Hub data intelligent communication method and system

Abstract

The invention relates to the technical field of communication processing, in particular to a concentrator data intelligent communication method and system. The method comprises the following steps: acquiring to-be-transmitted time sequence state curve data of a hub, and determining a change characteristic index according to data change and extreme value distribution; determining an initial transmission priority according to the difference of the change characteristic indexes and the value distribution difference; performing time-frequency decomposition on the signal to obtain a component signal, and determining a decomposition characteristic coefficient according to distribution of extreme points and numerical characteristics of instantaneous frequency; determining a moment anomaly index according to the difference of the decomposition characteristic coefficients and the difference of the signal values; and determining a corrected transmission priority, and performing communication transmission on all the time sequence state curve data at each moment according to the corrected transmission priority of all the time sequence state curve data at each moment. According to the method, the priority can be effectively and reasonably distributed, and the stability and the real-time performance of data transmission and the response speed to key information are improved.

Description

Technical Field

[0001] The present invention relates to the field of communication processing technology, and in particular to a hub data intelligent communication method and system. Background Art

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

[0003] In the related art, the importance of the data to be transmitted is usually 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 based on the analysis of the data type, the importance of the data entity is characterized, and thus it is impossible to effectively perform specific transmission allocation for abnormal data. As a result, the response speed to key information is slow, the efficiency of transmission priority allocation is low, and the stability of data transmission is poor. Summary of the invention

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

[0005] The present invention proposes a hub data intelligent communication method, the method comprising:

[0006] 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;

[0007] 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;

[0008] 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;

[0009] 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.

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

[0011] 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;

[0012] 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;

[0013] 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;

[0014] 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;

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

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

[0017] 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;

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

[0019] The first transmission weight and the second transmission weight are combined to obtain transmission impact indicators of the data to be tested and the data to be analyzed;

[0020] The average values ​​of the transmission impact indicators of the data to be tested and each type of comparison data are normalized and used as the initial transmission priority of the data to be tested.

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

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

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

[0024] 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;

[0025] 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.

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

[0027] Taking component signals other than the signal to be tested as other component signals, at any time, calculating the absolute value of the difference between the decomposition characteristic coefficients of the signal to be tested and any other component signal, and obtaining a first abnormality indicator of the signal to be tested and the corresponding other component signal;

[0028] The absolute value of the difference between the signal value of the signal to be tested and any other component signal is used as a second abnormality indicator of the signal to be tested and the corresponding other component signal;

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

[0030] The mean of the momentary anomaly levels of all component signals at the same moment is normalized and used as the momentary anomaly index of the data to be measured at the corresponding moment.

[0031] Furthermore, the method for obtaining the abnormality degree of the signal to be measured at a certain moment includes:

[0032] Fusing the first abnormality index and the second abnormality index of the signal to be tested and any other component signal to obtain a comparative abnormality coefficient between the signal to be tested and the corresponding other component signal, wherein both the first abnormality index and the second abnormality index are negatively correlated with the comparative abnormality coefficient;

[0033] The average of the abnormality coefficients of the comparison between the signal to be tested and all other component signals is taken as the momentary abnormality degree of the signal to be tested.

[0034] Furthermore, the method for obtaining the modified transmission priority includes:

[0035] The product of the abnormal index at the time and the initial transmission priority is calculated and normalized as the corrected transmission priority of the data to be measured at the corresponding time.

[0036] Furthermore, the communication transmission of all the time series status curve data at each moment according to the modified transmission priority of all the time series status curve data at each moment includes:

[0037] All the timing state curve data at the same time are sorted in descending order according to the modified transmission priority, and all the timing state curve data at the time are transmitted in the sorted 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, it implements the steps of the hub data intelligent communication method described above.

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

[0040] The present invention obtains a variety of timing state curve data to be transmitted by the hub, and determines the change characteristic index of each timing state curve data according to the data change and extreme value distribution, and determines the initial transmission priority by combining the difference in the change characteristic index and the difference in the numerical distribution of different timing state curve data. The initial transmission priority can effectively characterize the transmission status of each timing state curve data. Since abnormal analysis is required at each moment in the transmission process, the embodiment of the present invention combines the signal time-frequency decomposition technology to realize the signal analysis of each data to be tested, determines the decomposition characteristic coefficient according to the distribution of extreme value points and the numerical characteristics of instantaneous frequency, and combines different component signals at the same moment. The decomposed characteristic coefficients and signal values ​​are used to analyze the moment anomaly indicators of different timing state curve data at the same moment; the initial transmission priority is adjusted in combination with the moment anomaly indicators, and the abnormal analysis of different timing state curve data can be performed, and the abnormal analysis results are combined 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, and the priority of all timing state curve data can be evaluated, thereby effectively allocating the priority of different data to be transmitted at the same moment, improving the stability and real-time performance of data transmission and the response speed to key information. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A flow chart of a hub data intelligent communication method provided by one embodiment of the present invention;

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

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

[0045] Figure 4 A schematic diagram of a method for obtaining a change characteristic indicator provided by an embodiment of the present invention;

[0046] 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;

[0047] Figure 6 A schematic diagram of component signals provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the hub data intelligent communication method and system proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

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

[0050] The specific scheme of the hub data intelligent communication method provided by the present invention is described in detail below with reference to the accompanying drawings.

