A multi-control parameter synchronous optimization system and method for intelligent meter regulation
Through IoT monitoring networks and curve fitting analysis, the intelligent instrument control system achieves synchronous optimization of multiple parameters, solves the problem of disordered adjustment in traditional control methods, and improves the coordination and adaptability of multi-instrument clusters.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHANGSHA PEACOCK AUTOMATION ENG CO LTD
- Filing Date
- 2026-05-12
- Publication Date
- 2026-06-12
AI Technical Summary
Traditional intelligent instrument control methods cannot adapt to the joint control of multiple parameters in multi-instrument cluster scenarios, and cannot determine the orderly adjustment requirements between dynamic instrument parameters, resulting in disordered and unbalanced parameter adjustment.
By building an IoT monitoring network, the system collects and fits curves of the periodic operation data of smart meters, analyzes the amplitude fluctuation difference rate and the synchronous fluctuation correlation index, generates smart meter adjustment commands, and achieves synchronous optimization of multiple control parameters.
It enables the orderly adjustment of parameters in multi-intelligent instrument cluster scenarios, improves coordination and adaptability, adapts to the multi-parameter adjustment needs under complex working conditions, and avoids parameter imbalance and abnormal working conditions.
Smart Images
Figure CN122194931A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of instrument parameter control technology, specifically a multi-control parameter synchronous optimization system and method for intelligent instrument control. Background Technology
[0002] In the current environment of continuous advancement in industrial automation and intelligent manufacturing, smart instruments have become a key infrastructure for achieving state perception, precise adjustment and stable operation in process industries, energy management and equipment operation, undertaking the core functions of multi-physical quantity acquisition, data processing and closed-loop control; Current industrial processes are generally characterized by strong coupling of multiple parameters, nonlinearity, and time-varying operating conditions, which increasingly demands the coordination and adaptability of control parameters. However, traditional instrument control often adopts single-loop, fixed-parameter setpoint or timed independent adjustment. Therefore, when facing the operation of multiple intelligent instrument clusters, each intelligent instrument adjusts its parameters independently and without order, making it difficult to adapt to the multi-parameter joint adjustment requirements in multi-instrument cluster scenarios. It is also impossible to determine the dynamic fluctuation relationship between multiple parameters of different intelligent instruments, thus making it impossible to determine the orderly adjustment requirements between dynamic instrument parameters. Summary of the Invention
[0003] The purpose of this invention is to provide a multi-control parameter synchronous optimization system and method for intelligent instrument control, so as to solve the problems raised in the prior art.
[0004] To achieve the above objectives, the present invention provides the following technical solution: A method for synchronous optimization of multiple control parameters for intelligent instrument control, comprising the following steps: Establish an IoT monitoring network to collect periodic operation time-series data of various smart meters in the target scenario; Curve fitting is performed on the periodic operation time series data of each smart meter to construct the periodic operation time series curve, and the amplitude fluctuation difference rate of adjacent time series on each periodic operation time series curve is analyzed. Based on the amplitude fluctuation difference rate of the operation time series curves of each cycle, a fluctuation correlation assessment analysis is performed on the operation time series curves of different cycles to obtain the synchronous fluctuation correlation index of the operation time series curves of different cycles, and a fluctuation correlation set of the operation time series curves of the smart instrument is constructed. The fluctuation intensity is analyzed based on the periodic operation time-series curves of each smart instrument in the fluctuation correlation set. Combined with the synchronous fluctuation correlation index, the coordinated control degree of each smart instrument in the fluctuation correlation set is analyzed, and smart instrument control instructions are generated.
[0005] Furthermore, each smart meter in the target scenario is located by tagging, and an IoT monitoring network is built by calling the local IoT to monitor each smart meter in relation to the other. The real-time operation data of each smart meter is retrieved through the IoT monitoring network, and a periodic window is constructed to slice the real-time operation data of each smart meter to obtain periodic operation time sequence data. It should be noted that the target scenario is pre-selected and defined by the operators; The IoT monitoring network connects the data ports of each smart meter in the target scenario to synchronously retrieve data from the smart meters and feed it back to the management port. The slicing process involves shifting and slicing the data on the time axis corresponding to the real-time operation data of each smart meter to obtain operation time data for different periods. The periodic operation time sequence data includes the operation data of the corresponding smart meter, the collection timestamp, and the smart meter's tag number.
[0006] Furthermore, the curve simulation model is called to perform curve fitting on the periodic operation time series data of each smart meter, and the periodic operation time series curve of the corresponding smart meter is constructed. The time-series curves for each cycle are extracted, and fluctuation analysis is performed on the curve amplitudes of adjacent time points on each curve. The curve values at each time point are traversed according to the time-series axis, and the amplitude difference ΔY between adjacent time points is calculated. i (t, t+1); where i is the smart meter tag number; t and t+1 are adjacent time points t and t+1 within the period; It should be noted that the difference in curve amplitude is used to measure the range of change in curve amplitude between adjacent time points, therefore the result of the calculation of the difference in curve amplitude between adjacent time points is a positive number; The coordinates of the curve points corresponding to each time point are extracted from the time-series curve of the periodic operation, and the amplitude variation vector of the curve points is constructed by associating the origin of the coordinates; the angle between the amplitude variation vectors of curve points of adjacent time series is determined, and the amplitude fluctuation difference rate AF of adjacent time series curve points on the periodic operation time-series curve is calculated. i The analysis is performed on (t, t+1); the calculation is as follows: ; Among them, AF i (t, t+1) represents the amplitude fluctuation difference rate between adjacent time points t and t+1 on the periodic operation time sequence curve of the smart instrument with tag i; θ(R t ,R t+1 R is the angle between the amplitude vectors of adjacent curve points on the periodic operation time sequence curve of the smart instrument with tag i; t and R t+1 These are the amplitude variation vectors of adjacent curve points on the periodic operation time sequence curve of the smart meter labeled i; It should be noted that the curve amplitude variation vector is constructed by taking the origin of the coordinate system as the starting point of the vector and extending it to the curve points at each time point; it is used to measure the changing trend of the curve amplitude from the starting point of the period to the time point corresponding to its curve point. Secondly, when calculating the amplitude fluctuation difference rate between adjacent time curve points on the time series curve of the periodic operation, the similarity analysis of the amplitude change vector of adjacent curve points is called. This is used to analyze the amplitude fluctuation difference between adjacent time curve points to determine the degree of amplitude fluctuation difference between adjacent curve points on the curve in the current cycle. It not only measures the amplitude difference between adjacent curve points, but also takes into account the amplitude fluctuation difference between adjacent curve points, which is more in line with the volatility analysis between data.
