CN117114205B - Energy-saving prediction model and method for digital energy blasting station - Google Patents
Energy-saving prediction model and method for digital energy blasting station Download PDFInfo
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Abstract
The invention discloses an energy-saving prediction model of a digital energy blasting station and a method thereof, comprising an energy monitoring module, a temperature control module and a control module, wherein the energy monitoring module is used for acquiring energy of temperature control equipment capable of generating corresponding energy from an energy absorption end, and an energy release end is used for releasing the energy to the temperature control equipment needing the corresponding energy for data analysis to obtain an energy monitoring coefficient; the energy-saving management platform compares the energy monitoring coefficient XJ with an energy monitoring coefficient threshold; the prediction module predicts the energy use condition of the energy blasting station when the energy-saving poor signal of the energy-saving management platform is obtained, so as to obtain an energy maintenance difference value; the energy-saving management platform acquires the energy maintenance difference value of the prediction module and compares the energy maintenance difference value with the energy maintenance difference threshold value.
Description
Technical Field
The invention relates to the technical field of energy blasting stations, in particular to an energy-saving prediction model and method of a digital energy blasting station.
Background
Chinese patent CN116384843a discloses an energy efficiency evaluation model training method and a monitoring method for a digital energy nitrogen station, which belong to the field of energy management and comprise: acquiring variable data of a nitrogen station recorded in a fixed time period to form time series data serving as a basis for establishing a model; preprocessing and cleaning data of a nitrogen station; checking the stationarity of the time series data; inputting the time series data into the ARIMA model, and checking and eliminating the non-stationarity of the time series data; and (5) establishing a nitrogen station energy utilization efficiency model with a fixed time period by using an ARIMA model to predict. The change rule of the nitrogen station is predicted by processing variable data of the nitrogen station in a fixed time period and establishing a time sequence model, the ARIMA model is selected as an energy efficiency evaluation model of the digital energy nitrogen station, the periodic change rule of the nitrogen station is effectively reflected, and the monitoring precision of the model is improved by fitting and fitting inspection of the model;
in the prior art, the energy blasting station cannot analyze and judge the energy supply when the energy supply is unstable, so that the energy supply is possibly only needed to be overhauled in a standby mode, and workers can halt and overhaul the energy supply, so that the energy consumed by starting and stopping the energy supply is poor in energy-saving effect.
Disclosure of Invention
The invention aims to solve the problems of the background technology and provides an energy-saving prediction model of a digital energy blowing station and a method thereof.
The aim of the invention can be achieved by the following technical scheme:
an energy efficient predictive model for a digital energy blast station, comprising:
the energy monitoring module is used for acquiring energy of the energy absorption end for absorbing temperature regulating equipment capable of generating corresponding energy and marking the energy as an energy absorption value ZXn; the energy release end is used for releasing energy to temperature regulating equipment requiring corresponding energy and is marked as an energy release value ZSn; data analysis is carried out on the energy absorption value ZXn and the energy release value ZSn to obtain an energy monitoring coefficient XJ;
the energy-saving management platform acquires an energy monitoring coefficient XJ of the energy monitoring module and compares the energy monitoring coefficient XJ with an energy monitoring coefficient threshold;
the prediction module predicts the energy use condition of the energy blasting station when the energy-saving poor signal of the energy-saving management platform is obtained, so as to obtain an energy maintenance difference value;
and the energy-saving management platform acquires the energy maintenance difference value of the prediction module and compares the energy maintenance difference value with an energy maintenance difference threshold value.
As a further scheme of the invention: the specific working process of the energy monitoring module is as follows:
setting an acquisition time node as T, wherein the acquisition time node T comprises an acquisition initial time Tc, an acquisition midpoint time Tz and an acquisition end time Tj;
acquiring an initial energy absorption value ZXnTc of an acquisition initial time Tc, a midpoint energy absorption value ZXnTz of an acquisition midpoint time Tz and an energy absorption value ZXnTj of an acquisition end time Tj;
acquiring an energy absorption maximum value ZXnmax and an energy absorption minimum value ZXnmin in an acquisition time node, and acquiring time Tmax corresponding to the energy absorption maximum value ZXnmax and acquiring time Tmin corresponding to the energy absorption minimum value ZXnmin;
the energy absorption average value ZXJ is calculated by the formula ZXJ= (ZXnTc+ZXnTz+ZXnTj)/T;
the energy absorption fluctuation value ZXB is calculated by the formula ZXB= (ZXnmax-ZXnmin)/(Tmax-Tmin).
