CN115513950B - Data intelligent monitoring system based on safe operating system - Google Patents

Data intelligent monitoring system based on safe operating system Download PDF

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CN115513950B
CN115513950B CN202211459666.4A CN202211459666A CN115513950B CN 115513950 B CN115513950 B CN 115513950B CN 202211459666 A CN202211459666 A CN 202211459666A CN 115513950 B CN115513950 B CN 115513950B
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power supply
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CN115513950A (en
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尹家亮
张友卫
徐尧尧
陈耀峰
常嘉民
徐婷婷
薛孝婷
胡霞
吴光明
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Jiangsu Desai Technology Co.,Ltd.
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Nanjing Dessel Information Technology Co ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract

The invention relates to the technical field of data monitoring, in particular to a data intelligent monitoring system based on a safe operating system, which comprises: and the flue gas treatment result prediction module respectively calculates the total sulfur content corresponding to each power generation scheme obtained by the power generation scheme generation module according to the relationship between the sulfur content in the tail flue gas and the power generation power in the power generation equipment, and selects the power generation scheme with the minimum total sulfur content as the optimal power generation scheme. The method not only monitors the sulfur content in the flue gas discharged by the power plant, but also considers the condition that the sulfur content of the flue gas generated by the power generation equipment at different power generation powers is different after the flue gas is processed by the flue gas processing equipment, generates different power generation schemes according to the predicted power consumption demand of power supply users, screens the power generation schemes by considering the total sulfur content of the tail flue gas corresponding to the different power generation schemes, manages and controls the power generation power of the power generation equipment according to the final screening result, and realizes effective management of the power generation equipment.