[0051] See also Figure 1 , which shows a flow chart of a hub data intelligent communication method provided by an embodiment of the present invention, the method comprising:

[0052] S101: Acquire at least two types of timing state curve data to be transmitted by a hub, and use any one type of timing state curve data as data to be tested.

[0053] The specific implementation scenario of the embodiment of the present invention may be, for example: in a data transmission scenario involving a hub, such as Figure 2 As shown, Figure 2 A schematic diagram of a hub data transmission scenario provided by an embodiment of the present invention, with a hub as a central node, receiving the timing state curve data sent by multiple sensors. Since the hub is a device for comprehensive analysis and transmission of multiple types of different data, multiple different types of sensors can be connected to the hub, and the hub has interfaces and communication protocols adapted to various sensors to connect and exchange data with the sensors; different types of sensor interfaces can be used, such as UART, SPI, I2C, etc., or through a universal interface module; the hub is configured to collect data from the sensor in a periodic or time-triggered manner, and the collected data is processed and sorted, and operations such as data format conversion, unit conversion, and data verification are designed to ensure the accuracy and availability of the data; ensure that the hub supports common communication protocols, such as Modbus, MQTTQ, HTTP, etc., so that the sensor data can be transmitted to the upper system or other devices through the network. Thus, the data collected by the sensor to be transmitted using the hub is used as the timing state curve data.

[0054] Of course, the device connected to the hub for data transmission in the embodiment of the present invention is not limited to a sensor, but may also be a variety of other devices or other data sources. For ease of understanding, the embodiment of the present invention uses a sensor for specific analysis, and other forms of data sources also fall within the protection scope of the embodiment of the present invention and are not limited to this.

[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)) or a wireless link (such as but not limited to wireless Fidelity (WIFI), Bluetooth and mobile device network).

[0056] The timing state curve data in the embodiment of the present invention is the data collected by all sensors at the same time point within a certain time period. Since sensor data generally refers to a device used to detect and measure specific parameters of the environment or objects, these parameters may include but are not limited to temperature, humidity, pressure, motion state, etc., the physical quantity is converted into the form of electrical signal curve data through the sensor, that is, the timing state curve data is a kind of electrical signal curve data in a timing series.

[0057] For example, an acquisition cycle of the embodiment of the present invention is 1 second, that is, to acquire the electrical signal data to be transmitted by the hub within 1 second, such as the electrical signal data representing the temperature, then a certain acquisition cycle includes multiple data acquisition moments, for example, when the acquisition cycle is 1 second, 100 data values ​​are collected within 1 second, then the collection interval of each data value is 0.01 second, and the 100 data values ​​are combined into a time series state curve data according to the time series. Figure 3 As shown, Figure 3 A schematic diagram of timing state curve data provided by an embodiment of the present invention.

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

[0059] S102: 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.

[0060] It is understandable that since the corresponding data changes between the sensor and the hub may be abnormal during the data transmission process, the abnormality may be due to changes in the actual state, such as a sudden increase in temperature, or it may be due to transmission abnormalities, such as transmission noise generated during the data transmission process. In the actual data transmission process, the abnormal data change situation requires a faster response time so that the exception can be handled in time. Therefore, in the data transmission process, the abnormal data change situation needs to be transmitted first.

[0061] In an embodiment of the present invention, since the timing status curve data acquired by the hub are of various types, but their sources are usually in the same environmental system, such as the temperature, humidity, air pressure and other weather systems in a certain area, after measurement using multiple different sensors, data transmission is performed based on the same hub. The data states of different timing status curve data have corresponding correlation relationships, and thus, the analysis of the initial transmission priority can be implemented based on the correlation relationship.

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

[0063] S401: taking the absolute value of the difference between two data values ​​that are closest to each other in time sequence in the data to be tested as the difference in numerical value changes of 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, and 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: in the process of calculating the mean square error, it is necessary to ensure that the vertical coordinate of the data change curve maintains a uniform dimension. Data standardization can be used to operate the time series to ensure a uniform dimension.

