Intelligent optimization and real-time decision platform for industrial big data

Abstract

The invention relates to the technical field of industrial intelligent decision-making, in particular to an intelligent optimization and real-time decision-making platform for industrial big data, and the platform comprises a node data time distribution analysis module which obtains the operation state parameters of product production nodes from the industrial big data and converts the operation state parameters into a time sequence; and extracting the characteristic value of the operation state parameter in each time period of the time sequence, counting the occurrence frequency of the characteristic value in the corresponding time period, and generating node time period distribution information. According to the method, the change characteristics and frequency distribution of parameters in different time periods can be accurately reflected by acquiring the time sequence distribution characteristics of the operation state parameters of each node in industrial production. By capturing the trend change of the operation state parameter and the key moment when the preset change threshold value is exceeded in time, the sensitivity and the accuracy of abnormal state detection are improved. The global coordination of parameter adjustment is optimized by analyzing the upper and lower limits of the range of the abnormal fluctuation point and the cross-stage association strength thereof.

Description

Technical Field

[0001] The present invention relates to the field of industrial intelligent decision-making technology, and in particular to an intelligent optimization and real-time decision-making platform for industrial big data. Background Art

[0002] The intelligent optimization and real-time decision-making platform for industrial big data refers to a platform that conducts real-time multi-source data collection, dynamic optimization and decision-making for the massive data generated in the industrial production process. It combines the different needs of the industrial production process and realizes dynamic management and real-time decision support of the production process through an integrated platform.

[0003] In the analysis of industrial production data, the existing technology can only optimize based on static parameters or data from a single stage, and it is difficult to fully capture the dynamic changes in the node operating status parameters. For example, the existing methods can usually only analyze the operating status parameters based on statistical values ​​or historical data, but cannot accurately extract key change points from the time series, resulting in a lag in the response of anomaly detection to key changes. In terms of the evaluation of the parameter fluctuation range, the existing technology can only make abnormal judgments, and it is difficult to accurately define the abnormal state. For example, when a parameter has multiple small fluctuations during the production process, it is difficult for the existing technology to distinguish whether the fluctuations belong to the normal operating range, which may lead to false positives or false negatives. In terms of cross-stage correlation optimization, the analysis of the existing technology is mostly limited to the optimization of parameters of a single node or a single stage, and it is difficult to coordinate the dynamic adjustment of global production. For example, the adjustment of operating status parameters between different nodes may cause an imbalance in production efficiency between stages due to the lack of global optimization guidance, which is easy to lead to limited overall optimization effects and affect the improvement of production efficiency and quality. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent optimization and real-time decision-making platform for industrial big data.

[0005] In order to achieve the above purpose, the present invention adopts the following technical solution: An intelligent optimization and real-time decision-making platform for industrial big data includes:

[0006] The node data time distribution analysis module obtains the operating status parameters of product production nodes from industrial big data and converts them into time series, extracts the characteristic values ​​of the operating status parameters in each period of the time series, counts the frequency of occurrence of the characteristic values ​​in the corresponding period, and generates node time period distribution information;

[0007] The node distribution dynamic evolution module performs dynamic trend evolution on the time series corresponding to the operating state parameters based on the node time period distribution information, generates dynamic trend evolution results, marks the time points when the operating state parameters in the dynamic trend evolution results change beyond a preset change threshold, and generates node distribution change point information;

[0008] The node fluctuation range assessment module analyzes the time series corresponding to the operating state parameters adjacent to the marked time points in the node distribution change point information, determines the upper and lower limits of the fluctuation range of the target operating state parameters according to the analysis results, marks the abnormal change points according to the upper and lower limits of the fluctuation range, and generates node abnormal fluctuation information;

[0009] The node multi-stage association optimization module obtains the abnormal operation state parameter optimization interval from the upper and lower limits of the fluctuation range of the target operation state parameter corresponding to the abnormal change point in the abnormal fluctuation information of the node and the optimal operation range of the preset operation state parameter, analyzes the association strength between the abnormal operation state parameter optimization interval and the entire product production node, and generates a cross-stage parameter optimization analysis result;

[0010] The node parameter suggestion generation module determines the adjustment sequence of different operating status parameters according to the correlation strength of all operating status parameters in the cross-stage parameter optimization analysis results, and generates a node parameter optimization decision report according to the adjustment sequence.

[0011] As a further solution of the present invention, the step of acquiring the node time period distribution information is specifically as follows:

[0012] Obtain the operating status parameters of product production nodes from industrial big data and convert them into time series. Divide the time series into different time periods, obtain the characteristic values ​​of the operating status parameters in each time period, including the maximum value, minimum value and quantile value, and generate a set of operating status parameter characteristic values.

[0013] The occurrence frequency of each characteristic value in the operating status parameter characteristic value set in the corresponding time period is counted, and a histogram is constructed according to the occurrence frequency to display the frequency distribution of the operating status parameters. The frequency distribution is smoothly fitted using the kernel density estimation method to generate the node time period distribution information.

[0014] As a further solution of the present invention, the steps of obtaining the dynamic trend evolution result are specifically as follows:

[0015] Based on the node time period distribution information, the formula is adopted:

[0016]

[0017] In the time series corresponding to the calculation of the operating status parameter, the operating status parameter at the previous moment is S t-1 When the current operating state parameter is S t The conditional probability P(S t |S t-1 ), generating the state change probability of the operating state parameters;

[0018] Among them, A(St-1 ,S t ) is obtained by counting the number of changes of the operating state parameters in the time series corresponding to the operating state parameters, and the operating state parameter S at the previous moment t-1 Transfer to the current operating state parameter S t The probability of n is the total number of all operating state parameters, B(S t ,O t ) is obtained based on the frequency of occurrence of each characteristic value in the operating state parameter characteristic value set in the corresponding time period. t The specified operating state parameter O is observed t The observation probability of k represents the index of all operating state parameters;

[0019] According to the state change probability of the operating state parameters, the change rules of the operating state parameters in different periods are analyzed to generate dynamic trend evolution results.

[0020] As a further solution of the present invention, the step of obtaining the node distribution change point information is specifically as follows:

[0021] Based on the dynamic trend evolution result, by calculating the change amount of the operating state parameter in the time series corresponding to the operating state parameter point by point, marking the time point when the change amount exceeds the preset change threshold, wherein the marked time point represents the moment when a key change occurs in the time series corresponding to the operating state parameter, and generating a marked time point set;

[0022] The change types and distribution density corresponding to the operating status parameters in the marked time point set are classified and counted, and the distribution law of each marked time point is analyzed in combination with the time interval of the time point to generate node distribution change point information.