As a further scheme of the invention: setting an acquisition time node as T, wherein the acquisition time node T comprises an acquisition initial time Tc, an acquisition midpoint time Tz and an acquisition end time Tj;
acquiring an initial energy release value ZSNTc of an acquisition initial time Tc, a midpoint energy release value ZSNTz of an acquisition midpoint time Tz and an energy release value ZSNTj of an acquisition end time Tj;
acquiring an energy release maximum value ZSnmas and an energy release minimum value ZSnmin in an acquisition time node, and acquiring time TmaS corresponding to the energy release maximum value ZSnmas and acquiring time Tmin corresponding to the energy release minimum value ZSnmin;
calculating to obtain an energy release average value ZSJ through a formula ZSJ= (ZSnTc+ZSnTz+ZSnTj)/T;
the energy release fluctuation value ZSB is calculated by the formula zsb= (ZSnmaS-ZSnmin)/(TmaS-Tmin).
As a further scheme of the invention: substituting the obtained energy absorption average value ZXJ, energy absorption fluctuation value ZXB, energy release average value ZSJ and energy release fluctuation value ZSB into a formulaCalculating an energy monitoring coefficient XJ; wherein a1 and a2 are proportionality coefficients.
As a further scheme of the invention: if the energy monitoring coefficient XJ is larger than or equal to the energy monitoring coefficient threshold XJy, generating an energy-saving poor signal;
if the energy monitoring coefficient XJ is smaller than the energy monitoring coefficient threshold XJy, an energy saving excellent signal is generated.
As a further scheme of the invention: the specific working process of the prediction module is as follows:
obtaining an energy monitoring coefficient XJ and an energy monitoring coefficient threshold XJy, and performing difference calculation to obtain an energy monitoring coefficient difference CXJ;
the energy value stored currently by the energy storage end is obtained and marked as ZCN; calculating to obtain an energy consumption limit value TX through a formula TX= (ZCN/CXJ) T; and the acquisition time node set by the energy monitoring module is T.
As a further scheme of the invention: acquiring maintenance data of an energy blasting station; the maintenance data of the energy blasting station comprises the maintenance frequency, the maintenance times and the maintenance effective values of the energy blasting station, and are respectively marked as Pw, sw and Yw;
calculating to obtain a maintenance duration value ZW of the energy blasting station through a formula ZW= (b1+b2+Yw)/(b3×Sw); wherein b1, b2 and b3 are all proportionality coefficients;
and calculating the difference value between the obtained energy consumption limit value TX and the maintenance duration value ZW of the energy blasting station to obtain an energy maintenance difference value.
As a further scheme of the invention: if the energy maintenance difference value is more than or equal to the energy maintenance difference threshold value, generating an energy blasting station standby maintenance signal;
and if the energy maintenance difference value is less than the energy maintenance difference threshold value, generating an energy blasting station shutdown maintenance signal.
As a further scheme of the invention: the method comprises the following steps:
step 1: the energy absorption end is used for absorbing the energy of the temperature regulating equipment capable of generating corresponding energy and is marked as an energy absorption value ZXn; the energy release end is used for releasing energy to temperature regulating equipment requiring corresponding energy and is marked as an energy release value ZSn; data analysis is carried out on the energy absorption value ZXn and the energy release value ZSn to obtain an energy monitoring coefficient XJ;
step 2: acquiring an energy monitoring coefficient XJ of an energy monitoring module, and comparing the energy monitoring coefficient XJ with an energy monitoring coefficient threshold value to generate an energy-saving poor signal or an energy-saving excellent signal;
step 3: when an energy-saving performance difference signal of the energy-saving management platform is obtained, predicting the energy use condition of the energy blasting station to obtain an energy maintenance difference value;
step 4: and obtaining an energy maintenance difference value of the prediction module, and comparing the energy maintenance difference value with an energy maintenance difference threshold value to generate an energy blast station standby maintenance signal or an energy blast station stop maintenance signal.
