HPLC-based multi-scenario data acquisition method and system for power Internet of Things

Abstract

The HPLC-based electric power Internet of Things multi-scenario data acquisition method and system disclosed in the present invention conducts experiments in different set scenarios to collect measured parameters of HPLC equipment and environmental characteristic values ​​of the set environment, and constructs an optimization parameter linear equation group under different scenarios based on the collected measured parameters, environmental characteristic values ​​and preset theoretical parameters. The parameters of the HPLC equipment in the current environment are then optimized through the optimization parameter linear equation group under different scenarios, thereby improving the accuracy of data collected by the HPLC equipment in the current scenario.

Description

Technical Field

[0001] The present invention relates to the technical field of power data acquisition, and more specifically, to a method and system for acquiring multi-scenario data of the power Internet of Things based on HPLC. Background Art

[0002] With the rapid development of smart grids, high-speed power line carrier communication (HPLC) has become a core communication method for smart meter data collection and distributed energy monitoring, thanks to its wide coverage and no need for additional wiring. However, this wide coverage also puts pressure on manual operations and maintenance, as well as on-site troubleshooting. Furthermore, different scenarios affect HPLC equipment differently, making a uniform adjustment inappropriate for actual conditions.

[0003] Therefore, the existing technology has defects and needs to be improved urgently. Summary of the Invention

[0004] In order to solve at least one of the above technical problems, the purpose of the present invention is to provide a multi-scenario data acquisition method and system for the power Internet of Things based on HPLC, which can improve the accuracy of data collected by HPLC equipment in the current scenario.

[0005] The first aspect of the present invention provides a multi-scenario data collection method for the power Internet of Things based on HPLC, comprising:

[0006] Deploy HPLC equipment in a set scenario and collect measured data during the operation of the HPLC equipment through the power Internet of Things. The set scenario includes at least a fixed laboratory, a GMP workshop, and an explosion-proof area.

[0007] Based on a preset sensor, obtain environmental characteristic values ​​in a set scene, wherein the environmental characteristic values ​​at least include an environmental noise communication signal value and a temperature value;

[0008] Based on the measured parameters, preset theoretical parameters and environmental characteristics, a linear equation system for optimizing parameters of HPLC equipment in different scenarios is constructed;

[0009] Obtain the voltage fluctuation value, temperature gradient change value, and noise intensity value of the HPLC equipment in the current scenario;

[0010] The voltage fluctuation value, temperature gradient change value, and noise intensity value of the HPLC device in the current scenario are input into the optimization parameter linear equation of the corresponding scenario to obtain the optimization parameters of the HPLC device in the current scenario;

[0011] Adjust the HPLC equipment in the current scenario according to the corresponding optimization parameters to improve data acquisition accuracy.

[0012] In this solution, the steps of constructing a set of linear equations for optimizing parameters of HPLC equipment in different scenarios based on measured parameters, preset theoretical parameters and environmental characteristic values ​​specifically include:

[0013] According to the measured parameters and the preset theoretical parameters, the parameter error set and the corresponding measured error index are obtained;

[0014] If the measured error index is greater than the preset error index threshold, the environmental characteristics of the corresponding time node of the set scene are saved to obtain the saved environmental characteristic value;

[0015] Extract the voltage value from the measured parameters at the corresponding time node;

[0016] Perform feature analysis on the saved environmental characteristic values ​​and the voltage values ​​in the measured parameters at the corresponding time nodes to obtain voltage fluctuation values, temperature gradient change values, and noise intensity values;

[0017] The voltage fluctuation value, temperature gradient change value and noise intensity value are divided into multiple sub-intervals and combined to obtain different characteristic intervals;

[0018] Extracting parameter errors from a parameter error set;

[0019] A multivariate linear equation is constructed by combining the parameter error with the voltage fluctuation value, temperature gradient change value, and noise intensity value in the characteristic interval of the corresponding measured parameter to obtain the optimized parameter linear equation for the corresponding characteristic interval;

[0020] After traversing all parameter errors in the parameter error set, the optimized parameter linear equations of the HPLC equipment in different scenarios are obtained.

[0021] In this solution, the step of performing feature analysis on the stored environmental characteristic values ​​and the voltage values ​​in the measured parameters at the corresponding time nodes to obtain the voltage fluctuation value, the temperature gradient change value, and the noise intensity value specifically includes:

[0022] Based on the same time node, the voltage value in the measured parameter is subtracted from the voltage value in the corresponding preset theoretical parameter, and then the absolute value is taken to obtain the voltage fluctuation value at the corresponding time node;

[0023] Extract the time node corresponding to the saved environmental characteristic value and set the temperature gradient change value as , whose formula is ,in Indicates the temperature gradient change value at time node i, represents the temperature value at time node i, Indicates the temperature value at the previous adjacent time node i-1 of time node i;

[0024] The environmental noise communication signal value in the environmental characteristic value is extracted, and the signal and noise in the environmental noise communication signal value are separated based on the preset software to obtain the noise intensity value at different time nodes.

