficient according to the distribution of extreme points and the numerical characteristics of the instantaneous frequency, and combines the decomposition characteristic coefficients and signal values of different component signals at the same moment to realize the analysis of the moment anomaly index of different time-series state curve data at the same moment; combining the moment anomaly index to adjust the initial transmission priority can perform anomaly analysis on different time-series state curve data, and combine the anomaly analysis result with the initial transmission priority to obtain the adjusted transmission priority, so that when data is transmitted according to the adjusted transmission priority, transmission analysis can be performed for abnormal situations, the priority of all time-series state curve data can be evaluated, and thus the priority of different data to be transmitted at the same moment can be reasonably allocated effectively, improving the stability and real-time performance of data transmission and the response speed to key information. Description of the Drawings

[0041] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0042] Figure 1 Flowchart of a method for intelligent communication of hub data provided by an embodiment of the present invention;

[0043] Figure 2 Schematic diagram of a hub data transmission scenario provided by an embodiment of the present invention;

[0044] Figure 3 Schematic diagram of the timing state curve data provided by an embodiment of the present invention;

[0045] Figure 4 Schematic diagram of a method for obtaining variable characteristic indicators provided by an embodiment of the present invention;

[0046] Figure 5 Schematic diagram of a method for obtaining the initial transmission priority of data to be measured provided by an embodiment of the present invention;

[0047] Figure 6 Schematic diagram of a component signal provided by an embodiment of the present invention. Detailed implementation manners

[0048] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in combination with the drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of a method and system for intelligent communication of hub data proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0050] The following specifically describes the specific solution of a method for intelligent communication of hub data provided by the present invention in conjunction with the drawings.

[0051] Please refer to Figure 1 , which shows a flowchart of a method for intelligent communication of hub data provided by an embodiment of the present invention. The method includes:

[0052] S101: Obtain at least two types of timing state curve data to be transmitted by the hub, and use any one of the timing state curve data as the data to be measured.

[0053] The specific implementation scenario of the embodiment of the present invention can be, for example: in a data transmission scenario including a hub, such as Figure 2 shown Figure 2 is a schematic diagram of a hub data transmission scenario provided by an embodiment of the present invention. With a hub as the central node, it receives the timing state curve data sent by multiple sensors. Since the hub is a device for comprehensively analyzing and transmitting multiple different types of data, multiple different types of sensors can be connected to the hub. The hub has interfaces and communication protocols adapted to various sensors for connection and data exchange with the sensors; different types of sensor interfaces can be used, such as UART, SPI, I2C, etc., or implemented through a general interface module; configure the hub to collect sensor data in a periodic or time-triggered manner, and process and organize the collected data, and design operations such as data format conversion, unit conversion, and data verification to ensure the accuracy and availability of the data; ensure that the hub supports common communication protocols, such as Modbus, MQTTQ, HTTP, etc., so as to transmit the sensor data to the upper-layer system or other devices through the network. Thus, the data collected by the sensors and to be transmitted using the hub is used as the timing state curve data.

[0054] Of course, the devices connected to the hub for data transmission in the embodiments of the present invention are not limited to sensors, and can also be various other devices or other data sources. For the convenience of understanding, the embodiments of the present invention use sensors for specific analysis. Other forms of data sources also fall within the protection scope of the embodiments of the present invention and are not limited thereto.

[0055] Optionally, the transmission medium can be a wired link (such as, but not limited to, coaxial cable, optical fiber, and Digital Subscriber Line (DSL), etc.) or a wireless link (such as, but not limited to, Wireless Fidelity (WIFI), Bluetooth, and mobile device network, etc.).

[0056] The timing state curve data in the embodiments of the present invention is the data collected by all sensors at the same time point within a certain time period. Since sensor data usually refers to a device for detecting and measuring specific parameters of the environment or object, these parameters may include, but are not limited to, temperature, humidity, pressure, motion state, etc. The physical quantity is converted into the form of an electrical signal curve data by the sensor, that is, the timing state curve data is a timing electrical signal curve data.

[0057] For example, the acquisition period of an embodiment of the present invention is 1 second. That is to say, the electrical signal data to be transmitted by the hub within 1 second is acquired. For example, the electrical signal data representing temperature. Then, within a certain acquisition period, there are multiple data acquisition moments. For example, when the acquisition period is 1 second, 100 data values are acquired within 1 second. Then, the acquisition interval for each data value is 0.01 second, and 100 data values are combined into time-series state curve data according to the time sequence. As Figure 3 shown Figure 3 is a schematic diagram of the time-series state curve data provided by an embodiment of the present invention.