[0007] Furthermore, the periodic operation time-series curves of various smart meters within the target scenario are coordinated to determine the corresponding amplitude fluctuation difference rate data, and fluctuation correlation assessment analysis is performed between different periodic operation time-series curves; wherein, the fluctuation correlation assessment analysis includes fluctuation correlation analysis and fluctuation synchronization rate analysis.
[0008] Furthermore, the fluctuation correlation analysis is performed based on the amplitude fluctuation difference rate of the time series curves of operations with different cycles to obtain the fluctuation correlation coefficient SC between the time series curves of operations with any cycle. ij The calculation is as follows: ; Among them, SC ij denoted as , where is the correlation coefficient of the fluctuations in the periodic operation time-series curves of smart meters labeled i and j; m is the number of time points on the periodic operation time-series curve; AF i (t,t+1) and AF j (t, t+1) represent the amplitude fluctuation difference rate of adjacent time points t and t+1 on the periodic operation time sequence curves of smart meters with tags i and j, respectively. and These are the average amplitude fluctuation difference rates of curve points at adjacent times t and t+1 on the periodic operation time sequence curves of smart meters labeled i and j, respectively. Perform a non-repeating, full-traversal fluctuation correlation analysis on the periodic operation time-series curves of each smart instrument in the target scenario, and compile and record the analysis results to construct a first-level fluctuation correlation analysis catalog. It should be noted that the purpose of performing time series fluctuation correlation analysis on any non-repeating cycle operation time series curve is to extract the difference rate of curve amplitude fluctuation by synchronously stepping on the time series, and then to determine the fluctuation correlation between any two non-repeating cycle operation time series curves through fluctuation correlation analysis. The primary synchronous time-series fluctuation correlation analysis directory is used to record the fluctuation correlation coefficients between corresponding non-repeating periodic operation time-series curves. It includes a smart instrument tag directory and a fluctuation correlation coefficient directory. The smart instrument tag directory is used to record the tag data of smart instruments. The fluctuation correlation coefficient directory is used to record the fluctuation correlation coefficients between the corresponding periodic operation time-series curves corresponding to the smart instrument tag locations.
[0009] Furthermore, the fluctuation synchronization rate analysis involves judging the changing trend of adjacent time series curve values on the time series curves of each cycle operation, and taking into account the changing trend of adjacent time series curve values on each curve, and performing fluctuation synchronization rate analysis on the changing trend of adjacent time series curve values on the time series curves of different cycles operation. The analysis of the changing trends of adjacent time series curve values on the time series curves of each cycle operation is as follows: ; Where, dir i (t) represents the trend index of the curve value at time t on the periodic operation time sequence curve of the smart instrument with tag i; Y i (t) and Y i (t+1) represent the curve values at time t and time t+1 on the periodic operation time sequence curve of the smart meter with tag i, respectively; Based on the analysis results of the changing trend index of adjacent time series curve values on the time series curve of each cycle, the changing trend of curve values at each time point on the curve is judged; It should be noted that when analyzing the trend of curve values at each moment on the time-series curve of a periodic operation, the instantaneous fluctuation direction at the corresponding moment is determined by calculating the amplitude changes of curve values at adjacent moments; the trend of change includes rising, unchanged, and falling. Based on the trend of curve value changes at each time point on the time series curves of different cycles, the consistency of the curve value changes at the same time point on the time series curves of different cycles is analyzed. A consistency discriminant function is constructed for analysis, and its calculation is as follows: ; Among them, Q ij (t) is the consistency discrimination function for the curve value change trend at the same moment on the time sequence curve of the periodic operation of smart meters with labels i and j; Based on the consistency of the curve value change trends at the same moment on the time-series curves of different cycles, record the number of curve values with consistent change trends at the same moment on different curves; and record the fluctuation synchronization rate Fs of the time-series curves of different cycles. ij The analysis and calculation are as follows: ; Among them, Fs ijLet w1 and w2 be the fluctuation synchronization rate of the time-series curves of the periodic operation of smart meters labeled i and j; w1 and w2 are the assigned weights; nmax is the number of consecutive curve values with the same trend at the same time on the time-series curves of different periodic operations; N is the number of curve values with the same trend at the same time on the time-series curves of different periodic operations; and m is the number of time points on the time-series curves of periodic operations. The specific values for the weights of w1 and w2 are determined by the operators based on historical data and actual working conditions. It should be noted that when analyzing the fluctuation synchronization rate of time-series curves for different cycles, the changing trend of the curve values at the same moment is judged first, and the number of curve values at the same moment with the same changing trend is considered based on the judgment result. In the subsequent calculation of the fluctuation synchronization rate, the analysis is carried out by combining the results of the consistency discriminant function analysis and the proportion of the number of curve values with the largest continuous simultaneous moment changing trend. It is considered that when the fluctuation synchronization rate analysis is carried out solely based on the analysis results of the consistency discriminant function, different time-series curves for different cycles may have different curve fluctuation distributions under the same consistency discriminant function analysis results. Therefore, by introducing the proportion of the number of curve values with the largest continuous simultaneous moment changing trend, this situation is distinguished, thereby improving the accuracy of the curve fluctuation synchronization rate. Perform a non-repeating full traversal of the fluctuation synchronization rate analysis on the periodic operation time series curves of each smart instrument in the target scenario, and compile the analysis results to construct a secondary synchronization volatility analysis catalog. It should be noted that the secondary synchronization volatility analysis directory is used to record the volatility synchronization rate between corresponding non-repeating periodic operation time series curves. It includes a smart instrument tag directory and a volatility synchronization rate directory. The smart instrument tag directory is used to record the tag data of smart instruments. The volatility synchronization rate directory is used to record the volatility synchronization rate between the corresponding periodic operation time series curves located by the corresponding smart instrument tags.
[0010] Furthermore, the correlation coefficient SC between the fluctuations of the periodic operation time-series curves of different smart meters was analyzed. ij And volatility synchronization rate Fs ij The synchronous fluctuation correlation index Sy for time series curves of different cycle operations ij The analysis and calculation are as follows: ; Among them, Sy ij The synchronous fluctuation correlation index of the periodic operation time-series curves of smart meters labeled i and j; SC ij The correlation coefficient of fluctuation in the time-series curves of the periodic operation of smart meters labeled i and j; Fs ij The fluctuation synchronization rate of the periodic operation time sequence curves of smart meters labeled i and j; Based on the analysis data of the synchronous fluctuation correlation index between the periodic operation time series curves of each smart meter, the fluctuation correlation set of the corresponding smart meter is constructed by correlation aggregation of the periodic operation time series curves. The correlation aggregation involves setting a reference threshold for the synchronous fluctuation correlation index, comparing the synchronous fluctuation correlation index between time-series curves of operations with different cycles, and judging the time-series curves of operations with different cycles that are greater than or equal to the threshold as having strong fluctuation correlation based on the comparison results; conversely, judging the time-series curves of operations with different cycles that are less than the threshold as having weak fluctuation correlation based on the comparison results. Then, by selecting the periodic operation time series curve of any smart meter as the association aggregation object, the periodic operation time series curves of smart meters that have strong fluctuation correlation with it are associated and aggregated. Among them, the reference threshold for the synchronous fluctuation correlation index is a value preset by the operators based on data analysis and experience analysis; It should be noted that when calculating the correlation index of synchronous fluctuations of time-series curves for different cycles, the absolute value of the correlation coefficient is used because the fluctuations between different curves may be in the same direction or opposite directions. The direction of fluctuation contained in the numerical sign of the correlation coefficient is the overall direction of fluctuation of the two curves, which is not precise enough. Therefore, the absolute value is used only for the numerical part that represents the correlation strength of the curves in the calculation, while the specific direction of fluctuation is reflected by the synchronization rate of the fluctuations of the two curves.