[0025] In this solution, after constructing the multivariate linear equation, the method further includes:

[0026] With parameter error as dependent variable, voltage fluctuation value, temperature gradient change value and noise intensity value as independent variables, a multidimensional space is constructed to obtain the coordinates of each point: ;in represents the parameter error of parameter n at time node i, represents the voltage fluctuation value of the corresponding parameter at time node i, Represents the noise intensity value of the corresponding parameter at time node i;

[0027] Taking the characteristic interval as the benchmark, extract the distance between the points in the corresponding interval and the corresponding multivariate linear equation to obtain the distance set;

[0028] Calculate the mean of the distances in the distance set to obtain the average distance value from the point to the multivariate linear equation;

[0029] If the average distance value from the point to the multivariate linear equation is greater than the preset distance threshold, the corresponding feature interval will be evenly split into two feature sub-intervals, and the multivariate linear equation within the feature sub-interval will be reconstructed. If the average distance value from the point to the multivariate linear equation corresponding to the feature sub-interval is still greater than the preset distance threshold, the feature sub-interval will continue to be split until the average distance value from the corresponding point to the multivariate linear equation is less than or equal to the preset distance threshold.

[0030] In this solution, the voltage fluctuation value, temperature gradient change value, and noise intensity value of the HPLC device in the current scenario are obtained as follows:

[0031] When the time node is zero, the voltage fluctuation value, temperature gradient change value, and noise intensity value of the HPLC device in the current scenario are the average values ​​of the historical voltage fluctuation value, temperature gradient change value, and noise intensity value of the HPLC device in the corresponding scenario respectively;

[0032] When the time node is 1, the voltage fluctuation value of the HPLC device in the current scenario is the average value of the historical voltage fluctuation values ​​of the HPLC device in the corresponding scenario; the temperature gradient change value is the absolute value of the temperature value at the current time node minus the initial temperature value of the corresponding HPLC device; the noise intensity value is the noise intensity value at the corresponding time node;

[0033] When the time node is greater than 1, the voltage fluctuation value of the HPLC equipment in the current scenario ,in ,m is the number of time nodes, Indicates the voltage value of the HPLC device at time node j in the current scenario, Indicates the average voltage of the HPLC device in the current scenario; the temperature gradient change value is the absolute value of the temperature value at the current time node minus the temperature value at the corresponding previous time node; the noise intensity value is the noise intensity value at the corresponding time node.

[0034] In this solution, after adjusting the HPLC equipment in the current scenario according to the corresponding optimization parameters, it also includes:

[0035] Obtain the voltage fluctuation value and temperature gradient change value of the HPLC equipment at the next time node in the current scenario;

[0036] Multiply the voltage fluctuation value at the next time node by the corresponding weight coefficient to obtain the voltage warning index value; multiply the temperature gradient change value at the next time node by the corresponding weight coefficient to obtain the temperature warning index value;

[0037] The voltage warning index value and the temperature warning index value are accumulated to obtain the warning index value of the current HPLC device;

[0038] If the warning index value of the current HPLC device is greater than a preset first warning threshold, then generating sampling frequency adjustment information;

[0039] If the warning index value of the current HPLC device is greater than the preset second warning threshold, hardware linkage protection information is generated;

[0040] The preset second warning threshold is greater than the preset first warning threshold.

[0041] In this solution, after generating the hardware linkage protection information, the following steps are further included:

[0042] Based on the hardware linkage protection information, the preset management terminal sends an FPGA reset instruction to the HPLC device;

[0043] Based on the FPGA reset instruction, the HPLC device switches the circuit to the backup ADC sampling circuit and starts the redundant communication protocol and device self-test program.

[0044] A second aspect of the present invention provides an HPLC-based electric power Internet of Things multi-scenario data acquisition system, comprising a memory and a processor, wherein the memory stores an HPLC electric power Internet of Things multi-scenario data acquisition method program, and when the HPLC electric power Internet of Things multi-scenario data acquisition method program is executed by the processor, the following steps are implemented:

[0045] Using HPLC technology to collect measured parameters of HPLC equipment in set scenarios, the set scenarios at least include fixed laboratories, GMP workshops, and explosion-proof areas;

[0046] Based on a preset sensor, obtain environmental characteristic values ​​in a set scene, wherein the environmental characteristic values ​​at least include an environmental noise communication signal value and a temperature value;

[0047] Based on the measured parameters, preset theoretical parameters and environmental characteristics, a linear equation system for optimizing parameters of HPLC equipment in different scenarios is constructed;

[0048] Obtain the voltage fluctuation value, temperature gradient change value, and noise intensity value of the HPLC equipment in the current scenario;

[0049] The voltage fluctuation value, temperature gradient change value, and noise intensity value of the HPLC device in the current scenario are input into the optimization parameter linear equation of the corresponding scenario to obtain the optimization parameters of the HPLC device in the current scenario;

[0050] Adjust the HPLC equipment in the current scenario according to the corresponding optimization parameters to improve data acquisition accuracy.