The invention has the beneficial effects that:
according to the invention, the energy absorption end is used for absorbing the energy of the temperature regulating equipment capable of generating corresponding energy, and the energy release end is used for releasing the energy to the temperature regulating equipment requiring the corresponding energy for data analysis, so that the energy monitoring coefficient XJ is obtained; comparing the energy monitoring coefficient XJ with an energy monitoring coefficient threshold;
and on the basis, the demand relation between the energy storage end and the energy source blasting station is predicted, so that standby maintenance or shutdown maintenance is arranged, the shutdown condition is reduced as much as possible under the condition that the energy source is unstable to be conveyed, the energy consumed by starting and stopping is avoided, and the digital energy source blasting station is more energy-saving when in fault maintenance.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a system block diagram of the present invention.
Description of the embodiments
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, the present invention is an energy-saving prediction model of a digital energy blower station and a method thereof, comprising:
the energy monitoring module is used for acquiring energy of the energy absorption end for absorbing temperature regulating equipment capable of generating corresponding energy and marking the energy as an energy absorption value ZXn; the energy release end is used for releasing energy to temperature regulating equipment requiring corresponding energy and is marked as an energy release value ZSn; data analysis is carried out on the energy absorption value ZXn and the energy release value ZSn to obtain an energy monitoring coefficient XJ;
the specific working process of the energy monitoring module is as follows:
step 1: setting an acquisition time node as T, wherein the acquisition time node T comprises an acquisition initial time Tc, an acquisition midpoint time Tz and an acquisition end time Tj;
acquiring an initial energy absorption value ZXnTc of an acquisition initial time Tc, a midpoint energy absorption value ZXnTz of an acquisition midpoint time Tz and an energy absorption value ZXnTj of an acquisition end time Tj;
acquiring an energy absorption maximum value ZXnmax and an energy absorption minimum value ZXnmin in an acquisition time node, and acquiring time Tmax corresponding to the energy absorption maximum value ZXnmax and acquiring time Tmin corresponding to the energy absorption minimum value ZXnmin;
the energy absorption average value ZXJ is calculated by the formula ZXJ= (ZXnTc+ZXnTz+ZXnTj)/T;
calculating to obtain an energy absorption fluctuation value ZXB through a formula ZXB= (ZXnmax-ZXnmin)/(Tmax-Tmin);
step 2: setting an acquisition time node as T, wherein the acquisition time node T comprises an acquisition initial time Tc, an acquisition midpoint time Tz and an acquisition end time Tj;
acquiring an initial energy release value ZSNTc of an acquisition initial time Tc, a midpoint energy release value ZSNTz of an acquisition midpoint time Tz and an energy release value ZSNTj of an acquisition end time Tj;
acquiring an energy release maximum value ZSnmas and an energy release minimum value ZSnmin in an acquisition time node, and acquiring time TmaS corresponding to the energy release maximum value ZSnmas and acquiring time Tmin corresponding to the energy release minimum value ZSnmin;
calculating to obtain an energy release average value ZSJ through a formula ZSJ= (ZSnTc+ZSnTz+ZSnTj)/T;
calculating to obtain an energy release fluctuation value ZSB according to a formula ZSB= (ZSnmas-ZSnmin)/(TmaS-Tmin);
step 3: substituting the obtained energy absorption average value ZXJ, energy absorption fluctuation value ZXB, energy release average value ZSJ and energy release fluctuation value ZSB into a formulaCalculating an energy monitoring coefficient XJ; wherein, a1 and a2 are proportionality coefficients, the value of a1 is 0.59, and the value of a2 is 0.54;
the energy-saving management platform acquires an energy monitoring coefficient XJ of the energy monitoring module and compares the energy monitoring coefficient XJ with an energy monitoring coefficient threshold;
the specific working process of the energy-saving management platform is as follows:
comparing the obtained energy monitoring coefficient XJ with an energy monitoring coefficient threshold XJy;
if the energy monitoring coefficient XJ is larger than or equal to the energy monitoring coefficient threshold XJy, generating an energy-saving poor signal;
if the energy monitoring coefficient XJ is smaller than the energy monitoring coefficient threshold XJy, generating an energy-saving excellent signal;