[0011] Furthermore, the periodic operation time-series curve of any smart meter in the target scenario is selected as the benchmark object. Its corresponding fluctuation correlation set is retrieved, and the periodic operation time-series curves of each smart meter in the fluctuation correlation set are traversed. The fluctuation intensity BE is then applied to the periodic operation time-series curve of each smart meter. i The analysis and calculation are as follows: ; Among them, BE i The fluctuation intensity of the periodic operation time sequence curve of the i-label smart meter; The mean of the amplitude difference between adjacent time values on the periodic operation time sequence curve of the i-label smart meter; σ[ΔY i [t, t+1)] represents the standard deviation of the difference in amplitude between adjacent curve values on the periodic operation time sequence curve of the smart instrument with label i. Based on the fluctuation intensity of the periodic operation time-series curves of each smart instrument, the coordinated control degree Cg of each smart instrument in the fluctuation correlation set of the benchmark object is calculated. ij The analysis and calculation are as follows: ; Among them, the periodic operation time series curve of the smart meter labeled i is selected as the reference object; I is the fluctuation correlation set of the periodic operation time series curve of the smart meter labeled i; j∈I is the periodic operation time series curve of the smart meter labeled j in the fluctuation correlation set I; Cg ij The degree of coordinated control between smart meter i (labeled with K) and smart meter j (labeled with K) is correlated with fluctuations. j,b BE is the inherent regulation coefficient of the smart meter labeled j in the fluctuation correlation set I; j Sy represents the fluctuation intensity of the periodic operation time series curve of the smart meter labeled j in the fluctuation correlation set I; ij The synchronous fluctuation correlation index of the periodic operation time sequence curves of smart meters labeled i and j; It should be noted that the inherent adjustment coefficient of a smart meter corresponds to the basic adjustment capability of the smart meter itself, and it is a constant. In the calculation of the coordinated control degree, the fluctuation intensity and adjustment coefficient of each smart instrument in the fluctuation correlation set are determined, and the disturbance influence between each smart instrument and the smart instrument corresponding to the benchmark object is analyzed. The coordinated control degree of the smart instrument corresponding to the benchmark object and each smart instrument in the fluctuation association set is sorted in descending order to generate a coordinated control sequence table of smart instruments, and the adjustment priority is assigned in order according to the sorting of smart instruments in the table; the adjustment command of each smart instrument is output according to the adjustment priority of each smart instrument. It should be noted that the smart meter collaborative control sequence list includes the smart meter label of the reference object, the smart meter labels of each smart meter in the fluctuation association set, and the corresponding collaborative control degree. When allocating the adjustment priority, the smart meters are first sorted in descending order according to the size of the collaborative control degree. The smart meters with larger collaborative control degrees are assigned higher priority to reflect the close influence and correlation between the data fluctuations of their corresponding smart meters and the smart meters of the reference object, and should be given priority for adjustment.
[0012] A multi-control parameter synchronous optimization system for intelligent instrument control: The system includes a data fitting and processing unit, a curve fluctuation difference analysis unit, a curve fluctuation correlation evaluation unit, a curve fluctuation intensity analysis unit, and an instrument adjustment and output unit; The data fitting and processing unit retrieves real-time operation data of each smart meter through the Internet of Things monitoring network, obtains periodic operation time series data, performs curve fitting, and constructs the periodic operation time series curve of the corresponding smart meter. The curve fluctuation difference analysis unit extracts the time sequence curves of each cycle operation and performs fluctuation analysis on the curve amplitudes of adjacent time sequences on each curve to determine the amplitude fluctuation difference rate of adjacent time sequence curve points. The curve fluctuation correlation assessment unit coordinates the amplitude fluctuation difference rate data of the periodic operation time series curves of various smart meters in the target scenario, and performs fluctuation correlation assessment analysis between different periodic operation time series curves; determines the synchronous fluctuation correlation index of different periodic operation time series curves based on the fluctuation correlation assessment data, and constructs the fluctuation correlation set of the corresponding smart meters by correlation aggregation of each periodic operation time series curve; The curve fluctuation intensity analysis unit selects the periodic operation time series curve of any smart instrument in the target scenario as the reference object, retrieves its corresponding fluctuation association set, traverses the periodic operation time series curves of each smart instrument in the fluctuation association set, and performs fluctuation intensity analysis on the periodic operation time series curves of each smart instrument respectively. The instrument regulation output unit performs coordinated regulation degree analysis on each smart instrument in the fluctuation correlation set of the reference object according to the fluctuation intensity of the periodic operation time sequence curve of each smart instrument, and generates a coordinated regulation sequence list of smart instruments based on the analysis results, and determines and outputs the regulation priority of each smart instrument.
[0013] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention achieves parameter correlation determination of different smart meters in a multi-smart meter group scenario by comprehensively analyzing and mining the fluctuation correlation of the operating parameter data of multiple smart meters. This effectively solves the defect of independent and disorderly adjustment of traditional smart meters and improves the coordination and adaptability of multi-meter group operation. 2. This invention accurately captures the dynamic fluctuation relationship between different smart instrument parameters by performing curve fitting, amplitude fluctuation analysis and fluctuation correlation evaluation on the operating data of smart instruments, so as to realize the orderly adjustment needs of multiple instrument parameters in complex working conditions, making the control commands more targeted and adapting to the characteristics of multi-parameter, nonlinear and time-varying working conditions in industrial processes. 3. This invention precisely quantifies the degree of coordinated control of each smart instrument and generates adjustment commands to enable dynamic coordinated adjustment of the parameters of each smart instrument, avoiding problems such as parameter imbalance and abnormal operating conditions caused by disordered adjustment of a single instrument, thereby improving the coordinated control of the smart instrument group. Attached Figure Description
[0014] Figure 1 This is a flowchart illustrating a method for synchronous optimization of multiple control parameters for intelligent instrument control according to the present invention. Detailed Implementation
[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0016] Example 1: As Figure 1 As shown, the present invention provides a technical solution: A method for synchronous optimization of multiple control parameters for intelligent instrument control, comprising the following steps: Establish an IoT monitoring network to collect periodic operation time-series data of various smart meters in the target scenario; Curve fitting is performed on the periodic operation time series data of each smart meter to construct the periodic operation time series curve, and the amplitude fluctuation difference rate of adjacent time series on each periodic operation time series curve is analyzed. Based on the amplitude fluctuation difference rate of the operation time series curves of each cycle, a fluctuation correlation assessment analysis is performed on the operation time series curves of different cycles to obtain the synchronous fluctuation correlation index of the operation time series curves of different cycles, and a fluctuation correlation set of the operation time series curves of the smart instrument is constructed. The fluctuation intensity is analyzed based on the periodic operation time-series curves of each smart instrument in the fluctuation correlation set. Combined with the synchronous fluctuation correlation index, the coordinated control degree of each smart instrument in the fluctuation correlation set is analyzed, and smart instrument control instructions are generated.