[0058] In an embodiment of the present invention, any one of the time-series state curve data is selected as the data to be measured, so as to facilitate specific analysis of the data to be measured in the subsequent process.

[0059] S102: Determine the change characteristic index of the data to be measured according to the data change and extreme value distribution of the data to be measured at each moment; determine the initial transmission priority of the data to be measured according to the difference between the change characteristic index of the data to be measured and that of each other time-series state curve data and the numerical distribution difference.

[0060] It can be understood that since there may be abnormal corresponding data changes during the data transmission between the sensor and the hub, this abnormality may be due to a change in the actual state, such as a sudden increase in temperature, or it may also be due to a transmission abnormality, such as transmission noise generated during the data transmission process. Then, during the actual data transmission process, the situation of abnormal data changes requires a faster response time to facilitate timely exception handling. Therefore, during the data transmission process, the situation of abnormal data changes needs to be transmitted preferentially.

[0061] In an embodiment of the present invention, since the types of time-series state curve data acquired by the hub are diverse, but because their sources are usually in the same environmental system, such as the weather system of temperature, humidity, air pressure, etc. in a certain area, after measuring with multiple different sensors and based on the same hub for data transmission, there is a corresponding correlation relationship between the data states of different time-series state curve data. Therefore, the analysis of the initial transmission priority can be realized based on this correlation relationship.

[0062] Further, in some embodiments of the present invention, in combination with Figure 4 , Figure 4 is a schematic diagram of the method for obtaining the change characteristic index provided by an embodiment of the present invention:

[0063] S401: Take the absolute value of the difference between the two data values closest in time sequence in the data to be measured as the numerical change difference between the two data values within their corresponding time intervals.

[0064] In an embodiment of the present invention, the absolute value of the difference between two adjacent data values in the data to be tested represents the data change characteristics of the time interval corresponding to the two data values. Thus, the difference in numerical change is calculated. The larger the difference in numerical change, the greater the change in the data value in the corresponding time interval, that is, the more consistent with the mutation characteristics of abnormal data.

[0065] S402: averaging the numerical variation differences of all time intervals in the data to be measured to obtain the numerical variation coefficient of the data to be measured.

[0066] In an embodiment of the present invention, a numerical variation coefficient is obtained by comprehensively averaging the differences in numerical variation of all time intervals. The numerical variation coefficient represents the overall numerical fluctuation of the data to be measured. It can be understood that the larger the numerical variation coefficient is, the greater the numerical fluctuation of the corresponding data to be measured in each time interval is, the more drastic the numerical variation of the data to be measured is, and the greater the abnormality is.

[0067] S403: taking the mean of the data values of the data to be tested at all times as the mean to be tested; performing extreme point detection on the data values of the data to be tested at all times according to the time series to obtain the extreme values to be tested; taking the difference between any extreme value to be tested and the mean to be tested as the extreme value mean difference of the corresponding extreme value to be tested; calculating the mean of the extreme value mean differences of all the extreme values to be tested as the extreme value distribution coefficient of the data to be tested.

[0068] Among them, the measured mean represents the overall numerical characteristics of the data values in the measured data, while the measured extreme value represents the maximum and minimum values corresponding to the measured data, that is, the extreme value distribution. Therefore, the numerical discreteness corresponding to the extreme value is determined by the difference between the extreme value and the mean, and the mean of all extreme value mean differences is used as the extreme value distribution coefficient. The larger the extreme value distribution coefficient, the more discrete the distribution of the extreme value points in the corresponding measured data, and the more drastic the change in its numerical value.

[0069] S404: Determine a change characteristic index of the data to be measured according to the numerical change coefficient and the extreme value distribution coefficient, wherein the numerical change coefficient and the extreme value distribution coefficient are both positively correlated with the change characteristic index, and the value of the change characteristic index is a normalized numerical value.

[0070] The present invention can perform an overall analysis on the numerical variation of the measured data according to the numerical variation coefficient and the extreme value distribution coefficient to obtain the variation characteristic index.