[0051] In this solution, the steps of constructing a set of linear equations for optimizing parameters of HPLC equipment in different scenarios based on measured parameters, preset theoretical parameters and environmental characteristic values ​​specifically include:

[0052] According to the measured parameters and the preset theoretical parameters, the parameter error set and the corresponding measured error index are obtained;

[0053] If the measured error index is greater than the preset error index threshold, the environmental characteristics of the corresponding time node of the set scene are saved to obtain the saved environmental characteristic value;

[0054] Extract the voltage value from the measured parameters at the corresponding time node;

[0055] Perform feature analysis on the saved environmental characteristic values ​​and the voltage values ​​in the measured parameters at the corresponding time nodes to obtain voltage fluctuation values, temperature gradient change values, and noise intensity values;

[0056] The voltage fluctuation value, temperature gradient change value and noise intensity value are divided into multiple sub-intervals and combined to obtain different characteristic intervals;

[0057] Extracting parameter errors from a parameter error set;

[0058] A multivariate linear equation is constructed by combining the parameter error with the voltage fluctuation value, temperature gradient change value, and noise intensity value in the characteristic interval of the corresponding measured parameter to obtain the optimized parameter linear equation for the corresponding characteristic interval;

[0059] After traversing all parameter errors in the parameter error set, the optimized parameter linear equations of the HPLC equipment in different scenarios are obtained.

[0060] In this solution, the step of performing feature analysis on the stored environmental characteristic values ​​and the voltage values ​​in the measured parameters at the corresponding time nodes to obtain the voltage fluctuation value, the temperature gradient change value, and the noise intensity value specifically includes:

[0061] Based on the same time node, the voltage value in the measured parameter is subtracted from the voltage value in the corresponding preset theoretical parameter, and then the absolute value is taken to obtain the voltage fluctuation value at the corresponding time node;

[0062] Extract the time node corresponding to the saved environmental characteristic value and set the temperature gradient change value as , whose formula is ,in Indicates the temperature gradient change value at time node i, represents the temperature value at time node i, Indicates the temperature value at the previous adjacent time node i-1 of time node i;

[0063] The environmental noise communication signal value in the environmental characteristic value is extracted, and the signal and noise in the environmental noise communication signal value are separated based on the preset software to obtain the noise intensity value at different time nodes.

[0064] The HPLC-based electric power Internet of Things multi-scenario data acquisition method and system disclosed in the present invention conducts experiments in different set scenarios to collect measured parameters of HPLC equipment and environmental characteristic values ​​of the set environment, and constructs an optimization parameter linear equation group under different scenarios based on the collected measured parameters, environmental characteristic values ​​and preset theoretical parameters. The parameters of the HPLC equipment in the current environment are then optimized through the optimization parameter linear equation group under different scenarios, thereby improving the accuracy of data collected by the HPLC equipment in the current scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 The flowchart of the HPLC-based multi-scenario data acquisition method of the power Internet of Things of the present invention is shown;

[0066] Figure 2 A flow chart of constructing a linear system of equations with optimized parameters according to the present invention is shown;

[0067] Figure 3 The block diagram of the HPLC-based multi-scenario data acquisition system of the power Internet of Things of the present invention is shown. DETAILED DESCRIPTION

[0068] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0069] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0070] Figure 1 The flowchart of the HPLC-based multi-scenario data acquisition method of the power Internet of Things of the present invention is shown.

[0071] like Figure 1 As shown, the present invention discloses a multi-scenario data acquisition method for the power Internet of Things based on HPLC, comprising:

[0072] S101, deploying HPLC equipment in a set scenario and collecting measured data during the operation of the HPLC equipment through the power Internet of Things, wherein the set scenario at least includes a fixed laboratory, a GMP workshop, and an explosion-proof area;

[0073] S102, acquiring environmental characteristic values ​​in a set scenario based on a preset sensor, the environmental characteristic values ​​including at least an environmental noise communication signal value and a temperature value;

[0074] S103, constructing a linear equation system of optimized parameters for the HPLC equipment under different scenarios based on the measured parameters, preset theoretical parameters, and environmental characteristic values;

[0075] S104, obtaining the voltage fluctuation value, temperature gradient change value, and noise intensity value of the HPLC device in the current scenario;

[0076] S105, inputting the voltage fluctuation value, temperature gradient change value, and noise intensity value of the HPLC device in the current scenario into the optimization parameter linear equation of the corresponding scenario to obtain the optimization parameters of the HPLC device in the current scenario;

[0077] S106, adjusting the HPLC equipment in the current scenario according to the corresponding optimization parameters to improve data acquisition accuracy.

[0078] According to an embodiment of the present invention, before the actual operation of the HPLC device, the parameters of the HPLC device need to be calibrated. The setting scenarios of the test include at least a fixed laboratory, a GMP workshop and an explosion-proof area. By adjusting the preset theoretical parameters and environmental characteristic values ​​under the set environment one by one, the measured parameters of the HPLC device at the corresponding time node when the HPLC device is powered on are obtained. The measured parameters include at least the voltage value and power after the corresponding HPLC device is powered on; for example, if the measured parameter is power, the optimization parameter of the HPLC device for the intelligent measurement switch in the current scenario is +10. If the measured parameter is 1500 watts, the output power collected after the optimization of the corresponding intelligent measurement switch is 1510 watts.

[0079] Figure 2 A flow chart of constructing a linear equation group of optimized parameters according to the present invention is shown.