[0017] Furthermore, each smart meter in the target scenario is located by tagging, and an IoT monitoring network is built by calling the local IoT to monitor each smart meter in relation to the other. The real-time operation data of each smart meter is retrieved through the IoT monitoring network, and a periodic window is constructed to slice the real-time operation data of each smart meter to obtain periodic operation time sequence data. It should be noted that the target scenario is pre-selected and defined by the operators; The IoT monitoring network connects the data ports of each smart meter in the target scenario to synchronously retrieve data from the smart meters and feed it back to the management port. The slicing process involves shifting and slicing the data on the time axis corresponding to the real-time operation data of each smart meter to obtain operation time data for different periods. The periodic operation time sequence data includes the operation data of the corresponding smart meter, the collection timestamp, and the smart meter's tag number.
[0018] In this embodiment, the operating data of the smart instrument is a continuous time-series data consisting of process quantities collected in real time, intermediate quantities generated by calculation, and control quantities output by the smart instrument during the process of monitoring operating conditions and adjusting parameters. In this embodiment, the operation time sequence data monitored by each smart meter is used to adjust the monitoring data in real time. Therefore, real-time periodic operation time sequence data is used as the analysis object of the periodic operation time sequence data of each smart meter in this embodiment. For example, real-time temperature data collected by smart instruments during industrial heating processes; Alternatively, in fluid transport systems, smart instruments monitor real-time flow and valve opening timing data.
[0019] Furthermore, the curve simulation model is called to perform curve fitting on the periodic operation time series data of each smart meter, and the periodic operation time series curve of the corresponding smart meter is constructed. The time-series curves for each cycle are extracted, and fluctuation analysis is performed on the curve amplitudes of adjacent time points on each curve. The curve values at each time point are traversed according to the time-series axis, and the amplitude difference ΔY between adjacent time points is calculated. i (t, t+1); where i is the smart meter tag number; t and t+1 are adjacent time points t and t+1 within the period; It should be noted that the difference in curve amplitude is used to measure the range of change in curve amplitude between adjacent time points, therefore the result of the calculation of the difference in curve amplitude between adjacent time points is a positive number; The coordinates of the curve points corresponding to each time point are extracted from the time-series curve of the periodic operation, and the amplitude variation vector of the curve points is constructed by associating the origin of the coordinates; the angle between the amplitude variation vectors of curve points of adjacent time series is determined, and the amplitude fluctuation difference rate AF of adjacent time series curve points on the periodic operation time-series curve is calculated. i The analysis is performed on (t, t+1); the calculation is as follows: ; Among them, AF i (t, t+1) represents the amplitude fluctuation difference rate between adjacent time points t and t+1 on the periodic operation time sequence curve of the smart instrument with tag i; θ(R t ,R t+1 R is the angle between the amplitude vectors of adjacent curve points on the periodic operation time sequence curve of the smart instrument with tag i; t and R t+1 These are the amplitude variation vectors of adjacent curve points on the periodic operation time sequence curve of the smart meter labeled i; It should be noted that the curve amplitude variation vector is constructed by taking the origin of the coordinate system as the starting point of the vector and extending it to the curve points at each time point; it is used to measure the changing trend of the curve amplitude from the starting point of the period to the time point corresponding to its curve point. Secondly, when calculating the amplitude fluctuation difference rate between adjacent time curve points on the time series curve of a periodic operation, the similarity analysis of the amplitude change vector of adjacent curve points is called. This is used to analyze the amplitude fluctuation difference between adjacent time curve points to determine the degree of amplitude fluctuation difference between adjacent curve points on the curve in the current cycle. It not only measures the amplitude difference between adjacent curve points, but also takes into account the amplitude fluctuation difference between adjacent curve points, which is more in line with the volatility analysis between data. In this embodiment, the angle range between the amplitude vectors of adjacent time curve points on the periodic operation time series curve should be [0, π / 2], and the corresponding similarity value range should be [0, 1]. This involves monitoring the values of the periodic operation time series curve on continuous time series for anomalies. Based on the similarity judgment interval, if the periodic operation curve values of adjacent time series are within the [0, 1] interval, it can be determined that the curve values on adjacent time series conform to the associated development trend. If they are not within the [0, 1] interval, it indicates that there is an abnormal jump in the curve values on adjacent time series, which does not conform to normal data fluctuations. Therefore, it is judged as abnormal data, marked, and alerted to the management personnel.
[0020] Furthermore, the periodic operation time-series curves of various smart meters within the target scenario are coordinated to determine the corresponding amplitude fluctuation difference rate data, and fluctuation correlation assessment analysis is performed between different periodic operation time-series curves; wherein, the fluctuation correlation assessment analysis includes fluctuation correlation analysis and fluctuation synchronization rate analysis.