[0023] As a further solution of the present invention, the step of determining the upper and lower limits of the fluctuation range of the target operating state parameter is specifically:

[0024] The time series corresponding to the running state parameters adjacent to the marked time points in the node distribution change point information are analyzed using the formula:

[0025]

[0026] Calculate the speed v at which the target operating state parameter deviates from the current value x to the corresponding mean value μ and the fluctuation range σ of the target operating state parameter respectively;

[0027] Among them, k' represents the intensity of the target operating state parameter returning to the mean after deviating from the corresponding mean, is the proportional coefficient, x is the current value of the target operating state parameter, μ represents the mean corresponding to the target operating state parameter, is the instantaneous change rate of the adjacent operating state parameters, β is the instantaneous change rate weight coefficient set according to the instantaneous change rate, D represents the basic value of the fluctuation range of the target operating state parameter, Δx is the fluctuation increment, which represents the difference between the maximum and minimum values ​​in the time series corresponding to the operating state parameter, and γ is the fluctuation range adjustment coefficient set according to the fluctuation increment;

[0028] According to the speed v at which the target operating state parameter deviates from the current value x to the corresponding mean value μ and the fluctuation range σ of the target operating state parameter, the formula is adopted:

[0029] [x min ,x max ]=[μ-(σ+v·Δt),μ+(σ+v·Δt)]

[0030] Determine the upper limit x of the fluctuation range of the target operating state parameter max and the lower limit x min ;

[0031] Among them, Δt represents the difference between adjacent time points in the time series corresponding to the operating status parameter.

[0032] As a further solution of the present invention, the step of obtaining the abnormal node fluctuation information is specifically as follows:

[0033] Compare the upper and lower limits of the fluctuation range of the target operating state parameter with the operating state parameter adjacent to the marked time point, mark the abnormal change points exceeding the upper and lower limits of the fluctuation range according to the comparison result, and generate the abnormal change point marking result;

[0034] Based on the abnormal change point marking results, the distribution characteristics of all abnormal change points are classified and sorted to generate node abnormal fluctuation information.

[0035] As a further solution of the present invention, the step of obtaining the node parameter optimization decision report is specifically as follows:

[0036] Determine the importance of different operating status parameters according to the correlation strength of all operating status parameters in the cross-stage parameter optimization analysis result, assign the operating status parameter adjustment order priority according to the importance, and generate the operating status parameter priority adjustment result;

[0037] The operation status parameter priority adjustment result is displayed in a visual manner to generate a node parameter optimization decision report.

[0038] Compared with the prior art, the advantages and positive effects of the present invention are:

[0039] In the present invention, by obtaining the time series distribution characteristics of the operating status parameters of each node in industrial production, the change characteristics and frequency distribution of the parameters in different time periods can be accurately reflected. By timely capturing the trend changes of the operating status parameters and the key moments exceeding the preset change threshold, the sensitivity and accuracy of abnormal state detection are improved. By analyzing the upper and lower limits of the range of abnormal fluctuation points and their cross-stage correlation strength, the global coordination of parameter adjustment is optimized, making the optimization of operating status parameters at different stages more efficient. At the same time, by adjusting the priority, it can be ensured that the parameter adjustment process has a clear execution order, and the optimization decision is more accurate and efficient. In the multi-stage optimization analysis, the correlation between different operating status parameters is systematically considered, which further improves the effect of global optimization and enhances the adaptability of the industrial production process to complex dynamic changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flow chart of the platform of the present invention;

[0041] Figure 2 A flow chart of obtaining node time period distribution information of the present invention;

[0042] Figure 3 A flow chart of obtaining dynamic trend evolution results for the present invention;

[0043] Figure 4 A flowchart of obtaining node distribution change point information of the present invention;

[0044] Figure 5 A flow chart for determining the upper and lower limits of the fluctuation range of the target operating state parameter of the present invention;

[0045] Figure 6 A flowchart of the present invention for obtaining abnormal node fluctuation information;

[0046] Figure 7 A flow chart for obtaining cross-stage parameter optimization analysis results for the present invention;

[0047] Figure 8 The present invention is a flowchart for obtaining a node parameter optimization decision report. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0049] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0050] See also Figure 1 , an intelligent optimization and real-time decision-making platform for industrial big data includes:

[0051] The node data time distribution analysis module obtains the operating status parameters of product production nodes from industrial big data and converts them into time series, extracts the characteristic values ​​of the operating status parameters in each period of the time series, counts the frequency of occurrence of the characteristic values ​​in the corresponding period, and generates node time period distribution information;

[0052] The node distribution dynamic evolution module performs dynamic trend evolution on the time series corresponding to the operating status parameters based on the node time period distribution information, generates dynamic trend evolution results, marks the time points when the operating status parameters in the dynamic trend evolution results change beyond the preset change threshold, and generates node distribution change point information;

[0053] The node fluctuation range assessment module analyzes the time series corresponding to the operating state parameters adjacent to the marked time points in the node distribution change point information, determines the upper and lower limits of the fluctuation range of the target operating state parameters according to the analysis results, marks the abnormal change points according to the upper and lower limits of the fluctuation range, and generates node abnormal fluctuation information;

[0054] The node multi-stage association optimization module obtains the abnormal operation state parameter optimization interval from the upper and lower limits of the fluctuation range of the target operation state parameter corresponding to the abnormal change point in the node abnormal fluctuation information and the optimal operation range of the preset operation state parameter, analyzes the correlation strength between the abnormal operation state parameter optimization interval and the entire product production node, and generates cross-stage parameter optimization analysis results;

[0055] The node parameter recommendation generation module determines the adjustment order of different operating status parameters according to the correlation strength of all operating status parameters in the cross-stage parameter optimization analysis results, and generates a node parameter optimization decision report based on the adjustment order.

[0056] See also Figure 2 ,The specific steps for obtaining the node time period distribution information are:

[0057] Obtain the operating status parameters of product production nodes from industrial big data and convert them into time series. Divide the time series into different time periods, obtain the characteristic values ​​of the operating status parameters in each time period, including the maximum value, minimum value and quantile value, and generate a set of operating status parameter characteristic values.

[0058] Extract the operating status parameters of product production nodes from industrial big data, such as the temperature (unit: °C), pressure (unit: MPa) and flow (unit: m 3 / h), etc., by sorting the timestamps of these parameters to form time series data, and dividing the time series into different time periods (such as one time period per hour), and calculating the maximum, minimum and quantile values ​​(such as 25%, 50%, 75% quantiles) of the equipment operation status parameters in each time period.