[0071] In one embodiment of the present invention, the normalization processing can be specifically, for example, maximum and minimum value normalization processing, and the normalization in subsequent steps can all adopt maximum and minimum value normalization processing. In other embodiments of the present invention, other normalization methods can be selected according to the specific range of numerical values, which will not be described in detail.

[0072] Among them, a positive correlation means that the dependent variable will increase as the independent variable increases, and the dependent variable will decrease as the independent variable decreases. The specific relationship can be a multiplication relationship, an addition relationship, or the power of an exponential function, which is determined by actual application; a negative correlation means that the dependent variable will decrease as the independent variable increases, and the dependent variable will increase as the independent variable decreases. It can be a subtraction relationship, a division relationship, etc., which is determined by actual application.

[0073] For example, an embodiment of the present invention can calculate the product of the numerical variation coefficient and the extreme value distribution coefficient, and then normalize the product value by the maximum and minimum values to obtain a variation characteristic index. The larger the value of the variation characteristic index, the more serious the numerical fluctuation of the corresponding data to be measured.

[0074] The present invention can effectively characterize the numerical fluctuation of the data to be tested itself by determining the change characteristic index. In normal communication transmission, the more serious the data fluctuation is, the more drastic the current data change is, and the higher the degree of data abnormality is, the higher the importance of transmission is.

[0075] It should be noted that the severity of numerical fluctuations may be related to environmental factors and cannot directly prove that they are abnormal data. It is also necessary to conduct an overall analysis based on multiple different types of data to determine the abnormality of the fluctuations and realize the analysis of initial transmission priority.

[0076] Further, in some embodiments of the present invention, in combination Figure 5 , Figure 5 A schematic diagram of a method for obtaining the initial transmission priority of data to be tested provided by an embodiment of the present invention includes:

[0077] S501: Use other time series state curve data except the data to be tested as comparison data, select any one of the comparison data as the data to be analyzed, and determine the difference between the change characteristic indicators of the data to be tested and the data to be analyzed as the first transmission weight.

[0078] In an embodiment of the present invention, the difference between the change characteristic indicators of the data to be tested and the data to be analyzed can be specifically, for example, the absolute value of the difference between the data to be tested and the data to be analyzed, that is, the absolute value of the difference between the change characteristic indicators of the data to be tested and the change characteristic indicators of the data to be analyzed is calculated as the first transmission weight.

[0079] In some other implementations of the present invention, the ratio of the difference and the sum of the change characteristic indicators of the data to be tested and the data to be analyzed can also be calculated and normalized as the first transmission weight. The larger the first transmission weight, the greater the difference in the numerical value of the change characteristic indicator between the data to be tested and the data to be analyzed.

[0080] S502: Using the mean square error between the data to be measured and the data to be analyzed as the second transmission weight.

[0081] It should be noted that: during the process of calculating the mean square error, it is necessary to ensure that the ordinate of the data change curve maintains a unified dimension. Data standardization can be used to operate on the time series to ensure a unified dimension.

[0082] Of course, in some other embodiments of the present invention, various other difference calculation methods can also be used to determine the second transmission weight. As long as the numerical difference between the data to be measured and the data to be analyzed is larger, the second transmission weight is larger. For example, the data to be measured and the data to be analyzed can be used as sequences in time series, and then the dtw value of the two sequences can be determined based on the dynamic time warping algorithm, and the dtw value can be normalized as the second transmission weight. There is no limitation on this.

[0083] S503: Fuse the first transmission weight and the second transmission weight to obtain the transmission influence index of the data to be measured and the data to be analyzed; normalize the mean value of the transmission influence indexes of the data to be measured and each type of comparison data as the initial transmission priority of the data to be measured.

[0084] In the embodiments of the present invention, through the difference in the change characteristic indexes and the mean square error between the data to be measured and the data of other time series state curve data, the difference analysis of the data to be measured and the data of other time series state curve data can be carried out. The product of the first transmission weight and the second transmission weight can be calculated as the transmission influence index, or the sum value of the first transmission weight and the second transmission weight can also be calculated as the transmission influence index. There is no limitation on this.

[0085] That is to say, the larger the value of the initial transmission priority, the greater the fluctuation difference between the data to be measured and all other time series state curve data, that is, the more abnormal the data to be measured itself, and the more necessary it is to ensure priority transmission, so as to be able to respond to abnormal data in a timely manner.