the energy-saving poor signal indicates that the current digital energy blasting station is unqualified in the expression state of the conveying condition when in operation, particularly the problem that the absorption end and the release end are possibly unmatched, and the energy-saving good signal indicates that the current digital energy blasting station is qualified in the expression state of the conveying condition when in operation, particularly the problem that the absorption end and the release end are not unmatched;
the prediction module predicts the energy use condition of the energy blasting station when the energy-saving poor signal of the energy-saving management platform is obtained, so as to obtain an energy maintenance difference value;
the specific working process of the prediction module is as follows:
step 1: obtaining an energy monitoring coefficient XJ and an energy monitoring coefficient threshold XJy, and performing difference calculation to obtain an energy monitoring coefficient difference CXJ;
the energy value stored currently by the energy storage end is obtained and marked as ZCN; calculating to obtain an energy consumption limit value TX through a formula TX= (ZCN/CXJ) T; wherein, T is the acquisition time node set by the energy monitoring module and is T;
step 2: acquiring maintenance data of an energy blasting station; the maintenance data of the energy blasting station comprises the maintenance frequency, the maintenance times and the maintenance effective values of the energy blasting station, and are respectively marked as Pw, sw and Yw;
the maintenance frequency Pw is represented as an interval of time used for each maintenance, the maintenance frequency Sw is represented as a sum of times of all maintenance in the historical time, the maintenance effective value Yw is represented as a sum of all maintenance effective times, and the maintenance effective time is represented as a maintenance time smaller than a maintenance time threshold in the historical time;
calculating to obtain a maintenance duration value ZW of the energy blasting station through a formula ZW= (b1+b2+Yw)/(b3×Sw); wherein b1, b2 and b3 are all proportionality coefficients, b1 takes on a value of 0.63, b2 takes on a value of 0.68 and b3 takes on a value of 0.42;
step 3: performing difference calculation on the obtained energy consumption limit value TX and the maintenance duration value ZW of the energy blasting station to obtain an energy maintenance difference value;
the energy-saving management platform acquires the energy maintenance difference value of the prediction module and compares the energy maintenance difference value with an energy maintenance difference threshold value;
the specific working process of the energy-saving management platform is as follows:
if the energy maintenance difference value is more than or equal to the energy maintenance difference threshold value, generating an energy blasting station standby maintenance signal;
if the energy maintenance difference value is less than the energy maintenance difference threshold value, generating an energy blasting station shutdown maintenance signal;
the standby maintenance signal of the energy blasting station indicates that the time spent on energy consumption of the energy blasting station currently meets the time spent on maintenance of the energy blasting station, and the energy blasting station can be checked while working;
the energy blowing station downtime overhaul signal indicates that the time spent by the energy consumption of the energy blowing station currently does not satisfy the time spent by the maintenance of the energy blowing station, and the inspection can not be performed while working.
Example 2
Based on the above embodiment 1, the present invention is an energy-saving prediction method for a digital energy blower station, comprising the steps of:
step 1: the energy absorption end is used for absorbing the energy of the temperature regulating equipment capable of generating corresponding energy and is marked as an energy absorption value ZXn; the energy release end is used for releasing energy to temperature regulating equipment requiring corresponding energy and is marked as an energy release value ZSn; data analysis is carried out on the energy absorption value ZXn and the energy release value ZSn to obtain an energy monitoring coefficient XJ;
step 2: acquiring an energy monitoring coefficient XJ of an energy monitoring module, and comparing the energy monitoring coefficient XJ with an energy monitoring coefficient threshold value to generate an energy-saving poor signal or an energy-saving excellent signal;
step 3: when an energy-saving performance difference signal of the energy-saving management platform is obtained, predicting the energy use condition of the energy blasting station to obtain an energy maintenance difference value;
step 4: and obtaining an energy maintenance difference value of the prediction module, and comparing the energy maintenance difference value with an energy maintenance difference threshold value to generate an energy blast station standby maintenance signal or an energy blast station stop maintenance signal.