[0059] The occurrence frequency of each characteristic value in the characteristic value set of the running status parameter in the corresponding time period is counted, and a histogram is constructed according to the occurrence frequency to show the frequency distribution of the running status parameter. The frequency distribution is smoothed and fitted using the kernel density estimation method to generate the node time period distribution information;

[0060] Perform frequency statistics on the acquired eigenvalue set. Taking the device temperature as an example, the maximum value, minimum value and quantile value are grouped and counted. By setting the numerical interval (such as 40℃ to 50℃, 50℃ to 60℃, 60℃ to 70℃, etc.), the frequency of occurrence of the eigenvalues ​​in each interval is counted. Assuming that the statistical result is that the interval 40℃ to 50℃ appears 10 times, the frequency of occurrence 50℃ to 60℃ appears 20 times, the frequency of occurrence 60℃ to 70℃ appears 15 times, and the frequency of occurrence 70℃ to 80℃ appears 5 times, the statistical result is drawn according to the statistical result Histogram, the horizontal axis of the histogram is the temperature range, and the vertical axis is the frequency of occurrence of the corresponding range. The unfitted histogram presents a discrete columnar distribution, reflecting the frequency changes of parameters in different intervals; the histogram data is smoothly fitted using the kernel density estimation method, for example, a Gaussian kernel function is selected to convert the columnar distribution into a continuous and smooth distribution curve. The fitted distribution curve eliminates the abruptness of the columnar distribution and can more clearly display the frequency change trend of each temperature range, generating node time period distribution information including the time axis and the temperature distribution fitting curve.

[0061] See also Figure 3 , the specific steps for obtaining the dynamic trend evolution results are:

[0062] Based on the node time period distribution information, the formula is used:

[0063]

[0064] In the time series corresponding to the calculation of the operating status parameter, the operating status parameter at the previous moment is St-1 When the current operating state parameter is S t The conditional probability P(S t |S t-1 ), generating the state change probability of the operating state parameters;

[0065] Among them, A(S t-1 ,S t ) is obtained by counting the number of changes of the operating state parameters in the time series corresponding to the operating state parameters, and the operating state parameter S at the previous moment t-1 Transfer to the current operating state parameter S t The probability is calculated by the following formula: N(S t-1 ,S t ) represents the running state parameter S at the last moment t-1 Transfer to the current operating state parameter S t The number of changes, is the operating state parameter S at the previous moment t-1 The total number of changes to all operating state parameters, n represents the total number of all operating state parameters (for example, when the operating state parameters are divided into 3 intervals, n = 3), j represents the index of each target operating state parameter (for example, j = 1, 2, 3 correspond to operating state parameters 1, 2, 3 respectively), and the calculation of the total number includes the following steps: t-1 The number of times N(S) the parameters are transferred to each operating state t-1 ,S j ), B(S t ,O t ) is obtained based on the frequency of occurrence of each characteristic value in the operating state parameter characteristic value set in the corresponding time period. t The specified operating state parameter O is observed t The observation probability, for a specified operating state parameter O t For example, if the temperature at a certain moment is observed to be 65°C, then O t =65℃, B(S t ,O t ) is calculated by the following formula: F(O t ,S t ) indicates that the current operating state parameter S t The specified operating state parameter O is observed t The frequency, Indicates the current operating state parameter S t The total frequency of all operating state parameters observed under i Indicates the current operating state parameter S tThe i-th observed value observed under, m represents the total number of observed values ​​in the current operating state parameter, t represents the time point of each operating state parameter in the time series corresponding to the operating state parameter, Indicates all operating status parameters S k The sum of is used to normalize the state change probability, and the operating state parameter set S k It is determined by dividing the operating state parameter interval generated in the operating state parameter characteristic value set (for example, the temperature interval 50℃-60℃ is operating state parameter 1, 60℃-70℃ is operating state parameter 2, and 70℃-80℃ is operating state parameter 3), and k represents the index of all operating state parameters.

[0066] Assume that the operating state parameter 1 (temperature range 50℃-60℃) at the previous moment is changed to the operating state parameter 2 (temperature range 60℃-70℃) at the current moment, and the observed value O t =65℃.

[0067] Operation state parameter transition probability A(S t-1 ,S t ) It is necessary to count the number of times the running state parameter 1 is transferred to the running state parameter 2, as well as the total number of times the running state parameter 1 is transferred to all the running state parameters: if the number of times from the running state parameter 1 to the running state parameter 2 is N(S t-1 ,S t )=30; if the number of times from running state parameter 1 to running state parameter 3 is N(S t-1 , S3) = 10; if the number of times the running state parameter 1 is maintained at the running state parameter 1 is N (S t-1 ,S1)=60. The total number of times is:

[0068] but:

[0069] Observation probability B(S t ,O t ) indicates that the parameter S t The parameter value O is observed t The probability of . Use the formula to calculate: If the frequency of the parameter value 65°C is observed under operating state parameter 2, F(O t =65,S t =2)=6; if the frequency of the parameter value 62°C is observed under the operating state parameter 2, F(O i =62,S t =2)=4; if the total frequency under operating state parameter 2 is Substituting into the formula:

[0070] Sum all possible operating state parameters: A(S t-1 ,S1)=0.6,B(S1,O t )=0.4(the probability of observing 65℃ under operating state parameter 1 is 0.4); A(S t-1 ,S2)=0.3,B(S2,O t )=0.6;A(S t-1 ,S3)=0.1,B(S3,O t )=0.2 (the probability of observing 65°C under operating state parameter 3 is 0.2). Calculation:

[0071] Substitute into the formula:

[0072] The results show that the probability of change from operating state parameter 1 (temperature range 50°C-60°C) to operating state parameter 2 (temperature range 60°C-70°C) is 40.91%. The same is true for the calculation of pressure and flow operating state parameters.

[0073] The hidden Markov model combines the state transition probability A(S t-1 ,S t ) and the observation probability B(S t ,O t ), and infer and calculate the probability of the operating state parameters at different times. Specifically, the hidden Markov model regards the operating state parameters as implicit variables, and calculates the conditional probability of state change P(S t |S t-1 ), the operating state parameters of the previous moment and the observed values ​​are used to update the operating state parameters of the current moment, and the probability sequence of the operating state parameter changes at each moment is obtained. This process combines the dynamic evolution trend of the time series and the distribution characteristics of the observed values, making the analysis of state changes more consistent with the actual operating rules.