[0021] Furthermore, the fluctuation correlation analysis is performed based on the amplitude fluctuation difference rate of the time series curves of operations with different cycles to obtain the fluctuation correlation coefficient SC between the time series curves of operations with any cycle. ij The calculation is as follows: ; Among them, SC ij denoted as , where is the correlation coefficient of the fluctuations in the periodic operation time-series curves of smart meters labeled i and j; m is the number of time points on the periodic operation time-series curve; AF i (t,t+1) and AF j (t, t+1) represent the amplitude fluctuation difference rate of adjacent time points t and t+1 on the periodic operation time sequence curves of smart meters with tags i and j, respectively. and These are the average amplitude fluctuation difference rates of curve points at adjacent times t and t+1 on the periodic operation time sequence curves of smart meters labeled i and j, respectively. Perform a non-repeating, full-traversal fluctuation correlation analysis on the periodic operation time-series curves of each smart instrument in the target scenario, and compile and record the analysis results to construct a first-level fluctuation correlation analysis catalog. It should be noted that the purpose of performing time series fluctuation correlation analysis on any non-repeating cycle operation time series curve is to extract the difference rate of curve amplitude fluctuation by synchronously stepping on the time series, and then to determine the fluctuation correlation between any two non-repeating cycle operation time series curves through fluctuation correlation analysis. The primary synchronous time-series fluctuation correlation analysis catalog is used to record the fluctuation correlation coefficients between corresponding non-repeating periodic operation time-series curves. It includes a smart instrument tag catalog and a fluctuation correlation coefficient catalog. The smart instrument tag catalog is used to record the tag data of smart instruments. The fluctuation correlation coefficient catalog is used to record the fluctuation correlation coefficients between the corresponding periodic operation time-series curves corresponding to the smart instrument tag locations. In this embodiment, by performing a non-repeating traversal extraction of the periodic operation time-series curves of each smart instrument in the target scenario, the amplitude fluctuation difference rate between any two periodic operation time-series curves is determined. Through the fluctuation correlation analysis of the synchronous time series, the fluctuation difference correlation mining of any non-repeating periodic operation time-series curves is realized, and the fluctuation relationship between any two smart instrument operation data is deeply analyzed, which facilitates the correlation control of the smart instrument operation.
[0022] Furthermore, the fluctuation synchronization rate analysis involves judging the changing trend of adjacent time series curve values on the time series curves of each cycle operation, and taking into account the changing trend of adjacent time series curve values on each curve, and performing fluctuation synchronization rate analysis on the changing trend of adjacent time series curve values on the time series curves of different cycles operation. The analysis of the changing trends of adjacent time series curve values on the time series curves of each cycle operation is as follows: ; Where, dir i (t) represents the trend index of the curve value at time t on the periodic operation time sequence curve of the smart instrument with tag i; Y i (t) and Y i (t+1) represent the curve values at time t and time t+1 on the periodic operation time sequence curve of the smart meter with tag i, respectively; Based on the analysis results of the changing trend index of adjacent time series curve values on the time series curve of each cycle, the changing trend of curve values at each time point on the curve is judged; It should be noted that when analyzing the trend of curve values at each moment on the time-series curve of a periodic operation, the instantaneous fluctuation direction at the corresponding moment is determined by calculating the amplitude changes of curve values at adjacent moments; the trend of change includes rising, unchanged, and falling. In this embodiment, regarding dir i The calculation of (t) determines the instantaneous fluctuation direction at time t on the periodic operation time sequence curve of the smart instrument with tag i, which is determined based on the curve value difference between time t and time t+1; it is particularly important to note that dir i (t) The sign of the difference in the analysis curve is independent of the magnitude of the difference; For example, when dir i When (t)=1, its corresponding Y i (t+1)-Y i (t)>0 indicates that the trend of the time sequence curve of the corresponding periodic operation at time t is upward; When dir i When (t)=0, its corresponding Y i (t+1)-Y i (t) = 0, indicating that the trend of the time sequence curve of the corresponding periodic operation at time t is stable and unchanged; When dir i When (t)=-1, its corresponding Y i (t+1)-Y i (t) < 0 indicates that the trend of the time sequence curve of the corresponding periodic operation at time t is downward. Based on the trend of curve value changes at each time point on the time series curves of different cycles, the consistency of the curve value changes at the same time point on the time series curves of different cycles is analyzed. A consistency discriminant function is constructed for analysis, and its calculation is as follows: ; Among them, Q ij (t) is the consistency discrimination function for the curve value change trend at the same moment on the time sequence curve of the periodic operation of smart meters with labels i and j; Based on the consistency of the curve value change trends at the same moment on the time-series curves of different cycles, record the number of curve values with consistent change trends at the same moment on different curves; and record the fluctuation synchronization rate Fs of the time-series curves of different cycles. ij The analysis and calculation are as follows: ; Among them, Fs ij Let w1 and w2 be the fluctuation synchronization rate of the time-series curves of the periodic operation of smart meters labeled i and j; w1 and w2 are the assigned weights; nmax is the number of consecutive curve values with the same trend at the same time on the time-series curves of different periodic operations; N is the number of curve values with the same trend at the same time on the time-series curves of different periodic operations; and m is the number of time points on the time-series curves of periodic operations. The specific values for the weights of w1 and w2 are determined by the operators based on historical data and actual working conditions. It should be noted that when analyzing the fluctuation synchronization rate of time-series curves for different cycles, the changing trend of the curve values at the same moment is judged first, and the number of curve values at the same moment with the same changing trend is considered based on the judgment result. In the subsequent calculation of the fluctuation synchronization rate, the analysis is carried out by combining the results of the consistency discriminant function analysis and the proportion of the number of curve values with the largest continuous simultaneous moment changing trend. It is considered that when the fluctuation synchronization rate analysis is carried out solely based on the analysis results of the consistency discriminant function, different time-series curves for different cycles may have different curve fluctuation distributions under the same consistency discriminant function analysis results. Therefore, by introducing the proportion of the number of curve values with the largest continuous simultaneous moment changing trend, this situation is distinguished, thereby improving the accuracy of the curve fluctuation synchronization rate. In this embodiment, if there are 10 time-stamped curve values on both of the two cycle operation time-series curves, and according to the consistency function judgment result, there are 7 time-stamped curve values with the same trend, then there are multiple curve fluctuation distribution situations. For example, the trend of 5 consecutive simultaneous curve values is consistent and the trend of 2 consecutive simultaneous curve values is consistent, that is, the fluctuation distribution of the two curves is 5+2; or it is 4 consecutive simultaneous curve values with the same trend, 2 consecutive simultaneous curve values with the same trend and 1 simultaneous curve value with the same trend, that is, 4+2+1. However, if the final fluctuation synchronization rate is calculated solely using the consistency discriminant function in both of the above scenarios, the result will be... However, different curve fluctuation distributions can affect the actual fluctuation relationship between the two curves, therefore, we need to introduce... The calculations can deeply explore the fluctuation synchronization between time-series curves of different cycle operations. With the weights assigned to both curves being 1 / 2, the final fluctuation synchronization rates for the two scenarios are approximately 0.75 for the 5+2 curve scenario and approximately 0.67 for the 4+2+1 curve scenario. In comparison, the 5+2 curve scenario is significantly more in line with the actual needs for analyzing the fluctuation synchronization rates of time-series curves of different cycle operations. Perform a non-repeating full traversal of the fluctuation synchronization rate analysis on the periodic operation time series curves of each smart instrument in the target scenario, and compile the analysis results to construct a secondary synchronization volatility analysis catalog. It should be noted that the secondary synchronization volatility analysis directory is used to record the volatility synchronization rate between corresponding non-repeating periodic operation time series curves. It includes a smart instrument tag directory and a volatility synchronization rate directory. The smart instrument tag directory is used to record the tag data of smart instruments. The volatility synchronization rate directory is used to record the volatility synchronization rate between the corresponding periodic operation time series curves located by the corresponding smart instrument tags.