[0082] Of course, in other embodiments of the present invention, a variety of other difference calculation methods can also be used to determine the second transmission weight, as long as the greater the numerical difference between the data to be tested and the data to be analyzed, the greater the second transmission weight. For example, the data to be tested and the data to be analyzed can be taken as time series, so as to determine the dtw values ​​of the two sequences based on the dynamic time warping algorithm, and the dtw values ​​can be normalized as the second transmission weight, and there is no limitation on this.

[0083] S503: The first transmission weight and the second transmission weight are integrated to obtain transmission impact indicators of the data to be tested and the data to be analyzed; the average value of the transmission impact indicators of the data to be tested and each type of comparison data is normalized and used as the initial transmission priority of the data to be tested.

[0084] In an embodiment of the present invention, a difference analysis is performed on the data to be tested and other timing state curve data through the difference in change characteristic indicators and the mean square error between the data to be tested and other timing state curve data. The product of the first transmission weight and the second transmission weight can be calculated as the transmission impact index, or the sum of the first transmission weight and the second transmission weight can be calculated as the transmission impact index, and there is no limitation on this.

[0085] In other words, the larger the value of the initial transmission priority, the greater the fluctuation difference between the data to be tested and all other timing state curve data. In other words, the more abnormal the data to be tested itself is, the more it needs to ensure priority transmission so that timely response can be made to the abnormal data.

[0086] It should be noted that the initial transmission priority only represents the abnormality of the fluctuation of the type of data to be tested. However, in the actual transmission process, due to the inconsistent changes in various environmental factors at different times, it is also necessary to consider the transmission analysis at each moment. For specific analysis, please refer to the subsequent embodiments.

[0087] S103: Perform 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 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.

[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 embodiment of the present invention, since in the process of empirical mode decomposition, the component signals are arranged in order from high frequency to low frequency, and there are large differences between adjacent components, and the frequency characteristics between the components are similar and there are similarities between data points at the same position, it can be said that the data point at that position is abnormal.

[0102] In the embodiment of the present invention, the smaller the value of the first abnormality index is, the smaller the difference in the decomposition characteristic coefficients at the corresponding moment is, that is, the more abnormal the signal to be measured is at the corresponding moment, and the smaller the second abnormality index is, the smaller the difference in the data value at the corresponding moment is, which can also characterize that the signal to be measured is more abnormal. Therefore, the embodiment of the present invention can calculate the product of the first abnormality index and the second abnormality index, normalize the negative number of the product as the comparison abnormality coefficient, and traverse all other component signals to determine the mean of the comparison abnormality coefficients of the signal to be measured and all other component signals, and obtain the moment abnormality degree of the signal to be measured, then the moment abnormality degree is the difference characteristic of the data fluctuation between the signal to be measured and all other component signals.

[0103] In an embodiment of the present invention, the corresponding moment anomaly degree of each component signal at the same moment is calculated, and then the average and normalization are processed 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 the signal decomposition analysis, which can accurately represent the data fluctuation abnormality of the data to be measured at the corresponding moment.

[0104] S104: adjusting the initial transmission priority according to the abnormal index at each moment to obtain a modified transmission priority, and performing communication transmission on all the timing state curve data at each moment according to the modified transmission priority of all the timing state curve data at each moment.

[0105] In the embodiment of the present invention, after determining the moment abnormality index, the initial transmission priority of the timing state curve data itself can be corrected. In the embodiment of the present invention, the moment abnormality index and the initial transmission priority can be combined to achieve the acquisition of the corrected transmission priority, and the corrected transmission priority is used as the priority coefficient obtained after the abnormality analysis of all the timing state curve data at each moment, so as to achieve the 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 modified transmission priority includes: calculating the product of the moment anomaly index and the initial transmission priority, and normalizing the product as the modified transmission priority of the data to be tested at the corresponding moment.

[0107] In the embodiment 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 them, the abnormal 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 value distribution of the time series state curve data itself, and the moment anomaly index corresponds to the abnormal data fluctuation at the moment.

[0108] Therefore, the corrected transmission priority calculated by the embodiment of the present invention, that is, the larger the corrected transmission priority of the data to be tested at a certain moment, the higher the abnormality of the data to be tested itself and the numerical fluctuation of the data to be tested at the corresponding moment, and the data to be tested at the corresponding moment is very likely to be abnormal data, which requires priority transmission and priority processing.

[0109] Furthermore, in some embodiments of the present invention, all timing status curve data at each moment are communicated and transmitted according to the modified transmission priority of all timing status curve data at each moment, including: sorting all timing status curve data at the same moment in descending order according to the modified transmission priority, and transmitting all timing status curve data at that moment in the sorted order.

[0110] In an embodiment of the present invention, since a larger value of the modified transmission priority indicates a greater need for priority transmission, the present invention transmits all timing state curve data at the same time in order of transmission priority from large to small, so as to improve the efficiency of response to anomalies, timely discover potential signs of failure or abnormal modes, and take measures in advance to troubleshoot and prevent failures, thereby improving the robustness and security of the system.