[0074] According to the state change probability of the operating state parameters, the changing rules of the operating state parameters in different periods are analyzed to generate dynamic trend evolution results;

[0075] According to the calculated operating state parameter change probability P(S t |S t-1)=0.4091, combined with the change trend of the operating state parameters in the time series, the change law of the operating state parameters is analyzed. Assume that the changes of the operating state parameters in the time series are as follows: at t1, the operating state parameter is S1 (temperature range 50℃-60℃); at t2, the operating state parameter is S2 (temperature range 60℃-70℃); at t3, the operating state parameter is S3 (temperature range 70℃-80℃). From t1 to t2, the operating state parameter changes from S1 to S2, and the change probability is calculated to be 40.91%. Combined with the time series analysis, this change of the equipment operating state parameters conforms to the continuous time evolution trend, and there is no obvious stagnation of the operating state parameters. The observation values ​​statistically analyzed from the operating state parameter feature value set show that the temperature increases from the interval at t1 (50℃-60℃) to the interval at t2 (60℃-70℃), indicating that the equipment operating state parameters continue to evolve to a higher temperature range. From t2 to t3, the operating state parameter changes from S2 to S3, and the change probability is assumed to be 60%. Combined with the time series analysis, the probability of change is higher than the probability from t1 to t2, indicating that the speed of change of the operating state parameters has increased. The observed value statistics show that the temperature is further increased from the interval of t2 (60℃-70℃) to the interval of t3 (70℃-80℃), and the operating state parameters of the equipment have not fluctuated repeatedly during this period. Based on the above analysis, the change law of the operating state parameters is manifested in: the direction of change of the operating state parameters is a continuous transition to a higher temperature interval, without stagnation or decline; the changes in different time periods have a certain continuity, the probability of change from t1 to t2 is 40.91%, and the probability of change from t2 to t3 is 60%, indicating that the evolution speed of the operating state parameters is gradually increasing, and vice versa; in the entire time series, there is no obvious repetition in the change of the operating state parameters, that is, in each stage of the time series, the operating state parameters are constantly evolving to a new interval, indicating that the equipment operation trend is relatively stable. In addition, if there is obvious repetition, it means that the equipment operating state parameters may be affected by external disturbances or internal unstable factors.

[0076] See also Figure 4 , the specific steps for obtaining the node distribution change point information are:

[0077] Based on the dynamic trend evolution results, the change amount of the operating state parameter in the time series corresponding to the operating state parameter is calculated point by point, and the time point when the change amount exceeds the preset change threshold is marked, wherein the marked time point represents the moment when a key change occurs in the time series corresponding to the operating state parameter, and a marked time point set is generated;

[0078] Mark the time points in the dynamic trend evolution results where the operating state parameter changes exceed the preset change threshold. First, calculate the change amount of the operating state parameter point by point based on the dynamic trend evolution results. For example, in the temperature parameter time series: assuming that the temperature at time point t1 is 60°C and the temperature at time point t2 is 75°C, calculate the change amount: 75-60=15, the preset temperature change threshold range is ±10°C, and the change amount of 15°C here exceeds the threshold, so mark t2 as an important change time point. Similarly, calculate the change amount of each time point in the time series and compare it with the preset threshold. For example: from t2 to t3, the temperature changes from 75°C to 85°C, the change amount is 10°C, which does not exceed the threshold; from t3 to t4, the temperature changes from 85°C to 100°C, the change amount is 15°C, which exceeds the threshold, and mark t4. Through the above screening, mark all time points that exceed the threshold, and the marking results include time points and their corresponding changes. For example, marking results: {time point t2, change amount 15}, {time point t4, change amount 15}. These marked time points indicate the moments when important changes have occurred in the time series corresponding to the operating status parameters and require further analysis.

[0079] Classify and count the change types and distribution density of the operating status parameters in the marked time point set, analyze the distribution law of each marked time point in combination with the time interval of the time point, and generate node distribution change point information;

[0080] Generate node distribution change point information based on the set of marked time points. First, classify and count the marked time points, for example, by change parameters (such as temperature) and change types (such as temperature rise): time point t2, change type is temperature rise, change amount is 15; time point t4, change type is temperature rise, change amount is 15. Next, analyze the distribution density of the marked time points in the time series. The distribution density judgment needs to be calculated in combination with the specific time unit. Assume that the time unit is "minutes", that is, the time points in the time series record the operating status parameters at intervals of every minute, for example, t1 is 1 minute, t2 is 2 minutes, and so on. Assume that the basis for judging the density is: if the time interval between the marked time points does not exceed 5 minutes, and there are 3 or more marked time points in this time period, it is judged as "high dense distribution"; for example, if the marked points are t2, t3, and t4, their time intervals are t3-t2 = 2 minutes, t4-t3 = 3 minutes, the total time period is within 5 minutes, and it contains 3 marked points, it is judged as "high dense distribution"; if the marked points are t2, t5, and t7, the time intervals are t5-t2 = 10 minutes, t7-t5 = 15 minutes, and the time intervals are all more than 5 minutes, it is judged as "low dense distribution". Finally, the marked time points are combined with the density information to generate the node distribution change point information. For example: {time point t2, parameter change type "heating", change amount 15, distribution density "high"}; {time point t4, parameter change type "heating", change amount 15, distribution density "low"}. Similarly, the marking of pressure parameters and flow parameters will also be divided based on their respective operating ranges. For example, pressure parameters can be divided into low pressure, medium pressure, and high pressure, and flow parameters can be divided into low flow, medium flow, and high flow, so that their change points can be classified, recorded, and marked.

[0081] See also Figure 5 , the steps for determining the upper and lower limits of the fluctuation range of the target operating state parameters are as follows:

[0082] The time series corresponding to the running status parameters adjacent to the marked time points in the node distribution change point information are analyzed using the formula:

[0083]

[0084] Calculate the speed v at which the target operating state parameter deviates from the current value x to the corresponding mean value μ and the fluctuation range σ of the target operating state parameter respectively;