[0023] Furthermore, the correlation coefficient SC between the fluctuations of the periodic operation time-series curves of different smart meters was analyzed.ij And volatility synchronization rate Fs ij The synchronous fluctuation correlation index Sy for time series curves of different cycle operations ij The analysis and calculation are as follows: ; Among them, Sy ij The synchronous fluctuation correlation index of the periodic operation time-series curves of smart meters labeled i and j; SC ij The correlation coefficient of fluctuation in the time-series curves of the periodic operation of smart meters labeled i and j; Fs ij The fluctuation synchronization rate of the periodic operation time sequence curves of smart meters labeled i and j; Based on the analysis data of the synchronous fluctuation correlation index between the periodic operation time series curves of each smart meter, the fluctuation correlation set of the corresponding smart meter is constructed by correlation aggregation of the periodic operation time series curves. In this example, within each fluctuation correlation set, the synchronous fluctuation correlation index of any different periodic operation time series curve is located and extracted using the labels of the periodic operation time series curves. The correlation aggregation involves setting a reference threshold for the synchronous fluctuation correlation index, comparing the synchronous fluctuation correlation index between time-series curves of operations with different cycles, and judging the time-series curves of operations with different cycles that are greater than or equal to the threshold as having strong fluctuation correlation based on the comparison results; conversely, judging the time-series curves of operations with different cycles that are less than the threshold as having weak fluctuation correlation based on the comparison results. Then, by selecting the periodic operation time series curve of any smart meter as the association aggregation object, the periodic operation time series curves of smart meters that have strong fluctuation correlation with it are associated and aggregated. Among them, the reference threshold for the synchronous fluctuation correlation index is a value preset by the operators based on data analysis and experience analysis; It should be noted that when calculating the correlation index of synchronous fluctuations of time-series curves of different cycles, the absolute value of the correlation coefficient is used because the fluctuations between different curves may be in the same direction or in opposite directions. The direction of fluctuation contained in the numerical sign of the correlation coefficient is the overall direction of fluctuation of the two curves, which is not precise enough. Therefore, the absolute value is used only for the numerical part that represents the correlation strength of the curves in the calculation, while the specific direction of fluctuation is reflected by the synchronization rate of the fluctuations of the two curves. In this embodiment, since the fluctuation correlation coefficient of any two-cycle operation time-series curves can be positive or negative, a positive result indicates a positive correlation between the fluctuations of the two curves, and a negative result indicates a negative correlation between the fluctuations of the two curves. Thus, the numerical value in the fluctuation correlation coefficient represents the correlation strength between the two curves, while the sign indicates the correlation direction. However, since the correlation direction of the correlation coefficient only considers whether the overall fluctuation directions of the two curves are the same or opposite, it does not take into account the fluctuation distribution of the curve values at each time point. Therefore, it does not meet the actual needs of intelligent instrument parameter control. Therefore, the absolute value calculation is used to retain only the numerical part to represent the correlation strength between the two curves and participate in the fluctuation synchronization rate calculation.
[0024] Furthermore, the periodic operation time-series curve of any smart meter in the target scenario is selected as the benchmark object. Its corresponding fluctuation correlation set is retrieved, and the periodic operation time-series curves of each smart meter in the fluctuation correlation set are traversed. The fluctuation intensity BE is then applied to the periodic operation time-series curve of each smart meter. i The analysis and calculation are as follows: ; Among them, BE i The fluctuation intensity of the periodic operation time sequence curve of the i-label smart meter; The mean of the amplitude difference between adjacent time values on the periodic operation time sequence curve of the i-label smart meter; σ[ΔY i [t, t+1)] represents the standard deviation of the difference in amplitude between adjacent curve values on the periodic operation time sequence curve of the smart instrument with label i. Based on the fluctuation intensity of the periodic operation time-series curves of each smart instrument, the coordinated control degree Cg of each smart instrument in the fluctuation correlation set of the benchmark object is calculated. ij The analysis and calculation are as follows: ; Among them, the periodic operation time series curve of the smart meter labeled i is selected as the reference object; I is the fluctuation correlation set of the periodic operation time series curve of the smart meter labeled i; j∈I is the periodic operation time series curve of the smart meter labeled j in the fluctuation correlation set I; Cg ij The degree of coordinated control between smart meter i (labeled with K) and smart meter j (labeled with K) is correlated with fluctuations. j,b BE is the inherent regulation coefficient of the smart meter labeled j in the fluctuation correlation set I; j Sy represents the fluctuation intensity of the periodic operation time series curve of the smart meter labeled j in the fluctuation correlation set I; ij The synchronous fluctuation correlation index of the periodic operation time sequence curves of smart meters labeled i and j; It should be noted that the inherent adjustment coefficient of a smart meter corresponds to the basic adjustment capability of the smart meter itself, and it is a constant. In the calculation of the coordinated control degree, the fluctuation intensity and adjustment coefficient of each smart instrument in the fluctuation correlation set are determined, and the disturbance influence between each smart instrument and the smart instrument corresponding to the benchmark object is analyzed. In this embodiment, the fluctuation disturbance impact between each smart meter in the fluctuation association set and the benchmark smart meter is determined by coupling the fluctuation intensity of each smart meter in the fluctuation association set, the fluctuation intensity of the benchmark smart meter, and the synchronous fluctuation association index with the benchmark smart meter. The coordinated control degree of the smart instrument corresponding to the benchmark object and each smart instrument in the fluctuation association set is sorted in descending order to generate a coordinated control sequence table of smart instruments, and the adjustment priority is assigned in order according to the sorting of smart instruments in the table; the adjustment command of each smart instrument is output according to the adjustment priority of each smart instrument. It should be noted that the smart meter collaborative control sequence list includes the smart meter label of the reference object, the smart meter labels of each smart meter in the fluctuation association set, and the corresponding collaborative control degree. When allocating the adjustment priority, the smart meters are first sorted in descending order according to the size of the collaborative control degree. The smart meters with larger collaborative control degrees are assigned higher priority to reflect the close influence and correlation between the data fluctuations of their corresponding smart meters and the smart meters of the reference object, and should be given priority for adjustment.