[0111] The present invention obtains a variety of timing state curve data to be transmitted by the hub, and determines the change characteristic index of each timing state curve data according to the data change and extreme value distribution, and determines the initial transmission priority by combining the difference in the change characteristic index and the difference in the numerical distribution of different timing state curve data. The initial transmission priority can effectively characterize the transmission status of each timing state curve data. Since abnormal analysis is required at each moment in the transmission process, the embodiment of the present invention combines the signal time-frequency decomposition technology to realize the signal analysis of each data to be tested, determines the decomposition characteristic coefficient according to the distribution of extreme value points and the numerical characteristics of instantaneous frequency, and combines the different component signals at the same moment. Decompose the characteristic coefficients and signal values ​​to realize the analysis of the moment anomaly indicators of different timing state curve data at the same time; adjust the initial transmission priority in combination with the moment anomaly indicators, and be able to perform anomaly analysis on different timing state curve data, and combine the anomaly analysis results with the initial transmission priority to obtain the adjusted transmission priority, so that when data is transmitted according to the adjusted transmission priority, it is possible to perform transmission analysis for abnormal situations and evaluate the priority of all timing state curve data, thereby effectively and reasonably allocating the priority of different data to be transmitted at the same time, improving the stability and real-time performance of data transmission and the system's response speed to key information.

[0112] 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 the aforementioned hub data intelligent communication method are implemented.

[0113] This embodiment also provides a computer-readable storage medium, in which a computer program code is stored. When the computer program code is executed on a computer, the computer executes the above-mentioned 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, it enables the computer to execute the above-mentioned 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 is 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 repeated here.

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

[0117] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on 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.

2. A hub data intelligent communication method as claimed in claim 1, characterized in that: 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; The change characteristic index of the data to be measured is determined 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 numerical value.

3. A hub data intelligent communication method as claimed in 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; The first transmission weight and the second transmission weight are combined to obtain transmission impact indicators of the data to be tested and the data to be analyzed; The average values ​​of the transmission impact indicators of the data to be tested and each type of comparison data are normalized and used as the initial transmission priority of the data to be tested.

4. A hub data intelligent communication method as claimed in claim 1, characterized in that: The step of performing signal time-frequency decomposition on the data to be measured to obtain component signals includes: Based on the EMD decomposition algorithm, the data to be measured is subjected to signal decomposition to obtain IMF components, and all IMF components are used as component signals.

5. A hub data intelligent communication method as claimed in claim 4, characterized in that: 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.

6. A hub data intelligent communication method as claimed in claim 1, characterized in that: The method for obtaining the moment anomaly index of the data to be measured at the corresponding moment includes: Taking component signals other than the signal to be tested as other component signals, at any time, calculating the absolute value of the difference between the decomposition characteristic coefficients of the signal to be tested and any other component signal, and obtaining a first abnormality indicator of the signal to be tested and the corresponding other component signal; The absolute value of the difference between the signal value of the signal to be tested and any other component signal is used as a second abnormality indicator of the signal to be tested and the corresponding other component signal; Determine the moment abnormality degree of the signal to be tested at the corresponding moment according to the first abnormality index and the second abnormality index of the signal to be tested and all other component signals; The mean of the momentary anomaly levels of all component signals at the same moment is normalized and used as the momentary anomaly index of the data to be measured at the corresponding moment.

7. A hub data intelligent communication method as claimed in claim 6, characterized in that: The method for obtaining the abnormality degree of the signal to be measured at a certain moment includes: Fusing the first abnormality index and the second abnormality index of the signal to be tested and any other component signal to obtain a comparative abnormality coefficient between the signal to be tested and the corresponding other component signal, wherein both the first abnormality index and the second abnormality index are negatively correlated with the comparative abnormality coefficient; The average of the abnormality coefficients of the comparison between the signal to be tested and all other component signals is taken as the momentary abnormality degree of the signal to be tested.

8. A hub data intelligent communication method as claimed in claim 1, characterized in that: The method for obtaining the modified transmission priority includes: The product of the abnormal index at the time and the initial transmission priority is calculated and normalized as the corrected transmission priority of the data to be measured at the corresponding time.

9. A hub data intelligent communication method as claimed in claim 1, characterized in that: The communication transmission of all the time series status curve data at each moment according to the modified transmission priority of all the time series status curve data at each moment includes: All the timing state curve data at the same time are sorted in descending order according to the modified transmission priority, and all the timing state curve data at the time are transmitted in the sorted order.

10. 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, the steps of the method according to any one of claims 1 to 9 are implemented.

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