Description

Data intelligent monitoring system based on safe operating system
Technical Field
The invention relates to the technical field of data monitoring, in particular to an intelligent data monitoring system based on a safe operating system.
Background
Thermal power plants are plants that produce electrical energy using combustible materials (e.g., coal) as fuel; thermal power plant can produce more flue gas at the power generation in-process, and the sulphide in the flue gas can cause great influence to environment and human body, consequently, need the content of strict control sulphide in the flue gas of power plant emission, often adopt flue gas treatment equipment among the prior art to produce the flue gas to the power plant and handle, but still can have partial sulphide in the afterbody flue gas after handling.
Current data intelligent monitoring system based on safe operating system, it is simple to monitor the sulphur content in the power plant's emission flue gas through the sensor only, through judging the sulphur content in the emission flue gas and comparing with standard index, and then judge whether the sulphur content in the emission flue gas is unusual, and report to the police to the abnormal conditions, prior art has great defect, do not consider the different circumstances of sulphur content after the flue gas that power generation facility produced at different generating power handles through flue gas treatment facility, and then can't manage and control power generation facility according to power user's power consumption demand.
Disclosure of Invention
The invention aims to provide a data intelligent monitoring system based on a safety operating system, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an intelligent data monitoring system based on a secure operating system, the system comprising: an operation data acquisition module, a flue gas treatment analysis module, an electricity demand analysis module, an electricity deviation analysis module, a power generation scheme generation module, a flue gas treatment result prediction module and a power generation equipment management module,
the operation data acquisition module acquires the power generation power of different power generation equipment corresponding to each time point in the historical data, numbers the different power generation equipment, and defaults that the specifications of the power generation equipment are the same;
the flue gas processing and analyzing module operates under the condition that each power generation device in the historical data keeps unchanged in power generation power PThe sulfur content in the tail flue gas of the flue gas generated in the first unit time t2 and treated by the flue gas treatment equipment is recorded as A P Analyzing the relation between the sulfur content in the tail flue gas and the power generation power in the power generation equipment, wherein the first unit time t2 is a preset constant in a database;
the power consumption demand analysis module acquires the change rate of the number of power supply users corresponding to the power supply quantity of the power plant in each time interval in historical data and the power consumption of each power supply user, analyzes the relation between the change rate of the number of the power supply users and the time interval to obtain a first relation result, and then predicts the power generation demand quantity of the power plant in the next time interval based on the time interval to which the current time belongs according to the first relation result, wherein the time length corresponding to each time interval is equal to a second unit time length t3, and the second unit time length t3 is a preset constant in a database;
the power consumption deviation analysis module is used for predicting a power generation demand deviation value of a power plant in the next time interval based on the time interval to which the current time belongs according to a power generation demand predicted value of the power plant in the next time interval based on the time interval to which the current time belongs, which is obtained by the power consumption demand analysis module, in combination with power consumption predicted values in different time intervals and corresponding power supply quantities of the power plant in historical data, and calibrating the power generation demand of the power plant in the next time interval based on the time interval to which the current time belongs to obtain a first calibration result JZD;
the power generation scheme generation module acquires a first calibration result JZD and the total number n1 of the power generation equipment and generates different power generation schemes with the total power generation amount as the first calibration result;
the flue gas treatment result prediction module respectively calculates the total sulfur content corresponding to each power generation scheme obtained by the power generation scheme generation module according to the relationship between the sulfur content in the tail flue gas and the power generation power in the power generation equipment, and selects the power generation scheme with the minimum total sulfur content as the optimal power generation scheme;
and the power generation equipment management module manages the power generation equipment according to the optimal power generation scheme and controls the power generation power of each equipment.
Further, in the process that the flue gas treatment and analysis module analyzes the relation between the sulfur content in the tail flue gas and the power generation power in the power generation equipment, the power generation power P and the corresponding power generation power A of the power generation equipment in historical data are obtained P Constructing a sulfur-containing relational data pair (P, A) P ），
The flue gas processing and analyzing module acquires each sulfur-containing relation data pair under the condition that P is different values, and marks each acquired sulfur-containing relation data pair on a corresponding coordinate point in a first plane rectangular coordinate system which is constructed by taking o as an origin, taking power generation power as an x axis and taking sulfur content in tail flue gas as a y axis,
the flue gas processing and analyzing module fits each marking point in a first plane rectangular coordinate system by using a first function model preset in a database as reference through MATLAB, and a fitting curve with the minimum sum of distances to each marking point is recorded as a final fitting result, a function corresponding to the final fitting result is recorded as G (x), wherein a1 is not less than x and not more than a2, a1 represents the preset minimum generating power of the generating equipment, a2 represents the preset maximum generating power of the generating equipment,
g (x) is the analysis result of the relationship between the sulfur content in the tail flue gas and the power generation power in the power generation equipment,
the preset first function model in the database is
Figure 663388DEST_PATH_IMAGE001
Wherein b1 is a first coefficient, b2 is a second coefficient, and b3 is a third coefficient.
In the process of analyzing the relationship between the sulfur content in the tail flue gas and the power generation power in the power generation equipment, the flue gas treatment and analysis module considers that the power generation equipment has different power generation powers in the power generation process, the concentrations of sulfides in the flue gas generated in the first unit time are different, and the contents of the sulfides in the tail flue gas after passing through the flue gas treatment equipment are also different, so that G (x) is obtained, the relationship between the power generation power of the power generation equipment and the sulfur content in the tail flue gas is quantified, the prediction of the total sulfur content in the tail flue gas generated in each power generation scheme in the subsequent process is facilitated, and the power generation schemes are screened.