The working principle of the invention is as follows: the energy monitoring coefficient XJ is obtained by acquiring the energy of the temperature regulating equipment which can generate corresponding energy from the energy absorbing end and the energy releasing end which is used for releasing the energy to the temperature regulating equipment which needs the corresponding energy for data analysis; comparing the energy monitoring coefficient XJ with an energy monitoring coefficient threshold;
and on the basis, the demand relation between the energy storage end and the energy source blasting station is predicted, so that standby maintenance or shutdown maintenance is arranged, the shutdown condition is reduced as much as possible under the condition that the energy source is unstable to be conveyed, the energy consumed by starting and stopping is avoided, and the digital energy source blasting station is more energy-saving when in fault maintenance.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (6)
1. An energy conservation prediction model for a digital energy blowing station, comprising:
the energy monitoring module is used for acquiring energy of the energy absorption end for absorbing temperature regulating equipment capable of generating corresponding energy and marking the energy as an energy absorption value ZXn; the energy release end is used for releasing energy to temperature regulating equipment requiring corresponding energy and is marked as an energy release value ZSn; data analysis is carried out on the energy absorption value ZXn and the energy release value ZSn to obtain an energy monitoring coefficient XJ;
the energy-saving management platform acquires an energy monitoring coefficient XJ of the energy monitoring module and compares the energy monitoring coefficient XJ with an energy monitoring coefficient threshold;
the prediction module predicts the energy use condition of the energy blasting station when the energy-saving poor signal of the energy-saving management platform is obtained, so as to obtain an energy maintenance difference value;
the energy-saving management platform acquires the energy maintenance difference value of the prediction module and compares the energy maintenance difference value with an energy maintenance difference threshold value;
the specific working process of the prediction module is as follows:
obtaining an energy monitoring coefficient XJ and an energy monitoring coefficient threshold XJy, and performing difference calculation to obtain an energy monitoring coefficient difference CXJ;
the energy value stored currently by the energy storage end is obtained and marked as ZCN; calculating to obtain an energy consumption limit value TX through a formula TX= (ZCN/CXJ) T; wherein T is an acquisition time node set by the energy monitoring module;
acquiring maintenance data of an energy blasting station; the maintenance data of the energy blasting station comprises the maintenance frequency, the maintenance times and the maintenance effective values of the energy blasting station, and are respectively marked as Pw, sw and Yw;
calculating to obtain a maintenance duration value ZW of the energy blasting station through a formula ZW= (b1+b2+Yw)/(b3×Sw); wherein b1, b2 and b3 are all proportionality coefficients;
performing difference calculation on the obtained energy consumption limit value TX and the maintenance duration value ZW of the energy blasting station to obtain an energy maintenance difference value;
if the energy maintenance difference value is more than or equal to the energy maintenance difference threshold value, generating an energy blasting station standby maintenance signal;
and if the energy maintenance difference value is less than the energy maintenance difference threshold value, generating an energy blasting station shutdown maintenance signal.
2. The energy conservation prediction model of the digital energy blowing station according to claim 1, wherein the energy monitoring module specifically works as follows:
setting an acquisition time node as T, wherein the acquisition time node T comprises an acquisition initial time Tc, an acquisition midpoint time Tz and an acquisition end time Tj;
acquiring an initial energy absorption value ZXnTc of an acquisition initial time Tc, a midpoint energy absorption value ZXnTz of an acquisition midpoint time Tz and an energy absorption value ZXnTj of an acquisition end time Tj;
acquiring an energy absorption maximum value ZXnmax and an energy absorption minimum value ZXnmin in an acquisition time node, and acquiring time Tmax corresponding to the energy absorption maximum value ZXnmax and acquiring time Tmin corresponding to the energy absorption minimum value ZXnmin;
the energy absorption average value ZXJ is calculated by the formula ZXJ= (ZXnTc+ZXnTz+ZXnTj)/T;
the energy absorption fluctuation value ZXB is calculated by the formula ZXB= (ZXnmax-ZXnmin)/(Tmax-Tmin).