[0086] It should be noted that the initial transmission priority only characterizes the abnormality of the fluctuation of this type of data to be measured. However, in the actual transmission process, due to the inconsistent changes of various environmental factors at different times, therefore, the transmission analysis at each moment also needs to be considered. For the specific analysis situation, please refer to the subsequent embodiments.

[0087] S103: Perform signal time-frequency decomposition on the data to be measured to obtain component signals. Take any component signal as the signal to be measured, and determine the decomposition characteristic coefficient of the signal to be measured according to the distribution of extreme points and the numerical characteristics of the instantaneous frequency in the signal to be measured; determine the moment anomaly index of the data to be measured at the corresponding moment according to the difference in the decomposition characteristic coefficients and the difference in signal values of different component signals at the same moment.

[0088] In the embodiment of the present invention, considering that the data in the transmission process is not a global anomaly, there may be anomalies in a certain type of data points at certain moments; therefore, it is necessary to give priority to the transmission of abnormal data points (important data points) in the timing state curve data to ensure data transmission efficiency and reasonable allocation of resources.

[0089] Furthermore, in some embodiments of the present invention, the measured data is subjected to signal time-frequency decomposition to obtain component signals, including: based on an EMD decomposition algorithm, the measured data is subjected to signal decomposition to obtain IMF components, and all IMF components are used as component signals.

[0090] The EMD decomposition algorithm is an empirical mode decomposition algorithm, which is a signal decomposition algorithm well known in the art. The EMD decomposition algorithm can decompose the signal into corresponding multiple IMF components. In the embodiment of the present invention, each IMF component is regarded as a component signal, such as Figure 6 As shown, Figure 6 A schematic diagram of component signals provided by an embodiment of the present invention.

[0091] Of course, in other embodiments of the present invention, other signal decomposition methods, such as EEMD, FEEMD, and CEEMD, etc., may also be used, without limitation thereto.

[0092] It can be understood that when the data fluctuation is more complex, the data fluctuation of the component signal obtained by the EMD decomposition algorithm is more irregular, and the fluctuation difference between different component signals is more obvious.

[0093] Furthermore, in some embodiments of the present invention, a method for obtaining the decomposition characteristic coefficient of a signal to be measured includes: determining the time interval between two adjacent extreme points in the signal to be measured as the extreme time interval; taking the inverse of the mean of all extreme time intervals as the time change rate of the signal to be measured; and determining the decomposition characteristic coefficient of the signal to be measured based on the instantaneous frequency and time change rate of the signal to be measured, wherein the instantaneous frequency and the time change rate are both positively correlated with the decomposition characteristic coefficient.

[0094] Among them, Hilbert transform is a signal processing method when performing instantaneous frequency analysis. In the embodiment of the present invention, Hilbert transform can be used to determine the instantaneous frequency of the signal to be measured at each moment, and the average value is calculated as the instantaneous frequency of the signal to be measured.

[0095] It can be understood that the extreme value time interval represents the time difference between two adjacent extreme value points. Since the extreme value points are maximum value points and minimum value points, the smaller the time difference between two adjacent extreme value points, that is, the smaller the value of the extreme value time interval, the faster the change of the signal to be measured is. The inverse of the mean of all extreme value time intervals is taken as the time change rate of the signal to be measured. The larger the time change rate, the more drastic the fluctuation of the signal to be measured.

[0096] The present invention determines the decomposition characteristic coefficient of the signal to be measured through the instantaneous frequency and time change rate of the signal to be measured. Since the instantaneous frequency and the time change rate are both positively correlated with the decomposition characteristic coefficient, the product of the instantaneous frequency and the time change rate can be calculated and normalized as the decomposition characteristic coefficient of the signal to be measured.

[0097] In the embodiment of the present invention, the instantaneous frequency represents the overall frequency change of the signal to be measured, and the greater the time change rate, the more drastic the fluctuation of the signal to be measured. Therefore, the embodiment of the present invention determines that the instantaneous frequency and the time change rate are both positively correlated with the decomposition characteristic coefficient, so that the larger the decomposition characteristic coefficient, the greater the fluctuation frequency and the fluctuation change rate of the signal to be measured.