[0025] Example 2: The present invention provides another technical solution: A multi-control parameter synchronous optimization system for intelligent instrument control: The system includes a data fitting and processing unit, a curve fluctuation difference analysis unit, a curve fluctuation correlation evaluation unit, a curve fluctuation intensity analysis unit, and an instrument adjustment and output unit; The data fitting and processing unit retrieves real-time operation data of each smart meter through the Internet of Things monitoring network, obtains periodic operation time series data, performs curve fitting, and constructs the periodic operation time series curve of the corresponding smart meter. The curve fluctuation difference analysis unit extracts the time sequence curves of each cycle operation and performs fluctuation analysis on the curve amplitudes of adjacent time sequences on each curve to determine the amplitude fluctuation difference rate of adjacent time sequence curve points. The curve fluctuation correlation assessment unit coordinates the amplitude fluctuation difference rate data of the periodic operation time series curves of various smart meters in the target scenario, and performs fluctuation correlation assessment analysis between different periodic operation time series curves; determines the synchronous fluctuation correlation index of different periodic operation time series curves based on the fluctuation correlation assessment data, and constructs the fluctuation correlation set of the corresponding smart meters by correlation aggregation of each periodic operation time series curve; The curve fluctuation intensity analysis unit selects the periodic operation time series curve of any smart instrument in the target scenario as the reference object, retrieves its corresponding fluctuation association set, traverses the periodic operation time series curves of each smart instrument in the fluctuation association set, and performs fluctuation intensity analysis on the periodic operation time series curves of each smart instrument respectively. The instrument regulation output unit performs coordinated regulation degree analysis on each smart instrument in the fluctuation correlation set of the reference object according to the fluctuation intensity of the periodic operation time sequence curve of each smart instrument, and generates a coordinated regulation sequence list of smart instruments based on the analysis results, and determines and outputs the regulation priority of each smart instrument.
[0026] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.
Claims
1. A method for synchronous optimization of multiple control parameters for intelligent instrument control, characterized in that: The method includes the following steps: Establish an IoT monitoring network to collect periodic operation time-series data of various smart meters in the target scenario; Based on the collected periodic operation time series data, curve fitting is performed to construct the periodic operation time series curve corresponding to each smart meter, and the amplitude fluctuation difference rate of adjacent time series points on each periodic operation time series curve is analyzed. Based on the amplitude fluctuation difference rate of the operation time series curves of each cycle, the fluctuation correlation of the operation time series curves of different cycles is evaluated, the synchronous fluctuation correlation index of the operation time series curves of different cycles is obtained, and the fluctuation correlation set is constructed. The fluctuation intensity is analyzed based on the periodic operation time-series curves of each smart instrument within the fluctuation correlation set, and the coordinated control degree of each smart instrument is analyzed in conjunction with the synchronous fluctuation correlation index to generate smart instrument control commands.
2. The method for synchronous optimization of multiple control parameters for intelligent instrument control according to claim 1, characterized in that: The curve simulation model is called to perform curve fitting on the periodic operation time series data of each smart meter, and the periodic operation time series curve of the corresponding smart meter is constructed. The time-series curves for each cycle are extracted, and fluctuation analysis is performed on the curve amplitudes of adjacent time points on each curve. The curve values at each time point are traversed according to the time-series axis, and the amplitude difference ΔY between adjacent time points is calculated. i (t, t+1); where i is the smart meter tag number; t and t+1 are adjacent time points t and t+1 within the period; The coordinates of the curve points corresponding to each time point are extracted from the time-series curve of the periodic operation, and the amplitude variation vector of the curve points is constructed by associating the origin of the coordinates; the angle between the amplitude variation vectors of curve points of adjacent time series is determined, and the amplitude fluctuation difference rate AF of adjacent time series curve points on the periodic operation time-series curve is calculated. i The analysis is performed on (t, t+1); the calculation is as follows: ； Among them, AF i (t, t+1) represents the amplitude fluctuation difference rate between adjacent time points t and t+1 on the periodic operation time sequence curve of the smart instrument with tag i; θ(R t ,R t+1 R is the angle between the amplitude vectors of adjacent curve points on the periodic operation time sequence curve of the smart instrument with tag i; t and R t+1 These are the amplitude variation vectors of adjacent curve points on the periodic operation time sequence curve of the smart meter labeled i.
3. The method for synchronous optimization of multiple control parameters for intelligent instrument control according to claim 1, characterized in that: The system coordinates the periodic operation time-series curves of various smart meters within the target scenario, determines the corresponding amplitude fluctuation difference rate data, and conducts fluctuation correlation assessment and analysis between different periodic operation time-series curves; wherein, the fluctuation correlation assessment and analysis includes fluctuation correlation analysis and fluctuation synchronization rate analysis.
4. The method for synchronous optimization of multiple control parameters for intelligent instrument control according to claim 3, characterized in that: The volatility correlation analysis is performed based on the amplitude volatility difference rate of the time series curves of operations with different cycles, to obtain the volatility correlation coefficient SC between the time series curves of operations with any cycle. ij The calculation is as follows: ； Among them, SC ij denoted as , where is the correlation coefficient of the fluctuations in the periodic operation time-series curves of smart meters labeled i and j; m is the number of time points on the periodic operation time-series curve; AF i (t,t+1) and AF j (t, t+1) represent the amplitude fluctuation difference rate of adjacent time points t and t+1 on the periodic operation time sequence curves of smart meters with tags i and j, respectively. and These are the average amplitude fluctuation difference rates of curve points at adjacent times t and t+1 on the periodic operation time sequence curves of smart meters labeled i and j, respectively. A non-repeating, full-traversal fluctuation correlation analysis is performed on the periodic operation time series curves of each smart instrument in the target scenario, and the analysis results are recorded in a coordinated manner to construct a first-level fluctuation correlation analysis catalog.
5. The method for synchronous optimization of multiple control parameters for intelligent instrument control according to claim 3, characterized in that: The fluctuation synchronization rate analysis is to judge the changing trend of adjacent time series curve values on the time series curve of each cycle operation, and to take into account the changing trend of adjacent time series curve values on each curve, and to perform fluctuation synchronization rate analysis on the changing trend of adjacent time series curve values on the time series curve of different cycles operation. The analysis of the changing trends of adjacent time series curve values on the time series curves of each cycle operation is as follows: ； Where, dir i (t) represents the trend index of the curve value at time t on the periodic operation time sequence curve of the smart instrument with tag i; Y i (t) and Y i (t+1) represent the curve values at time t and time t+1 on the periodic operation time sequence curve of the smart meter with tag i, respectively; Based on the analysis results of the changing trend index of adjacent time series curve values on the time series curve of each cycle, the changing trend of curve values at each time point on the curve is judged; Based on the trend of curve value changes at each time point on the time series curves of different cycles, the consistency of the curve value changes at the same time point on the time series curves of different cycles is analyzed. A consistency discriminant function is constructed for analysis, and its calculation is as follows: ； Among them, Q ij (t) is the consistency discrimination function for the curve value change trend at the same moment on the time sequence curve of the periodic operation of smart meters with labels i and j; Based on the consistency of the curve value change trends at the same moment on the time-series curves of different cycles, record the number of curve values with consistent change trends at the same moment on different curves; and record the fluctuation synchronization rate Fs of the time-series curves of different cycles. ij The analysis and calculation are as follows: ； Among them, Fs ij Let w1 and w2 be the fluctuation synchronization rate of the time-series curves of the periodic operation of smart meters labeled i and j; w1 and w2 are the assigned weights; nmax is the number of consecutive curve values with the same trend at the same time on the time-series curves of different periodic operations; N is the number of curve values with the same trend at the same time on the time-series curves of different periodic operations; and m is the number of time points on the time-series curves of periodic operations. A non-repeating, full-traversal fluctuation synchronization rate analysis is performed on the periodic operation time-series curves of each smart instrument in the target scenario, and the analysis results are recorded in a coordinated manner to construct a secondary synchronization volatility analysis catalog.