[0080] like Figure 2 As shown, the steps of constructing a set of linear equations for optimizing parameters of HPLC equipment in different scenarios based on the measured parameters, preset theoretical parameters and environmental characteristic values ​​specifically include;

[0081] S201, obtaining a parameter error set and a corresponding measured error index based on the measured parameters and the preset theoretical parameters;

[0082] S202: If the measured error index is greater than a preset error index threshold, the environmental characteristics of the set scene corresponding to the time node are saved to obtain a saved environmental characteristic value;

[0083] S203, extracting the voltage value from the measured parameters at the corresponding time node;

[0084] S204, performing feature analysis on the stored environmental characteristic values ​​and the voltage values ​​in the measured parameters at the corresponding time nodes to obtain voltage fluctuation values, temperature gradient change values, and noise intensity values;

[0085] S205, dividing the voltage fluctuation value, the temperature gradient change value, and the noise intensity value into multiple sub-intervals and combining them to obtain different characteristic intervals;

[0086] S206, extracting parameter errors from the parameter error set;

[0087] S207, constructing a multivariate linear equation using the parameter error and the voltage fluctuation value, temperature gradient change value, and noise intensity value in the characteristic interval of the corresponding measured parameter to obtain an optimized parameter linear equation for the corresponding characteristic interval;

[0088] S208, after traversing all parameter errors in the parameter error set, obtain the optimized parameter linear equations of the HPLC equipment in different scenarios.

[0089] It should be noted that after obtaining the measured parameters, the measured parameters are marked with a preset time sensing device to obtain the measured parameters at different time nodes; with the same parameter as the benchmark, the preset theoretical parameter is subtracted from the measured parameter to obtain the corresponding parameter error; after traversing all parameters, all parameter errors are combined into a corresponding parameter error set; different error index weights are set for different parameters; based on the same time node, the parameter error is multiplied by the corresponding error index weight to obtain the error index of the corresponding parameter; after traversing all parameters, the error indices of different parameters are accumulated to obtain the measured error index of the current time node; obtain environmental characteristics When the environmental characteristics are detected, the preset time sensing device is used to identify the obtained environmental characteristics to obtain the environmental characteristic values ​​at different time nodes; when the environmental characteristic values ​​are saved, the time node of the saved environmental characteristic values ​​is used as a reference to extract the voltage value in the measured parameters of the corresponding time node, and the voltage fluctuation value, temperature gradient change value and noise intensity value of the same time node are combined, and then divided according to the pre-set division interval to obtain different characteristic intervals, and then the parameter error of the corresponding time node is extracted, and the parameter error is used as the independent variable, and the voltage fluctuation value, temperature gradient change value and noise intensity value of the corresponding time node are used as the dependent variable to construct a multivariate linear equation.

[0090] According to an embodiment of the present invention, the step of performing feature analysis on the stored environmental characteristic values ​​and the voltage values ​​in the measured parameters at the corresponding time nodes to obtain the voltage fluctuation value, the temperature gradient change value, and the noise intensity value specifically includes:

[0091] Based on the same time node, the voltage value in the measured parameter is subtracted from the voltage value in the corresponding preset theoretical parameter, and then the absolute value is taken to obtain the voltage fluctuation value at the corresponding time node;

[0092] Extract the time node corresponding to the saved environmental characteristic value and set the temperature gradient change value as , whose formula is ,in Indicates the temperature gradient change value at time node i, represents the temperature value at time node i, Indicates the temperature value at the previous adjacent time node i-1 of time node i;

[0093] The environmental noise communication signal value in the environmental characteristic value is extracted, and the signal and noise in the environmental noise communication signal value are separated based on the preset software to obtain the noise intensity value at different time nodes.

[0094] According to an embodiment of the present invention, after constructing the multivariate linear equation, the method further includes:

[0095] With parameter error as dependent variable, voltage fluctuation value, temperature gradient change value and noise intensity value as independent variables, a multidimensional space is constructed to obtain the coordinates of each point: ;in represents the parameter error of parameter n at time node i, represents the voltage fluctuation value of the corresponding parameter at time node i, Represents the noise intensity value of the corresponding parameter at time node i;

[0096] Taking the characteristic interval as the benchmark, extract the distance between the points in the corresponding interval and the corresponding multivariate linear equation to obtain the distance set;

[0097] Calculate the mean of the distances in the distance set to obtain the average distance value from the point to the multivariate linear equation;

[0098] If the average distance value from the point to the multivariate linear equation is greater than the preset distance threshold, the corresponding feature interval will be evenly split into two feature sub-intervals, and the multivariate linear equation within the feature sub-interval will be reconstructed. If the average distance value from the point to the multivariate linear equation corresponding to the feature sub-interval is still greater than the preset distance threshold, the feature sub-interval will continue to be split until the average distance value from the corresponding point to the multivariate linear equation is less than or equal to the preset distance threshold.

[0099] It should be noted that the accuracy of the optimization parameters is improved by splitting the optimization parameter linear equation into multiple small interval optimization parameter linear equations.

[0100] According to an embodiment of the present invention, the voltage fluctuation value, temperature gradient change value, and noise intensity value of the HPLC device in the current scenario are obtained as follows:

[0101] When the time node is zero, the voltage fluctuation value, temperature gradient change value, and noise intensity value of the HPLC device in the current scenario are the average values ​​of the historical voltage fluctuation value, temperature gradient change value, and noise intensity value of the HPLC device in the corresponding scenario respectively;

[0102] When the time node is 1, the voltage fluctuation value of the HPLC device in the current scenario is the average value of the historical voltage fluctuation values ​​of the HPLC device in the corresponding scenario; the temperature gradient change value is the absolute value of the temperature value at the current time node minus the initial temperature value of the corresponding HPLC device; the noise intensity value is the noise intensity value at the corresponding time node;

[0103] When the time node is greater than 1, the voltage fluctuation value of the HPLC equipment in the current scenario ,in ,m is the number of time nodes, Indicates the voltage value of the HPLC device at time node j in the current scenario, Indicates the average voltage of the HPLC device in the current scenario; the temperature gradient change value is the absolute value of the temperature value at the current time node minus the temperature value at the corresponding previous time node; the noise intensity value is the noise intensity value at the corresponding time node.