[0098] Furthermore, in some embodiments of the present invention, a method for obtaining a momentary anomaly index of the data to be measured at a corresponding moment includes: taking component signals other than the signal to be measured as other component signals, and at any moment, calculating the absolute value of the difference between the decomposition characteristic coefficients of the signal to be measured and any other component signal to obtain a first anomaly index of the signal to be measured and the corresponding other component signal; taking the absolute value of the difference between the signal values of the signal to be measured and any other component signal as a second anomaly index of the signal to be measured and the corresponding other component signal; determining the momentary anomaly degree of the signal to be measured at the corresponding moment based on the first anomaly index and the second anomaly index of the signal to be measured and all other component signals; and taking the mean of the momentary anomaly degrees of all component signals at the same moment, normalizing them and using them as the momentary anomaly index of the data to be measured at the corresponding moment.

[0099] In the embodiment of the present invention, the first abnormality index is calculated to indicate the difference of the decomposed characteristic coefficients at any moment, and the second abnormality index indicates the difference of the signal value of the component signal at the corresponding moment. The difference between the signal to be tested and other component signals in these two dimensions can be determined by the first abnormality index and the second abnormality index, and used as the abnormality degree of the signal to be tested itself at the corresponding moment. The abnormality index at the moment is determined by combining the overall mean of the abnormality degree of all component signals at the moment.

[0100] Furthermore, in some embodiments of the present invention, a method for obtaining the momentary abnormality degree of a signal to be measured includes: fusing the first abnormality indicator and the second abnormality indicator of the signal to be measured with any other component signal to obtain a comparative abnormality coefficient between the signal to be measured and the corresponding other component signal, wherein the first abnormality indicator and the second abnormality indicator are both negatively correlated with the comparative abnormality coefficient; and taking the average of the comparative abnormality coefficients of the signal to be measured and all other component signals as the momentary abnormality degree of the signal to be measured.

[0101] In the embodiments of the present invention, during the process of empirical mode decomposition, the component signals are arranged in the order from high frequency to low frequency, and there are significant differences between adjacent components. If the frequency characteristics between components are similar and the data points at the same positions are similar, it indicates that the data points at that position are abnormal.

[0102] In the embodiments of the present invention, the smaller the value of the first anomaly index, the smaller the difference in the decomposition feature coefficients at the corresponding moment, that is, the more abnormal the signal to be measured at the corresponding moment. And the smaller the second anomaly index, the smaller the difference in the data values at the corresponding moment, which can also characterize that the signal to be measured is more abnormal. Therefore, in the embodiments of the present invention, the product of the first anomaly index and the second anomaly index can be calculated, and the negative of this product is normalized to be used as the comparison anomaly coefficient. Then, all other component signals are traversed to determine the average value of the comparison anomaly coefficients between the signal to be measured and all other component signals, so as to obtain the moment anomaly degree of the signal to be measured. The moment anomaly degree is the difference feature of the data fluctuation between the signal to be measured and all other component signals.

[0103] In the embodiments of the present invention, for each component signal, the moment anomaly degree corresponding to the same moment is calculated. Then, the average value is obtained and normalized to be used as the moment anomaly index of the data to be measured at the corresponding moment. The moment anomaly index is the abnormal situation obtained by combining signal decomposition analysis, and it can accurately represent the data fluctuation abnormality of the data to be measured at the corresponding moment.

[0104] S104: Adjust the initial transmission priority according to the moment anomaly index to obtain the corrected transmission priority, and perform communication transmission on all the time series state curve data at each moment according to the corrected transmission priority of all the time series state curve data at each moment.

[0105] In the embodiments of the present invention, after determining the moment anomaly index, the initial transmission priority of the time series state curve data itself can be corrected. The embodiments of the present invention can combine the moment anomaly index and the initial transmission priority to obtain the corrected transmission priority, and use the corrected transmission priority as the priority coefficient obtained after anomaly analysis of all the time series state curve data at each moment, so as to perform communication transmission at each moment according to the corrected transmission priority.

[0106] Furthermore, in some embodiments of the present invention, the method for obtaining the corrected transmission priority includes: calculating the product of the moment anomaly index and the initial transmission priority, and normalizing it to be used as the corrected transmission priority of the data to be measured at the corresponding moment.

[0107] In the embodiments of the present invention, the moment anomaly index can be used as the weight of the initial transmission priority to analyze the initial transmission priority. By calculating the product of the moment anomaly index and the initial transmission priority and normalizing it, the anomaly situation at the corresponding moment is combined with the initial transmission priority of the time series state curve data itself. Since the larger the initial transmission priority, the more abnormal the data change and numerical distribution of the time series state curve data itself, and the moment anomaly index corresponds to the abnormal data fluctuation at the corresponding moment.