6. The method for synchronous optimization of multiple control parameters for intelligent instrument control according to claim 3, characterized in that: The correlation coefficient SC between the cyclic operation time-series curves of different smart meters ij And volatility synchronization rate Fs ij The synchronous fluctuation correlation index Sy for time series curves of different cycle operations ij The analysis and calculation are as follows: ； Among them, Sy ij The synchronous fluctuation correlation index of the periodic operation time-series curves of smart meters labeled i and j; SC ij The correlation coefficient of fluctuation in the time-series curves of the periodic operation of smart meters labeled i and j; Fs ij The fluctuation synchronization rate of the periodic operation time sequence curves of smart meters labeled i and j; Based on the analysis data of the synchronous fluctuation correlation index between the periodic operation time series curves of each smart meter, the fluctuation correlation set of the corresponding smart meter is constructed by correlation aggregation of the periodic operation time series curves. The correlation aggregation involves setting a reference threshold for the synchronous fluctuation correlation index, comparing the synchronous fluctuation correlation index between time-series curves of operations with different cycles, and judging the time-series curves of operations with different cycles that are greater than or equal to the threshold as having strong fluctuation correlation based on the comparison results; conversely, judging the time-series curves of operations with different cycles that are less than the threshold as having weak fluctuation correlation based on the comparison results. Then, by selecting the periodic operation time series curve of any smart meter as the associated aggregation object, the periodic operation time series curves of smart meters that have a strong fluctuation correlation with it are associated and aggregated.
7. The method for synchronous optimization of multiple control parameters for intelligent instrument control according to claim 1, characterized in that: The periodic operation time-series curve of any smart meter in the target scenario is selected as the reference object. The corresponding fluctuation correlation set is retrieved, and the periodic operation time-series curves of each smart meter in the fluctuation correlation set are traversed. The fluctuation intensity BE is then applied to the periodic operation time-series curve of each smart meter. i The analysis and calculation are as follows: ； Among them, BE i The fluctuation intensity of the periodic operation time sequence curve of the i-label smart meter; The mean of the amplitude difference between adjacent time points on the periodic operation time sequence curve of the tag i smart meter; σ[ΔY i [t, t+1)] represents the standard deviation of the difference in amplitude between adjacent time values on the periodic operation time sequence curve of the smart instrument with label i; Based on the fluctuation intensity of the periodic operation time-series curves of each smart instrument, the coordinated control degree Cg of each smart instrument in the fluctuation correlation set of the benchmark object is calculated. ij The analysis and calculation are as follows: ； Among them, the periodic operation time series curve of the smart meter labeled i is selected as the reference object; I is the fluctuation correlation set of the periodic operation time series curve of the smart meter labeled i; j∈I is the periodic operation time series curve of the smart meter labeled j in the fluctuation correlation set I; Cg ij The degree of coordinated control between smart meter i (labeled with K) and smart meter j (labeled with K) is correlated with fluctuations. j,b BE is the inherent regulation coefficient of the smart meter labeled j in the fluctuation correlation set I; j Sy represents the fluctuation intensity of the periodic operation time series curve of the smart meter labeled j in the fluctuation correlation set I; ij The synchronous fluctuation correlation index is used for the periodic operation time sequence curves of smart meters labeled i and j.
8. The method for synchronous optimization of multiple control parameters for intelligent instrument control according to claim 7, characterized in that: The coordinated control degree of the smart meter corresponding to the benchmark object and each smart meter in its fluctuation association set is sorted in descending order to generate a coordinated control sequence table of smart meters, and the adjustment priority is assigned in sequence according to the sorting of the smart meters in the table. The intelligent instrument adjustment commands are output according to the adjustment priority of each intelligent instrument.
9. The method for synchronous optimization of multiple control parameters for intelligent instrument control according to claim 1, characterized in that: The system locates each smart meter in the target scenario by tagging it, and then uses a local IoT network to build an IoT monitoring network to monitor each smart meter in relation to the other. The real-time operation data of each smart meter is retrieved through the IoT monitoring network, and a periodic window is constructed to slice the real-time operation data of each smart meter to obtain periodic operation time sequence data.
10. A system for executing the multi-control parameter synchronization optimization method for intelligent instrument control as described in any one of claims 1-9, characterized in that: The system includes a data fitting and processing unit, a curve fluctuation difference analysis unit, a curve fluctuation correlation evaluation unit, a curve fluctuation intensity analysis unit, and an instrument adjustment and output unit; The data fitting and processing unit retrieves real-time operation data of each smart meter through the Internet of Things monitoring network, obtains periodic operation time series data, performs curve fitting, and constructs the periodic operation time series curve of the corresponding smart meter. The curve fluctuation difference analysis unit extracts the time sequence curves of each cycle operation and performs fluctuation analysis on the curve amplitudes of adjacent time sequences on each curve to determine the amplitude fluctuation difference rate of adjacent time sequence curve points. The curve fluctuation correlation evaluation unit integrates the amplitude fluctuation difference rate data of the periodic operation time series curves of various smart instruments, and performs fluctuation correlation evaluation analysis on different periodic operation time series curves. Determine the synchronous fluctuation correlation index of the time sequence curves of different cycle operations, and construct the corresponding fluctuation correlation set of smart meters; The curve fluctuation intensity analysis unit selects the periodic operation time series curve of any smart instrument as the reference object, retrieves its corresponding fluctuation association set, and performs fluctuation intensity analysis on the periodic operation time series curve of each smart instrument in the fluctuation association set. The instrument regulation output unit analyzes the coordinated regulation degree of each smart instrument in the fluctuation correlation set of the reference object according to the fluctuation intensity of the periodic operation time sequence curve of each smart instrument, generates a coordinated regulation sequence list of smart instruments, and determines and outputs the regulation priority of each smart instrument.