[0104] It should be noted that when the time node is zero, it means that the corresponding HPLC device has not yet been operated in the corresponding set scenario. Therefore, the corresponding optimization parameters are set according to the time node being zero; when the time node is 1, the data of the HPLC device is only collected once; when the time node is greater than 1, since the data of the HPLC device is collected multiple times, the historical voltage fluctuation value of the HPLC device in the current scenario can be ignored.

[0105] According to an embodiment of the present invention, after adjusting the HPLC equipment in the current scenario according to the corresponding optimization parameters, the method further includes:

[0106] Obtain the voltage fluctuation value and temperature gradient change value of the HPLC equipment at the next time node in the current scenario;

[0107] Multiply the voltage fluctuation value at the next time node by the corresponding weight coefficient to obtain the voltage warning index value; multiply the temperature gradient change value at the next time node by the corresponding weight coefficient to obtain the temperature warning index value;

[0108] The voltage warning index value and the temperature warning index value are accumulated to obtain the warning index value of the current HPLC device;

[0109] If the warning index value of the current HPLC device is greater than a preset first warning threshold, then generating sampling frequency adjustment information;

[0110] If the warning index value of the current HPLC device is greater than the preset second warning threshold, hardware linkage protection information is generated;

[0111] The preset second warning threshold is greater than the preset first warning threshold.

[0112] It should be noted that, for example, if the initial sampling frequency is set to collect data once every 10 seconds, if after optimizing the parameters in the HPLC device, the warning index value corresponding to the data collected by the corresponding HPLC device at the next time node is still greater than the preset first warning threshold, then the sampling frequency of the corresponding HPLC device is adjusted. For example, if the ratio is set to 4 / 5, the revised sampling frequency is 10*4 / 5=8 seconds.

[0113] According to an embodiment of the present invention, after generating the hardware linkage protection information, the method further includes:

[0114] Based on the hardware linkage protection information, the preset management terminal sends an FPGA reset instruction to the HPLC device;

[0115] Based on the FPGA reset instruction, the HPLC device switches the circuit to the backup ADC sampling circuit and starts the redundant communication protocol and device self-test program.

[0116] It should be noted that after the hardware linkage protection information is generated, the management end sends the FPGA (Field-Programmable Gate Array) reconfiguration instruction to the HPLC device, switches to the backup ADC (analog-to-Digital Converter) sampling circuit, and replaces the previous communication protocol by starting the redundant communication protocol.

[0117] According to an embodiment of the present invention, the further embodiment includes:

[0118] When the noise intensity value is greater than a preset first noise intensity threshold, data transmission is interrupted, the collected data is temporarily stored, and the duration of the corresponding noise intensity value is recorded;

[0119] If the duration of the corresponding noise intensity value is greater than the preset time threshold, a noise warning message will be generated.

[0120] It should be noted that if the noise intensity value is greater than the preset noise intensity threshold, it means that the current noise intensity value has a greater impact on data transmission, for example, greater than 105 decibels; therefore, data transmission is interrupted and the collected data is temporarily stored.

[0121] Furthermore, when noise warning information is generated, the current scene position is recorded; with the current scene position as the center point, if there are three scenes within the preset range that generate noise warning information within the set time difference, the corresponding regional-level fault tracing warning information is generated.

[0122] According to an embodiment of the present invention, after generating noise warning information, the method further includes:

[0123] Setting a noise intensity value greater than a preset noise intensity threshold as a first noise intensity value and triggering a redundant node;

[0124] Based on the redundant node, a noise intensity value is obtained and set as a second noise intensity value;

[0125] Comparing and analyzing the first noise intensity value and the second noise intensity value to determine the time difference between the same noise intensity value reaching the preset sensor and the redundant node;

[0126] The three-dimensional coordinates of the corresponding noise source are determined according to the time difference between the same noise intensity value reaching the preset sensor and the redundant node, and the three-dimensional coordinates of the noise source are sent to the preset management terminal for prompting.

[0127] It should be noted that the environment where the HPLC equipment is located is three-dimensionally scanned to construct a three-dimensional coordinate system. Based on the locations of the preset sensors and redundant nodes, the coordinates of the corresponding preset sensors and redundant nodes are determined, and the formula satisfies: ;in is the time difference between the arrival of the same noise intensity value at the preset sensor and the redundant node, Indicates the coordinate point of the preset sensor, Indicates the coordinate points of redundant nodes, The coordinate point of the noise source is represented. The redundant node is provided with a sensor or device capable of collecting noise. The number of the redundant nodes is not less than 3. By combining two different redundant nodes and preset sensors, a group of time delay difference equations is constructed to calculate the three-dimensional coordinates of the noise source.