[0085] Among them, k' represents the intensity of the target operating state parameter returning to the mean after deviating from the corresponding mean. It is a proportional coefficient used to control the proportion of mean recovery in the drift rate. The value range of k' is usually obtained by fitting historical data. For example, under a device operating state parameter, the relationship between the deviation value (x-μ) of the parameter and its regression speed v is recorded. The actual value of k' can be determined by fitting the curve using the least squares method. In order to record the relationship between the deviation value of the operating state parameter and its regression speed, the real-time data of the operating state parameter at different times can be collected. First, the deviation value of the operating state parameter from the target mean at each time point is calculated, such as the difference between the recorded temperature value and the target mean. At the same time, the regression speed is calculated between adjacent time points, that is, the rate of change per unit time is obtained by differencing the time series data. These data points are organized into a set of data pairs, each pair of data includes a deviation value and a corresponding regression speed. The curve is fitted using the least squares method to obtain the best fitting straight line for these data points. The slope of the fitting curve is the actual value of the mean recovery intensity k'. For example, if within a period of time The temperature deviation from the target mean is recorded to gradually decrease from 5°C to 1°C, and the corresponding regression speeds are recorded as 1.25°C / minute and 0.25°C / minute, respectively. By fitting these data points, the linear relationship between the deviation value and the regression speed can be intuitively determined, thereby calculating k'. In addition, k' can be adjusted in combination with the sensitivity of the equipment operation. For example, the value of k' in the temperature change sensitive equipment will be set larger to quickly regress to the mean, while the k' value for the stable operation equipment can be smaller. For sensitive equipment (such as environments with high temperature control accuracy), k' is usually in the range of 0.3 to 0.5. For non-sensitive equipment (such as environmental parameters with low-frequency fluctuations), k' can be in the range of 0.1 to 0.3. x is the current operating status parameter value, which is collected and recorded in real time by the sensor. For example, the temperature sensor can directly record the operating temperature of the current equipment in °C. μ represents the mean value corresponding to the target operating status parameter, which can be obtained by statistical calculation of a large amount of historical data. For example, in industrial equipment, the target operating temperature range is 60°C-80°C. The mean value can be obtained as μ=70°C through long-term operation data. It is the instantaneous rate of change of adjacent operating state parameters, indicating the speed of change of operating state parameters per unit time, and is calculated by the following formula: x(t1) and x(t2) are the operating state parameter values ​​at two adjacent time points, and t2-t1 is the time interval between the two time points. The instantaneous rate of change can be obtained by directly calculating the parameter increment and the time increment. The unit is the unit of the operating state parameter per unit time (such as ℃ / minute). For example, the temperature at the time point t1=1 minute is 60℃, and the temperature at the time point t2=2 minutes is 75℃, then: β is the instantaneous change rate weight coefficient set according to the instantaneous change rate, which indicates the influence of the instantaneous change on the drift rate. The size of β directly determines the proportion of the instantaneous change rate in the drift rate. The value of β is usually obtained by analyzing the instantaneous change rate (such as ) to the drift speed. For example, by observing the improvement effect of the instantaneous change rate on the overall regression speed when it is high, a reasonable weight value can be determined. In addition, β can be adjusted in combination with the actual equipment operation characteristics. For example, for fast-response equipment, the effect of the instantaneous change rate is more significant, and the value of β will be set higher. For equipment with smooth fluctuations, the value of β is set lower accordingly. For example, the operating temperature (operating status parameter) of industrial welding equipment will fluctuate rapidly with environmental changes, and the instantaneous change rate has a greater impact on the drift rate. In this case, by analyzing the historical time series, it is found that when the instantaneous change rate When the drift rate exceeds 10℃ / minute, the fluctuation range of the drift rate increases significantly. According to this situation, the value of β can be set to 0.5-0.7 to ensure that the instantaneous change occupies a higher weight. In the ambient temperature control equipment, the operating temperature changes slowly, and the instantaneous change has little effect on the drift rate. The historical data shows Most of the time, the temperature is kept below 1℃ / minute. The drift rate mainly depends on the mean reversion process. Therefore, for such equipment, the value of β can be set to 0.2-0.4. D represents the basic value of the fluctuation range of the target operating state parameter, which is obtained by statistically analyzing the variance of the time series corresponding to the operating state parameter. For example, within a period of time, the temperature fluctuation of the equipment is within the range of 60℃-80℃. The calculation process is: x i' is the current operating status parameter value at the i'th time point, is the mean value of the current operating state parameter value x, n' is the number of time points in the time series corresponding to the operating state parameter, Δx is the fluctuation increment, which represents the difference between the maximum value max(x) and the minimum value min(x) in the time series corresponding to the operating state parameter, and is calculated by the following formula: Δx = max(x) - min(x), γ is the fluctuation range adjustment coefficient set according to the fluctuation increment, which represents the contribution ratio of the fluctuation increment Δx to the fluctuation amplitude, and the value of γ is determined by fitting the contribution relationship of the fluctuation increment to the fluctuation amplitude. For example, by analyzing the proportion of the maximum fluctuation increment to the fluctuation amplitude in the historical data, a reasonable γ value can be determined. In addition, for parameters with larger fluctuations, the value of γ can be set higher, while for parameters with smaller fluctuations, the value of γ will be lower. For example, in chemical equipment In the data, the operating pressure (operating state parameter) fluctuates in a large range, which may rise from 100 kPa to 200 kPa within 10 minutes. This drastic fluctuation requires the fluctuation increment Δx to occupy a higher weight in the fluctuation amplitude calculation. Therefore, by analyzing the historical data, it is found that when Δx>50 kPa, the change trend of the fluctuation amplitude shows a strong linear relationship with the increment, so γ can be set to 0.4~0.5. In the temperature control equipment, the fluctuation range of the operating temperature (operating state parameter) is small, which may vary between 60℃ and 70℃. The fluctuation increment Δx has little effect on the fluctuation amplitude, and the fluctuation amplitude mainly depends on the random fluctuation intensity D. Therefore, by analyzing the historical data, it is found that the change trend of the fluctuation amplitude is weakly correlated with Δx, and γ can be set to 0.2~0.3.

[0086] Assume that the temperature time series is as follows: time point t1 = 0 minutes, temperature x(t1) = 60°C; time point t2 = 1 minute, temperature x(t2) = 75°C; time point t3 = 2 minutes, temperature x(t3) = 85°C. Assume that the historical operation target mean μ = 70°C; mean recovery strength k' = 0.25; instantaneous change rate weight coefficient β = 0.5; fluctuation range adjustment coefficient γ = 0.3;

[0087] Calculate the basic value of fluctuation range D:

[0088] The basic value of the fluctuation range is D≈116.67℃.

[0089] Calculate the fluctuation increment Δx: from time point t1 to t2: Δx = x(t2) - x(t1) = 75 - 60 = 15°C;

[0090] Time point t2 to t3: Δx = x(t3) - x(t2) = 85 - 75 = 10°C;

[0091] Calculate the instantaneous rate of change

[0092] From time point t1 to t2:

[0093] Time point t2 to t3:

[0094] Calculate the speed v at which the temperature operating state parameter deviates from the current value x to the corresponding mean value μ and then regresses:

[0095] Time point t1 to t2: current temperature x = 75°C; target mean μ = 70°C; instantaneous rate of change Substituting into the formula: v = -0.25·(75-70)+0.5·15 = 6.25°C / min;

[0096] Time point t2 to t3: current temperature x = 85°C; target mean μ = 70°C; instantaneous rate of change Substituting into the formula: v = -0.25·(85-70)+0.5·10 = 1.25°C / min;

[0097] Calculate the fluctuation range σ:

[0098] Time point t1 to t2: D = 116.67°C, k' = 0.25, γ = 0.3, Δx = 15°C.