[0108] Therefore, the larger the corrected transmission priority calculated in the embodiments of the present invention, that is, the corrected transmission priority of the data to be measured at a certain moment, the higher the abnormality of the data to be measured itself and the numerical fluctuation of the data to be measured at the corresponding moment. Then, the data to be measured at the corresponding moment is very likely to be abnormal data and needs to be transmitted and processed preferentially.

[0109] Furthermore, in some embodiments of the present invention, according to the corrected transmission priorities of all time series state curve data at each moment, all time series state curve data at each moment are communicated and transmitted, including: sorting all time series state curve data at the same moment in descending order of the corrected transmission priority, and transmitting all time series state curve data at that moment according to the sorting order.

[0110] In the embodiments of the present invention, since the larger the value of the corrected transmission priority, the more it needs to be transmitted preferentially, the present invention transmits all time series state curve data at the same moment in descending order of the transmission priority to improve the response efficiency to anomalies, timely discover potential fault signs or abnormal patterns, and thus take measures in advance for fault troubleshooting and prevention to improve the robustness and security of the system.

[0111] The present invention obtains various timing state curve data to be transmitted by a hub, determines the change characteristic indexes of each timing state curve data according to data changes and extreme value distributions, and determines the initial transmission priority by combining the differences in the change characteristic indexes and the numerical distribution differences of different timing state curve data. Then, the initial transmission priority can effectively represent the transmission situation of each timing state curve data. Since anomaly analysis needs to be performed during the transmission process at each moment, the embodiment of the present invention combines the signal time-frequency decomposition technology to implement signal analysis on each data to be measured, determines the decomposition characteristic coefficients according to the distribution of extreme points and the numerical characteristics of the instantaneous frequency, and combines the decomposition characteristic coefficients and signal values of different component signals at the same moment to implement the analysis of the moment anomaly indexes of different timing state curve data at the same moment. By combining the moment anomaly indexes to adjust the initial transmission priority, anomaly analysis can be performed on different timing state curve data, and the anomaly analysis results are combined with the initial transmission priority to obtain the adjusted transmission priority. When data is transmitted according to the adjusted transmission priority, transmission analysis can be performed for abnormal situations, the priority of all timing state curve data can be evaluated, so as to effectively and reasonably allocate the priorities of different data to be transmitted at the same moment, and improve the stability and real-time performance of data transmission and the response speed of the system to key information.

[0112] The present invention also provides a hub data intelligent communication system. The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a hub data intelligent communication method as described above are implemented.

[0113] This embodiment also provides a computer-readable storage medium. Computer program code is stored in the computer-readable storage medium. When the computer program code runs on a computer, the computer is caused to execute the above-related method steps to implement a hub data intelligent communication method provided by the above embodiment.

[0114] This embodiment also provides a computer program product. When the computer program product runs on a computer, the computer is caused to execute the above-related steps to implement a hub data intelligent communication method provided by the above embodiment.

[0115] Among them, the system, computer-readable storage medium, or computer program product provided in this embodiment are all used to execute the corresponding method provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, and will not be elaborated here.

[0116] It should be noted that the above order of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0117] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