[0128] Furthermore, if the current noise intensity value is less than or equal to a preset second noise intensity threshold, high-order modulation is used to increase the data transmission rate; if the current noise intensity value is greater than the preset second noise intensity threshold and less than or equal to the preset first noise intensity threshold, low-order modulation is switched to and the preset error correction coding redundancy is activated to enhance anti-interference capability; the preset second noise intensity threshold is less than the preset first noise intensity threshold.

[0129] Furthermore, historical noise data under the same set scenario is obtained, and a noise data model corresponding to the set scenario is constructed based on the historical noise data. The noise intensity value of the next time node is predicted by the noise data model. If the predicted noise intensity value of the next time node is greater than the preset second noise intensity threshold, it switches to low-order modulation in advance and starts the preset error correction coding redundancy to enhance the anti-interference capability.

[0130] Figure 3 The block diagram of the HPLC-based multi-scenario data acquisition system of the power Internet of Things of the present invention is shown.

[0131] like Figure 3 As shown, the second aspect of the present invention provides an HPLC-based electric power Internet of Things multi-scenario data acquisition system 3, comprising a memory 31 and a processor 32. The memory stores an HPLC electric power Internet of Things multi-scenario data acquisition method program. When the HPLC electric power Internet of Things multi-scenario data acquisition method program is executed by the processor, the following steps are implemented:

[0132] Using HPLC technology to collect measured parameters of HPLC equipment in set scenarios, the set scenarios at least include fixed laboratories, GMP workshops, and explosion-proof areas;

[0133] Based on a preset sensor, obtain environmental characteristic values ​​in a set scene, wherein the environmental characteristic values ​​at least include an environmental noise communication signal value and a temperature value;

[0134] Based on the measured parameters, preset theoretical parameters and environmental characteristics, a linear equation system for optimizing parameters of HPLC equipment in different scenarios is constructed;

[0135] Obtain the voltage fluctuation value, temperature gradient change value, and noise intensity value of the HPLC equipment in the current scenario;

[0136] The voltage fluctuation value, temperature gradient change value, and noise intensity value of the HPLC device in the current scenario are input into the optimization parameter linear equation of the corresponding scenario to obtain the optimization parameters of the HPLC device in the current scenario;

[0137] Adjust the HPLC equipment in the current scenario according to the corresponding optimization parameters to improve data acquisition accuracy.

[0138] In this solution, the steps of constructing a set of linear equations for optimizing parameters of HPLC equipment in different scenarios based on measured parameters, preset theoretical parameters and environmental characteristic values ​​specifically include:

[0139] According to the measured parameters and the preset theoretical parameters, the parameter error set and the corresponding measured error index are obtained;

[0140] If the measured error index is greater than the preset error index threshold, the environmental characteristics of the corresponding time node of the set scene are saved to obtain the saved environmental characteristic value;

[0141] Extract the voltage value from the measured parameters at the corresponding time node;

[0142] Perform feature analysis on the saved environmental characteristic values ​​and the voltage values ​​in the measured parameters at the corresponding time nodes to obtain voltage fluctuation values, temperature gradient change values, and noise intensity values;

[0143] The voltage fluctuation value, temperature gradient change value and noise intensity value are divided into multiple sub-intervals and combined to obtain different characteristic intervals;

[0144] Extracting parameter errors from a parameter error set;

[0145] A multivariate linear equation is constructed by combining the parameter error with the voltage fluctuation value, temperature gradient change value, and noise intensity value in the characteristic interval of the corresponding measured parameter to obtain the optimized parameter linear equation for the corresponding characteristic interval;

[0146] After traversing all parameter errors in the parameter error set, the optimized parameter linear equations of the HPLC equipment in different scenarios are obtained.

[0147] In this solution, the step of performing feature analysis on the stored environmental characteristic values ​​and the voltage values ​​in the measured parameters at the corresponding time nodes to obtain the voltage fluctuation value, the temperature gradient change value, and the noise intensity value specifically includes:

[0148] Based on the same time node, the voltage value in the measured parameter is subtracted from the voltage value in the corresponding preset theoretical parameter, and then the absolute value is taken to obtain the voltage fluctuation value at the corresponding time node;

[0149] Extract the time node corresponding to the saved environmental characteristic value and set the temperature gradient change value as , whose formula is ,in Indicates the temperature gradient change value at time node i, represents the temperature value at time node i, Indicates the temperature value at the previous adjacent time node i-1 of time node i;

[0150] The environmental noise communication signal value in the environmental characteristic value is extracted, and the signal and noise in the environmental noise communication signal value are separated based on the preset software to obtain the noise intensity value at different time nodes.

[0151] The HPLC-based electric power Internet of Things multi-scenario data acquisition method and system disclosed in the present invention conducts experiments in different set scenarios to collect measured parameters of HPLC equipment and environmental characteristic values ​​of the set environment, and constructs an optimization parameter linear equation group under different scenarios based on the collected measured parameters, environmental characteristic values ​​and preset theoretical parameters. The parameters of the HPLC equipment in the current environment are then optimized through the optimization parameter linear equation group under different scenarios, thereby improving the accuracy of data collected by the HPLC equipment in the current scenario.