3. The energy-saving prediction model of a digital energy blowing station according to claim 2, wherein the acquisition time node is set as T, and the acquisition time node T comprises an acquisition initial time Tc, an acquisition midpoint time Tz and an acquisition end time Tj;
acquiring an initial energy release value ZSNTc of an acquisition initial time Tc, a midpoint energy release value ZSNTz of an acquisition midpoint time Tz and an energy release value ZSNTj of an acquisition end time Tj;
acquiring an energy release maximum value ZSnmas and an energy release minimum value ZSnmin in an acquisition time node, and acquiring time TmaS corresponding to the energy release maximum value ZSnmas and acquiring time Tmin corresponding to the energy release minimum value ZSnmin;
calculating to obtain an energy release average value ZSJ through a formula ZSJ= (ZSnTc+ZSnTz+ZSnTj)/T;
the energy release fluctuation value ZSB is calculated by the formula zsb= (ZSnmaS-ZSnmin)/(TmaS-Tmin).
4. A model for energy conservation prediction of a digital energy blowing station according to claim 3, wherein the obtained energy absorption average value ZXJ, energy absorption fluctuation value ZXB, energy release average value ZSJ and energy release fluctuation value ZSB are substituted into the formulaCalculating an energy monitoring coefficient XJ; wherein a1 and a2 are proportionality coefficients.
5. The model for predicting energy conservation in a digital energy blowing station according to claim 4, wherein the energy conservation poor signal is generated if the energy monitoring coefficient XJ is equal to or greater than the energy monitoring coefficient threshold XJy;
if the energy monitoring coefficient XJ is smaller than the energy monitoring coefficient threshold XJy, an energy saving excellent signal is generated.
6. The energy-saving prediction method of the digital energy blasting station is characterized by comprising the following steps of:
step 1: the energy absorption end is used for absorbing the energy of the temperature regulating equipment capable of generating corresponding energy and is marked as an energy absorption value ZXn; the energy release end is used for releasing energy to temperature regulating equipment requiring corresponding energy and is marked as an energy release value ZSn; data analysis is carried out on the energy absorption value ZXn and the energy release value ZSn to obtain an energy monitoring coefficient XJ;
step 2: acquiring an energy monitoring coefficient XJ, and comparing the energy monitoring coefficient XJ with an energy monitoring coefficient threshold value to generate an energy-saving poor signal or an energy-saving excellent signal;
step 3: when an energy-saving performance difference signal of the energy-saving management platform is obtained, predicting the energy use condition of the energy blasting station to obtain an energy maintenance difference value;
the specific working process is predicted as follows:
obtaining an energy monitoring coefficient XJ and an energy monitoring coefficient threshold XJy, and performing difference calculation to obtain an energy monitoring coefficient difference CXJ;
the energy value stored currently by the energy storage end is obtained and marked as ZCN; calculating to obtain an energy consumption limit value TX through a formula TX= (ZCN/CXJ) T; wherein T is a set acquisition time node;
acquiring maintenance data of an energy blasting station; the maintenance data of the energy blasting station comprises the maintenance frequency, the maintenance times and the maintenance effective values of the energy blasting station, and are respectively marked as Pw, sw and Yw;
calculating to obtain a maintenance duration value ZW of the energy blasting station through a formula ZW= (b1+b2+Yw)/(b3×Sw); wherein b1, b2 and b3 are all proportionality coefficients;
performing difference calculation on the obtained energy consumption limit value TX and the maintenance duration value ZW of the energy blasting station to obtain an energy maintenance difference value;
if the energy maintenance difference value is more than or equal to the energy maintenance difference threshold value, generating an energy blasting station standby maintenance signal;
if the energy maintenance difference value is less than the energy maintenance difference threshold value, generating an energy blasting station shutdown maintenance signal;
step 4: and obtaining an energy maintenance difference value, and comparing the energy maintenance difference value with an energy maintenance difference threshold value to generate an energy blasting station standby maintenance signal or an energy blasting station shutdown maintenance signal.
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