Claims

1. A hub data intelligent communication method, characterized in that, The method comprises: Acquire at least two kinds of timing state curve data to be transmitted by the hub, and use any one kind of timing state curve data as the data to be tested; Determine the change characteristic index of the data to be tested according to the data change and extreme value distribution of the data to be tested at each moment; determine the initial transmission priority of the data to be tested according to the difference in the change characteristic index and value distribution between the data to be tested and each other time series state curve data; Perform signal time-frequency decomposition on the data to be tested to obtain component signals, take any component signal as the signal to be tested, and determine the decomposition characteristic coefficient of the signal to be tested according to the distribution of extreme value points in the signal to be tested and the numerical characteristics of the instantaneous frequency; determine the moment anomaly index of the data to be tested at the corresponding moment according to the difference in the decomposition characteristic coefficients and the difference in signal values of different component signals at the same moment; The initial transmission priority is adjusted according to the abnormal index at the moment to obtain a modified transmission priority, and all the timing state curve data at each moment are communicated and transmitted according to the modified transmission priority of all the timing state curve data at each moment; The method for obtaining the change characteristic index of the data to be measured includes: The absolute value of the difference between the two data values that are closest to each other in time sequence in the data to be tested is taken as the difference in the value changes of the two data values in their corresponding time intervals; Averaging the numerical variation differences of all time intervals in the data to be measured to obtain the numerical variation coefficient of the data to be measured; The mean of the data values of the data to be tested at all times is taken as the mean value to be tested; extreme value points are detected on the data values of the data to be tested at all times according to the time sequence to obtain the extreme value to be tested; The difference between any of the extreme values to be measured and the mean value to be measured is taken as the extreme value mean difference of the corresponding extreme value to be measured; the mean of the extreme value mean differences of all the extreme values to be measured is calculated as the extreme value distribution coefficient of the data to be measured; Determine a change characteristic index of the measured data according to the numerical change coefficient and the extreme value distribution coefficient, wherein both the numerical change coefficient and the extreme value distribution coefficient are positively correlated with the change characteristic index, and the value of the change characteristic index is a normalized value; The method for obtaining the decomposition characteristic coefficients of the signal to be measured comprises: Determine the time interval between two adjacent extreme value points in the signal to be measured as the extreme value time interval; take the reciprocal of the mean of all extreme value time intervals as the time change rate of the signal to be measured; The decomposition characteristic coefficient of the signal to be measured is determined according to the instantaneous frequency and the time change rate of the signal to be measured, wherein the instantaneous frequency and the time change rate are both positively correlated with the decomposition characteristic coefficient.

2. The data intelligent communication method of a hub according to claim 1, characterized in that, The method for obtaining the initial transmission priority of the data to be tested includes: Using other time series state curve data except the data to be tested as comparison data, selecting any one of the comparison data as the data to be analyzed, and determining the difference between the change characteristic index of the data to be tested and the data to be analyzed as the first transmission weight; Using a mean square error between the data to be measured and the data to be analyzed as a second transmission weight; Fuse the first transmission weight and the second transmission weight to obtain the transmission influence index of the data to be measured and the data to be analyzed; Normalize the mean of the transmission influence indexes of the data to be measured and each type of comparison data as the initial transmission priority of the data to be measured.

3. A hub data intelligent communication method according to claim 1, characterized in that Performing signal time-frequency decomposition on the data to be measured to obtain component signals, including: Based on the EMD decomposition algorithm, perform signal decomposition on the data to be measured to obtain IMF components, and use all IMF components as component signals.

4. The intelligent communication method for hub data according to claim 1, wherein The method for obtaining the moment anomaly index of the data to be measured at a corresponding moment includes: Taking the component signals other than the signal to be measured as other component signals, and at any moment, calculating the absolute value of the difference between the decomposition feature coefficients of the signal to be measured and any other component signal to obtain the first anomaly index of the signal to be measured and the corresponding other component signal; Taking the absolute value of the difference between the signal values of the signal to be measured and any other component signal as the second anomaly index of the signal to be measured and the corresponding other component signal; Determine the moment anomaly degree of the signal to be measured at the corresponding moment according to the first anomaly index and the second anomaly index of the signal to be measured and all other component signals; Normalize the mean of the moment anomaly degrees of all component signals at the same moment as the moment anomaly index of the data to be measured at the corresponding moment.

5. The intelligent communication method for hub data according to claim 4, characterized in that, The method for obtaining the moment anomaly degree of the signal to be measured includes: Fuse the first anomaly index and the second anomaly index of the signal to be measured and any other component signal to obtain the comparison anomaly coefficient of the signal to be measured and the corresponding other component signal, wherein both the first anomaly index and the second anomaly index are negatively correlated with the comparison anomaly coefficient; Take the mean of the comparison anomaly coefficients of the signal to be measured and all other component signals as the moment anomaly degree of the signal to be measured.

6. The intelligent communication method for hub data according to claim 1, characterized in that, The method for obtaining the corrected transmission priority includes: Calculate the product of the moment anomaly index and the initial transmission priority, and normalize it as the corrected transmission priority of the data to be measured at the corresponding moment.

7. A hub data intelligent communication method according to claim 1, characterized in that, According to the corrected transmission priorities of all time series state curve data at each moment, perform communication transmission on all time series state curve data at each moment, including: Sort all the time series state curve data at the same moment in descending order according to the corrected transmission priority, and transmit all the time series state curve data at that moment according to the sorting order.

8. A hub data intelligent communication system, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

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