[0099] Substituting into the formula:

[0100] Time point t2 to t3: D = 116.67°C, k' = 0.25, γ = 0.3, Δx = 10°C.

[0101] Substituting into the formula:

[0102] The results show that from time point t1 to t2: the fluctuation increment Δx = 15°C; the instantaneous change rate The speed at which the temperature operating state parameter deviates from the current value x to the corresponding mean value μ and then regresses is v = 6.25°C / minute; the fluctuation range of the temperature operating state parameter σ≈15.42°C. Time point t2 to t3: Fluctuation increment Δx = 10°C; instantaneous rate of change The speed at which the temperature operating state parameter deviates from the current value x to the corresponding mean value μ and then regresses is v=1.25°C / minute; the fluctuation range of the temperature operating state parameter σ≈15.37°C.

[0103] The drift rate and fluctuation amplitude of pressure and flow can also be evaluated in a similar way to temperature. Among them, the drift rate indicates the speed at which the pressure or flow returns to the target mean after deviating from its target mean, while the fluctuation amplitude indicates the fluctuation range of pressure or flow in a specific time interval. In practical applications, the time series data of pressure or flow can be used as input, and the pressure or flow values ​​at different time points are extracted to calculate their instantaneous rate of change and the deviation from the target mean. Similar to the calculation process of temperature, the drift rate can be calculated by analyzing the deviation and instantaneous rate of change of pressure or flow from the target mean, and the fluctuation amplitude can reflect its range by the variance or incremental change of historical pressure or flow data. Similarly, the fluctuation amplitude of pressure can be evaluated based on the incremental range of pressure change and the variance of historical pressure. For flow, the time series may record the flow per unit time. By calculating the incremental change of flow in the time interval and combining it with the deviation from the target flow mean, the drift rate can be calculated. The fluctuation amplitude of flow can be characterized by its dynamic range through the variance or local incremental change of historical flow data. Similar to temperature calculation, through time series-based pressure and flow data, combined with the instantaneous rate of change and the deviation from the target mean, the drift rate and fluctuation amplitude can be calculated respectively, providing a basis for the dynamic change analysis of pressure and flow and the evaluation of operating status parameters.

[0104] According to the speed v of the target operating state parameter deviating from the current value x to the corresponding mean μ and the fluctuation range σ of the target operating state parameter, the formula is adopted:

[0105] [x min ,x max ]=[μ-(σ+v·Δt),μ+(σ+v·Δt)]

[0106] Determine the upper limit x of the fluctuation range of the target operating state parameter max and the lower limit x min ;

[0107] Wherein, Δt represents the difference between adjacent time points in the time series corresponding to the operating status parameter. Assuming that time points t1 = 0 minutes and t2 = 1 minute, then: Δt = t2-t1 = 1 minute;

[0108] The parameters were set as μ=70°C; σ=15.42°C and σ=15.38°C; v=6.25°C / min and v=1.25°C / min; Δt=1 min.

[0109] Substitute into the formula: Time point t1 to t2: x min =μ-(σ+v·Δt)=70-(15.42+6.25)=48.33℃; x max=μ+(σ+v·Δt)=70+(15.42+6.25)=91.67℃;

[0110] The results show that the temperature fluctuation range from time point t1 to t2 is [48.33℃, 91.67℃].

[0111] Time point t2 to t3: x min =μ-(σ+v·Δt)=70-(15.38+1.25)=53.37℃; x max =μ+(σ+v·Δt)=70+(15.38+1.25)=86.63℃;

[0112] The results show that the temperature fluctuation range from time point t2 to t3 is [53.37℃, 86.63℃].

[0113] The upper and lower limits of the fluctuation range of pressure and flow can be determined in the same way as temperature. The core method is to dynamically calculate based on the drift rate and fluctuation amplitude. For the operating state parameters of pressure or flow, first obtain the target mean through historical operating data, and use the drift rate formula to calculate its deviation regression speed in different time periods, and combine the fluctuation amplitude formula to calculate the fluctuation amplitude of the dynamic range. , The target mean of pressure or flow is corrected to the dynamic upper and lower limit interval, and the upper and lower limits of the interval are determined according to the adjustment results of the fluctuation amplitude and drift rate to the mean. For example, for pressure, the drift rate and fluctuation amplitude of pressure can be calculated according to the pressure changes at adjacent time points in the operating period, and these dynamic parameters can be combined with the historical pressure mean to obtain the dynamic fluctuation interval of pressure. Similarly, flow, through the time series data of flow, calculate the drift rate and fluctuation amplitude of flow, combine these data with the target flow mean, and determine the upper and lower limits of dynamic flow. Therefore, whether it is pressure or flow, the calculation logic of the fluctuation range can be completely applied to the temperature processing method, and only the parameters need to be adjusted according to the specific pressure or flow data.

[0114] See also Figure 6 ,The specific steps for obtaining node abnormal fluctuation information are:

[0115] Compare the upper and lower limits of the fluctuation range of the target operating status parameter with the operating status parameter adjacent to the marked time point, mark the abnormal change points exceeding the upper and lower limits of the fluctuation range according to the comparison results, and generate the abnormal change point marking results;

[0116] Assume that the temperature time series adjacent to a certain marked time point is {65℃, 72℃, 90℃, 55℃, 80℃}, and the upper and lower limits of the fluctuation range are [60℃, 85℃]. For the first temperature value 65℃: check whether it falls within the range of [60℃, 85℃], and if it is within the range, mark it as "normal". For the second temperature value 72℃: check whether it falls within the range of [60℃, 85℃], and if it is within the range, mark it as "normal". For the third temperature value 90℃: check whether it falls within the range of [60℃, 85℃], and if it is higher than the upper limit of 85℃, mark it as "higher than the upper limit", and record the excess value as 90-85=5℃. For the fourth temperature value 55℃: check whether it falls within the range of [60℃, 85℃], and if it is lower than the lower limit of 60℃, mark it as "lower than the lower limit", and record the excess value as 60-55=5℃. For the fifth temperature value 80℃: check whether it falls within the range of [60℃,85℃[, and if it is found to be within the range, mark it as "normal". The marking results are: time point 1: 65℃, normal; time point 2: 72℃, normal; time point 3: 90℃, higher than the upper limit, exceeding 5℃; time point 4: 55℃, lower than the lower limit, exceeding 5℃; time point 5: 80℃, normal. At the same time, the analysis method for the operating status parameters of pressure and flow is similar, providing data support for the subsequent generation of abnormal node fluctuation information.