[0152] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0153] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0154] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0155] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware associated with program instructions, and the aforementioned program may be stored in a computer-readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0156] Alternatively, if the integrated units described above are implemented as software modules and sold or used as standalone products, they can also be stored on a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product, stored on a storage medium, includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute all or part of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as removable storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. The multi-scenario data acquisition method of the power Internet of Things based on HPLC is characterized by: include: Deploy HPLC equipment in a set scenario and collect measured data during the operation of the HPLC equipment through the power Internet of Things. The set scenario includes at least a fixed laboratory, a GMP workshop, and an explosion-proof area. Based on a preset sensor, obtain environmental characteristic values ​​in a set scene, wherein the environmental characteristic values ​​at least include an environmental noise communication signal value and a temperature value; Based on the measured parameters, preset theoretical parameters and environmental characteristics, a linear equation system for optimizing parameters of HPLC equipment in different scenarios is constructed; Obtain the voltage fluctuation value, temperature gradient change value, and noise intensity value of the HPLC equipment in the current scenario; The voltage fluctuation value, temperature gradient change value, and noise intensity value of the HPLC device in the current scenario are input into the optimization parameter linear equation of the corresponding scenario to obtain the optimization parameters of the HPLC device in the current scenario; Adjust the HPLC equipment in the current scenario according to the corresponding optimization parameters to improve data acquisition accuracy; The steps of constructing a set of linear equations for optimizing parameters of HPLC equipment in different scenarios based on the measured parameters, preset theoretical parameters and environmental characteristic values ​​specifically include; According to the measured parameters and the preset theoretical parameters, the parameter error set and the corresponding measured error index are obtained; If the measured error index is greater than the preset error index threshold, the environmental characteristics of the corresponding time node of the set scene are saved to obtain the saved environmental characteristic value; Extract the voltage value from the measured parameters at the corresponding time node; Perform feature analysis on the saved environmental characteristic values ​​and the voltage values ​​in the measured parameters at the corresponding time nodes to obtain voltage fluctuation values, temperature gradient change values, and noise intensity values; The voltage fluctuation value, temperature gradient change value and noise intensity value are divided into multiple sub-intervals and combined to obtain different characteristic intervals; Extracting parameter errors from a parameter error set; A multivariate linear equation is constructed by combining the parameter error with the voltage fluctuation value, temperature gradient change value, and noise intensity value in the characteristic interval of the corresponding measured parameter to obtain the optimized parameter linear equation for the corresponding characteristic interval; After traversing all parameter errors in the parameter error set, the optimized parameter linear equations of the HPLC equipment in different scenarios are obtained.

2. The HPLC-based multi-scenario data acquisition method for the electric power Internet of Things according to claim 1 is characterized in that: The step of performing feature analysis on the stored environmental characteristic values ​​and the voltage values ​​in the measured parameters at the corresponding time nodes to obtain the voltage fluctuation value, the temperature gradient change value, and the noise intensity value specifically includes: Based on the same time node, the voltage value in the measured parameter is subtracted from the voltage value in the corresponding preset theoretical parameter, and then the absolute value is taken to obtain the voltage fluctuation value at the corresponding time node; Extract the time node corresponding to the saved environmental characteristic value and set the temperature gradient change value as , whose formula is ,in Indicates the temperature gradient change value at time node i, represents the temperature value at time node i, Indicates the temperature value at the previous adjacent time node i-1 of time node i; The environmental noise communication signal value in the environmental characteristic value is extracted, and the signal and noise in the environmental noise communication signal value are separated based on the preset software to obtain the noise intensity value at different time nodes.

3. The HPLC-based multi-scenario data acquisition method for electric power Internet of Things according to claim 1, characterized in that: After constructing the multivariate linear equation, the method further includes: With parameter error as dependent variable, voltage fluctuation value, temperature gradient change value and noise intensity value as independent variables, a multidimensional space is constructed to obtain the coordinates of each point: ;in represents the parameter error of parameter n at time node i, represents the voltage fluctuation value of the corresponding parameter at time node i, Represents the noise intensity value of the corresponding parameter at time node i; Taking the characteristic interval as the benchmark, extract the distance between the points in the corresponding interval and the corresponding multivariate linear equation to obtain the distance set; Calculate the mean of the distances in the distance set to obtain the average distance value from the point to the multivariate linear equation; If the average distance value from the point to the multivariate linear equation is greater than the preset distance threshold, the corresponding feature interval will be evenly split into two feature sub-intervals, and the multivariate linear equation within the feature sub-interval will be reconstructed. If the average distance value from the point to the multivariate linear equation corresponding to the feature sub-interval is still greater than the preset distance threshold, the feature sub-interval will continue to be split until the average distance value from the corresponding point to the multivariate linear equation is less than or equal to the preset distance threshold.

4. The HPLC-based multi-scenario data acquisition method for electric power Internet of Things according to claim 1, characterized in that: The voltage fluctuation value, temperature gradient change value, and noise intensity value of the HPLC device in the current scenario are obtained as follows: When the time node is zero, the voltage fluctuation value, temperature gradient change value, and noise intensity value of the HPLC device in the current scenario are the average values ​​of the historical voltage fluctuation value, temperature gradient change value, and noise intensity value of the HPLC device in the corresponding scenario respectively; When the time node is 1, the voltage fluctuation value of the HPLC device in the current scenario is the average value of the historical voltage fluctuation values ​​of the HPLC device in the corresponding scenario; the temperature gradient change value is the absolute value of the temperature value at the current time node minus the initial temperature value of the corresponding HPLC device; the noise intensity value is the noise intensity value at the corresponding time node; When the time node is greater than 1, the voltage fluctuation value of the HPLC equipment in the current scenario ,in ,m is the number of time nodes, Indicates the voltage value of the HPLC device at time node j in the current scenario, Indicates the average voltage of the HPLC device in the current scenario; the temperature gradient change value is the absolute value of the temperature value at the current time node minus the temperature value at the corresponding previous time node; the noise intensity value is the noise intensity value at the corresponding time node.