[0117] Based on the abnormal change point marking results, the distribution characteristics of all abnormal change points are classified and sorted to generate node abnormal fluctuation information;

[0118] Integrate the marking results, extract the operating status parameters and time point information marked as abnormal change points, organize the abnormal points below the lower limit of the fluctuation range and above the upper limit of the fluctuation range into different categories, analyze the distribution pattern of abnormal points in the time series, and record the excess amplitude of each abnormal change point. At the same time, combine the information of the time point to generate complete node abnormal fluctuation information. The node abnormal fluctuation information includes the abnormal type, time point, abnormal value of the operating status parameter and the corresponding deviation, which is used for subsequent analysis of the change trend of the node operating status parameters and the law of abnormal occurrence.

[0119] See also Figure 7 , the specific steps for obtaining the cross-stage parameter optimization analysis results are:

[0120] Compare the upper and lower limits of the fluctuation range of the operating state parameters corresponding to the abnormal change points in the node abnormal fluctuation information with the optimal operating range of the preset operating state parameters, and obtain the abnormal operating state parameter optimization interval from the comparison result;

[0121] The fluctuation range upper and lower limits of the operating state parameters corresponding to the abnormal change points in the node abnormal fluctuation information are compared with the optimal operating range of the preset operating state parameters. The operating state parameters of the abnormal change points and their fluctuation range upper and lower limits are extracted and compared with the preset optimal operating range pL, U], where L represents the lower limit of the optimal operating range and U represents the upper limit of the optimal operating range. Specifically, L and U are set based on historical operating data, production target requirements, and equipment design parameter ranges of operating state parameters. Assuming that the historical operating data range of temperature in industrial production is p55℃, 90℃ [, after analysis, it is found that the optimal performance range of the equipment is p60℃, 85℃], so L is set to 60℃ and U is set to 85℃. This setting is obtained through statistical analysis of multiple historical operating data. For example, based on the long-term operating performance and production quality data of the equipment, the frequency distribution is fitted in combination with the kernel density estimation method, and the corresponding high probability distribution interval is extracted. The upper and lower limits of the interval are further reasonably adjusted in combination with production requirements, thereby setting the optimal operating range. By comparing the fluctuation range of the abnormal change point point by point to see if it is completely within the optimal range, if the upper limit of the fluctuation range exceeds U, it is marked as "high" and the exceeded value is recorded. If the lower limit of the fluctuation range is lower than L, it is marked as "low" and the lower value is recorded. Assuming that the abnormal fluctuation information records the upper and lower limits of the temperature parameter fluctuation range of a node as [58℃, 88℃], and the preset optimal operating range is [60℃, 85℃], then by comparison, it is concluded that the lower limit of the fluctuation range of the temperature parameter is 60℃ lower than the lower limit of the optimal range, and the lower value is 60-58=2℃, while the upper limit of the fluctuation range is 85℃ higher than the upper limit of the optimal range, and the higher value is 88-85=3℃. The optimization interval of the abnormal operation status parameter is obtained as the interval below 2℃ and above 3℃, and the optimization goal is to adjust the fluctuation range of the temperature parameter to the optimal operating range of [60℃, 85℃].

[0122] By referring to the changing trends of the time series corresponding to different operating status parameters, the correlation strength between the optimization interval of abnormal operating status parameters and the entire product production node is analyzed to generate cross-stage parameter optimization analysis results;

[0123] Analyze the strength of the correlation between the optimization interval of abnormal operating state parameters and the entire product production node. By extracting the deviation range of the optimization interval of abnormal operating state parameters and combining it with the time series data of other relevant parameters in the product production node for analysis, focus on evaluating the correlation between different operating state parameters. The specific method includes extracting the optimization interval of the operating state parameters (such as temperature, pressure, and flow) in the time period corresponding to the abnormal change point and comparing it with the variation range of the relevant parameters of the adjacent nodes, and judging the correlation based on the fluctuation trend of the time series of the two. For example, the abnormal optimization interval of the temperature parameter of a certain node is determined to be [-2℃, +3℃], indicating that the temperature fluctuation range is between below 2℃ and above 3℃, which exceeds the optimal range. Combined with the operating range of the node pressure parameter [2.3MPa, 2.7MPa] and the flow parameter [100L / s, 130L / s], it is found that the change trend of the temperature parameter in the abnormal interval has a synchronous fluctuation relationship with the high trend of the pressure parameter (for example, the pressure increases from 2.3MPa to 2.8MPa), and is anti-correlated with the low trend of the flow parameter (for example, the flow decreases from 120L / s to 100L / s) in the time series. Further comparison of the operating status parameters of the adjacent nodes shows that the abnormal deviation range of the temperature parameter of the node has a similar trend with the pressure adjustment parameters of the adjacent nodes within the synchronous time of the pressure change, so it is judged that the temperature and pressure are correlated in this interval. According to the above analysis results, the relationship between temperature, pressure and flow is comprehensively judged: the fluctuation trend of temperature and pressure at multiple time points is synchronized, and they show a stable correlation in the marked abnormal change point time period, so it can be considered that temperature optimization will have a direct impact on pressure; while the temperature and flow show an opposite change trend, indicating that flow adjustment may be affected by other factors, or only indirectly related to temperature. Generate cross-stage parameter optimization analysis results.

[0124] See also Figure 8 , the specific steps for obtaining the node parameter optimization decision report are:

[0125] According to the correlation strength of all operating status parameters in the cross-stage parameter optimization analysis results, the importance of different operating status parameters is determined, and the priority of the operating status parameter adjustment sequence is allocated according to the importance, and the operating status parameter priority adjustment result is generated;

[0126] According to the correlation between temperature, pressure and flow that has been clarified in the analysis, combined with the specific optimization requirements of cross-stage parameters, the ranking basis of synchronization and importance in the correlation strength is extracted, and the target parameters with a high synchronization relationship with other parameters in multiple abnormal change point time periods are prioritized. Through comprehensive analysis of the results of strong synchronous fluctuations between temperature and pressure and long time spans, it is determined that the priority of temperature adjustment is higher than that of pressure, while the time series synchronization with the reverse change trend of flow is less, indicating that flow has the lowest priority. Finally, the adjustment range and adjustment order of temperature are determined according to the priority.

[0127] The operation status parameter priority adjustment results are displayed in a visual way to generate a node parameter optimization decision report;

[0128] Integrate clear target operating status parameter priority information into visual content, including generating priority sorting tables and trend analysis charts. The priority sorting tables are arranged in order of priority of temperature, pressure, and flow, combined with the specific fluctuation range and adjustment range of each parameter in the time series. At the same time, the adjustment range in the cross-stage parameter optimization analysis results is marked in the trend analysis chart, and the fluctuation range of each parameter is distinguished by different colors or line styles, and historical data and optimization recommendation values ​​are displayed in line or bar charts. Furthermore, the parameter adjustment sequence and the corresponding adjustment period are summarized as text descriptions, and a complete optimization decision report is formed in conjunction with the trend analysis chart. The report is presented in a clear combination of text and pictures, providing a reference for real-time optimization and adjustment of production nodes.

[0129] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An intelligent optimization and real-time decision-making platform for industrial big data, characterized by: The platform includes: The node data time distribution analysis module obtains the operating status parameters of product production nodes from industrial big data and converts them into time series, extracts the characteristic values ​​of the operating status parameters in each period of the time series, counts the frequency of occurrence of the characteristic values ​​in the corresponding period, and generates node time period distribution information; The node distribution dynamic evolution module performs dynamic trend evolution on the time series corresponding to the operating state parameters based on the node time period distribution information, generates dynamic trend evolution results, marks the time points when the operating state parameters in the dynamic trend evolution results change beyond a preset change threshold, and generates node distribution change point information; The node fluctuation range assessment module analyzes the time series corresponding to the operating state parameters adjacent to the marked time points in the node distribution change point information, determines the upper and lower limits of the fluctuation range of the target operating state parameters according to the analysis results, marks the abnormal change points according to the upper and lower limits of the fluctuation range, and generates node abnormal fluctuation information; The node multi-stage association optimization module obtains the abnormal operation state parameter optimization interval from the upper and lower limits of the fluctuation range of the target operation state parameter corresponding to the abnormal change point in the abnormal fluctuation information of the node and the optimal operation range of the preset operation state parameter, analyzes the association strength between the abnormal operation state parameter optimization interval and the entire product production node, and generates a cross-stage parameter optimization analysis result; The node parameter suggestion generation module determines the adjustment sequence of different operating status parameters according to the correlation strength of all operating status parameters in the cross-stage parameter optimization analysis results, and generates a node parameter optimization decision report according to the adjustment sequence.

2. The intelligent optimization and real-time decision-making platform for industrial big data according to claim 1 is characterized in that: The steps for obtaining the node time period distribution information are specifically as follows: Obtain the operating status parameters of product production nodes from industrial big data and convert them into time series. Divide the time series into different time periods, obtain the characteristic values ​​of the operating status parameters in each time period, including the maximum value, minimum value and quantile value, and generate a set of operating status parameter characteristic values. The occurrence frequency of each characteristic value in the operating status parameter characteristic value set in the corresponding time period is counted, and a histogram is constructed according to the occurrence frequency to display the frequency distribution of the operating status parameters. The frequency distribution is smoothly fitted using the kernel density estimation method to generate the node time period distribution information.

3. The intelligent optimization and real-time decision-making platform for industrial big data according to claim 2 is characterized in that: The steps for obtaining the dynamic trend evolution result are specifically as follows: Based on the node time period distribution information, the formula is adopted: In the time series corresponding to the calculation of the operating status parameter, the operating status parameter at the previous moment is S t-1 When the current operating state parameter is S t The conditional probability P ( S t |S t-1) , generate the state change probability of the operating state parameters; Among them, A ( S t-1 ,S t) The running state parameter S from the previous moment is obtained by counting the number of changes of the running state parameter in the time series corresponding to the running state parameter. t-1 Transfer to the current operating state parameter S t The probability of n is the total number of all operating state parameters, B ( S t ,O t) It is obtained based on the frequency of occurrence of each characteristic value in the operating state parameter characteristic value set in the corresponding time period. t The specified operating state parameter O is observed t The observation probability of k represents the index of all operating state parameters; According to the state change probability of the operating state parameters, the change rules of the operating state parameters in different periods are analyzed to generate dynamic trend evolution results.

4. The intelligent optimization and real-time decision-making platform for industrial big data according to claim 1 is characterized in that: The steps for obtaining the node distribution change point information are specifically as follows: Based on the dynamic trend evolution result, by calculating the change amount of the operating state parameter in the time series corresponding to the operating state parameter point by point, marking the time point when the change amount exceeds the preset change threshold, wherein the marked time point represents the moment when a key change occurs in the time series corresponding to the operating state parameter, and generating a marked time point set; The change types and distribution density corresponding to the operating status parameters in the marked time point set are classified and counted, and the distribution law of each marked time point is analyzed in combination with the time interval of the time point to generate node distribution change point information.

5. The intelligent optimization and real-time decision-making platform for industrial big data according to claim 1 is characterized in that: The steps of determining the upper and lower limits of the fluctuation range of the target operating state parameter are specifically as follows: The time series corresponding to the running state parameters adjacent to the marked time points in the node distribution change point information are analyzed using the formula: Calculate the speed v at which the target operating state parameter deviates from the current value x to the corresponding mean value μ and the fluctuation range σ of the target operating state parameter respectively; Among them, k' represents the intensity of the target operating state parameter returning to the mean after deviating from the corresponding mean, is the proportional coefficient, x is the current value of the target operating state parameter, μ represents the mean corresponding to the target operating state parameter, is the instantaneous change rate of the adjacent operating state parameters, β is the instantaneous change rate weight coefficient set according to the instantaneous change rate, D represents the basic value of the fluctuation range of the target operating state parameter, Δx is the fluctuation increment, which represents the difference between the maximum and minimum values ​​in the time series corresponding to the operating state parameter, and γ is the fluctuation range adjustment coefficient set according to the fluctuation increment; According to the speed v of the target operating state parameter deviating from the current value x to the corresponding mean μ and the fluctuation range σ of the target operating state parameter, the formula is adopted: [x] min ,x max ]=[μ-(σ+v·Δt),μ+(σ+v·Δt)] Determine the upper limit x of the fluctuation range of the target operating state parameter max and the lower limit x min ; Among them, Δt represents the difference between adjacent time points in the time series corresponding to the operating status parameter.

6. The intelligent optimization and real-time decision-making platform for industrial big data according to claim 5 is characterized in that: The specific steps for obtaining the abnormal node fluctuation information are as follows: Compare the upper and lower limits of the fluctuation range of the target operating state parameter with the operating state parameter adjacent to the marked time point, mark the abnormal change points exceeding the upper and lower limits of the fluctuation range according to the comparison result, and generate the abnormal change point marking result; Based on the abnormal change point marking results, the distribution characteristics of all abnormal change points are classified and sorted to generate node abnormal fluctuation information.

7. The intelligent optimization and real-time decision-making platform for industrial big data according to claim 1 is characterized in that: The steps for obtaining the node parameter optimization decision report are specifically as follows: Determine the importance of different operating status parameters according to the correlation strength of all operating status parameters in the cross-stage parameter optimization analysis result, assign the operating status parameter adjustment order priority according to the importance, and generate the operating status parameter priority adjustment result; The operation status parameter priority adjustment result is displayed in a visual manner to generate a node parameter optimization decision report.

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