5. The HPLC-based multi-scenario data acquisition method for electric power Internet of Things according to claim 1, characterized in that: After adjusting the HPLC equipment in the current scenario according to the corresponding optimization parameters, the method further includes: Obtain the voltage fluctuation value and temperature gradient change value of the HPLC equipment at the next time node in the current scenario; Multiply the voltage fluctuation value at the next time node by the corresponding weight coefficient to obtain the voltage warning index value; multiply the temperature gradient change value at the next time node by the corresponding weight coefficient to obtain the temperature warning index value; The voltage warning index value and the temperature warning index value are accumulated to obtain the warning index value of the current HPLC device; If the warning index value of the current HPLC device is greater than a preset first warning threshold, then generating sampling frequency adjustment information; If the warning index value of the current HPLC device is greater than the preset second warning threshold, hardware linkage protection information is generated; The preset second warning threshold is greater than the preset first warning threshold.

6. The HPLC-based multi-scenario data acquisition method for the electric power Internet of Things according to claim 5 is characterized in that: After the hardware linkage protection information is generated, the method further includes: Based on the hardware linkage protection information, the preset management terminal sends an FPGA reset instruction to the HPLC device; Based on the FPGA reset instruction, the HPLC device switches the circuit to the backup ADC sampling circuit and starts the redundant communication protocol and device self-test program.

7. The HPLC-based power Internet of Things multi-scenario data acquisition system is characterized by: The system includes a memory and a processor, wherein the memory stores a HPLC program for collecting data in multiple scenarios of the electric power Internet of Things. When the HPLC program for collecting data in multiple scenarios of the electric power Internet of Things is executed by the processor, the following steps are implemented: Using HPLC technology to collect measured parameters of HPLC equipment in set scenarios, the set scenarios at least include fixed laboratories, GMP workshops, and explosion-proof areas; Based on a preset sensor, obtain environmental characteristic values ​​in a set scene, wherein the environmental characteristic values ​​at least include an environmental noise communication signal value and a temperature value; Based on the measured parameters, preset theoretical parameters and environmental characteristics, a linear equation system for optimizing parameters of HPLC equipment in different scenarios is constructed; Obtain the voltage fluctuation value, temperature gradient change value, and noise intensity value of the HPLC equipment in the current scenario; The voltage fluctuation value, temperature gradient change value, and noise intensity value of the HPLC device in the current scenario are input into the optimization parameter linear equation of the corresponding scenario to obtain the optimization parameters of the HPLC device in the current scenario; Adjust the HPLC equipment in the current scenario according to the corresponding optimization parameters to improve data acquisition accuracy; The steps of constructing a set of linear equations for optimizing parameters of HPLC equipment in different scenarios based on the measured parameters, preset theoretical parameters and environmental characteristic values ​​specifically include; According to the measured parameters and the preset theoretical parameters, the parameter error set and the corresponding measured error index are obtained; If the measured error index is greater than the preset error index threshold, the environmental characteristics of the corresponding time node of the set scene are saved to obtain the saved environmental characteristic value; Extract the voltage value from the measured parameters at the corresponding time node; Perform feature analysis on the saved environmental characteristic values ​​and the voltage values ​​in the measured parameters at the corresponding time nodes to obtain voltage fluctuation values, temperature gradient change values, and noise intensity values; The voltage fluctuation value, temperature gradient change value and noise intensity value are divided into multiple sub-intervals and combined to obtain different characteristic intervals; Extracting parameter errors from a parameter error set; A multivariate linear equation is constructed by combining the parameter error with the voltage fluctuation value, temperature gradient change value, and noise intensity value in the characteristic interval of the corresponding measured parameter to obtain the optimized parameter linear equation for the corresponding characteristic interval; After traversing all parameter errors in the parameter error set, the optimized parameter linear equations of the HPLC equipment in different scenarios are obtained.

8. The HPLC-based electric power Internet of Things multi-scenario data acquisition system according to claim 7 is characterized in that: The step of performing feature analysis on the stored environmental characteristic values ​​and the voltage values ​​in the measured parameters at the corresponding time nodes to obtain the voltage fluctuation value, the temperature gradient change value, and the noise intensity value specifically includes: Based on the same time node, the voltage value in the measured parameter is subtracted from the voltage value in the corresponding preset theoretical parameter, and then the absolute value is taken to obtain the voltage fluctuation value at the corresponding time node; Extract the time node corresponding to the saved environmental characteristic value and set the temperature gradient change value as , whose formula is ,in Indicates the temperature gradient change value at time node i, represents the temperature value at time node i, Indicates the temperature value at the previous adjacent time node i-1 of time node i; The environmental noise communication signal value in the environmental characteristic value is extracted, and the signal and noise in the environmental noise communication signal value are separated based on the preset software to obtain the noise intensity value at different time nodes.

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