Further, in the process of analyzing the relationship between the change rate of the number of power supply users and the time interval, the power consumption demand analysis module acquires the change rate of the number of power supply users corresponding to the power supply amount of the power plant in each time interval and the power consumption of each power supply user in the historical data, records the change rate of the number of power supply users corresponding to the power supply amount of the power plant in the kth time interval as YHk, wherein k is an integer greater than or equal to 0,
the YHk corresponding to each time interval k with the same integer part in the k/a3 result is divided into one group to obtain a [ k/a3] +1 group set of change rates corresponding to the time intervals, which is marked as a [ k/a3] +1 set,
the electricity demand analysis module respectively obtains a first type data pair corresponding to each element in each set, and records the first type data pair corresponding to the elements YHk in the [ k/a3] +1 set as ([ k% a3], YHk), wherein [ k/a3] represents an integer part in a k/a3 result, and [ k% a3] represents a decimal part in the k/a3 result,
the power consumption demand analysis module takes o1 as an origin, the remainder of the time interval number divided by a3 as an x1 axis and the power supply user number change rate as a y1 axis to construct a second plane rectangular coordinate system,
will be [ k/a3]]Marking corresponding coordinate points in the second rectangular plane coordinate system by the first type data pairs corresponding to each element in the +1 set respectively, connecting two adjacent marked coordinate points in the second rectangular plane coordinate system according to the sequence from small to large of the x-axis coordinate, and marking the piecewise function corresponding to the obtained broken line as the [ k/a3] th]Function HB of change rate of number of +1 power supply users along time interval [k/a3]+1 （x1）；
The electricity demand analysis module records the number of the time interval to which the current time belongs as kd to obtain a first relation result HBC (x 1), wherein the HBC (x 1) is a piecewise function, x1 is more than or equal to 0 and less than or equal to a3-1,
Figure 431755DEST_PATH_IMAGE002
the one-year duration is an integral multiple of t3, the number of time intervals corresponding to each year is equal, a3 represents the number of time intervals corresponding to one year, the number of power supply users at the end time point of the kth time interval is designated as YHJk, the number of power supply users at the start time point of the kth time interval is designated as YHQk, YHk = (YHJk-YHQk)/YHQk, and YHQk is greater than 0.
In the process of analyzing the relation between the change rate of the number of power supply users and the time interval by the power demand analysis module, the [ k/a3] and the [ k% a3] are calculated, so that the time intervals are grouped according to the time interval numbers, the [ k/a3] corresponding to each time interval in each group is equal and the [ k% a3] is different, a change function of the change rate of the number of the power supply users corresponding to each group along with the time interval is obtained according to the change rate corresponding to each time interval in each group in the subsequent process, a first relation result is obtained, and data reference is provided for the subsequent prediction of the power generation demand of a power plant in the next time interval based on the time interval to which the current time belongs.
Further, the power demand analysis module judges the relationship between [ (kd-1)/a 3] and [ kd/a3] in the process of predicting the power generation demand of the power plant in the next time interval of the time interval to which the current time belongs,
when [ (kd-1)/a 3] = [ kd/a3], then a first rate deviation value HBP is obtained,
Figure 254217DEST_PATH_IMAGE003
wherein YHk represents the change rate of the power supply amount of the power plant corresponding to the number of power supply users in the k 1-th time interval, HBC ([ k1% a3 ]) represents the change rate of the number of power supply users obtained by substituting [ k1% a3] into x1 in HBC (x 1),
when [ (kd-1)/a 3] +1= [ kd/a3], then a first rate deviation value HBP is obtained,
Figure 521250DEST_PATH_IMAGE004
predicting the change rate YHYkd, YHYkd = HBC ([ kd% a3 ]) -HBP of the power supply user number in the kd-th time interval corresponding to the power supply amount of the power plant, predicting the change rate YHY (kd + 1), YHY (kd + 1) = HBC ([ (kd + 1)% a3 ]) -HBP of the power supply user number in the kd + 1-th time interval corresponding to the power supply amount of the power plant,
acquiring the power supply amount of a power plant corresponding to the number of power supply users YHJ (kd-1) at the end time point of the kd-1 time interval, predicting the power supply amount of the power plant corresponding to the number of power supply users YHJ (kd + 1) at the end time point of the kd +1 time interval, and YHJ (kd + 1) = YHJ (kd-1) × (YHYkd + 1) (YHY (kd + 1) + 1);
the electricity consumption analysis module obtains average electricity consumption of each user in corresponding time intervals in historical data, the electricity consumption is marked as E1, the electricity supply users with the changed quantity comprise newly added users and cancelled users in corresponding time intervals, the electricity consumption analysis module obtains the increase rate of electricity consumption of the same power supply user in two adjacent time intervals with electricity consumption not being 0 in the historical data, calculates the average value of the increase rate of each electricity consumption corresponding to all the users, the average value is marked as g, and obtains average electricity consumption of each power supply user in the remaining power supply users except the power supply users with the changed divisor in kd-1 time intervals, the average value is marked as E,
the power demand analysis module obtains a predicted value YCD of the power plant power generation demand in the next time interval based on the time interval to which the current time belongs,
YCD=YHJ(kd-1)*(YHYkd+1)*E*(g+1)+(YHJ(kd+1)-YHJ(kd-1)*(YHYkd+1))*E1。
in the process of predicting the power generation demand of a power plant in the next time interval of the current time-based time interval by the power demand analysis module, the relation between [ (kd-1)/a 3] and [ kd/a3] is judged, so that a first change rate deviation value is accurately obtained, the change rates of the power supply quantity of the power plant corresponding to the power supply user number in the kd time interval and the kd +1 time interval are conveniently and accurately predicted subsequently, and data reference is provided for obtaining a predicted value YCD; and when the power supply amount of the power plant corresponds to the power supply user number YHJ (kd + 1) at the end time point of the kd +1 th time interval, YHJ (kd-1) is taken as a reference, because the power supply amount of the power plant corresponds to the power supply user number YHJ (kd-1) at the end time point of the kd-1 th time interval, which can be accurately obtained in the database, and the time interval to which the current time belongs may not have a result yet, and thus the power supply amount of the power plant corresponds to the power supply user number at the end time point of the kd-th time interval cannot be accurately obtained through the database.
Further, the power consumption deviation analysis module obtains power consumption demand predicted values and corresponding power plant power supply amounts in different time intervals in the historical data, records the power consumption demand predicted value corresponding to the m 1-th time interval in the historical data as LXDm1, records the power plant power supply amount corresponding to the m 1-th time interval in the historical data as LGDm1,
if the predicted value of the electricity demand amount corresponding to the m 1-th time interval in the historical data does not exist, determining that LXDm1=0,
if the power supply amount of the power plant corresponding to the m 1-th time interval in the historical data does not exist, judging that LGDm1=0,
when LXDm1 x LGDm1=0, judging that no deviation value of the power generation demand amount exists in the m 1-th time interval in the historical data,
when LXDm1 x LGDm1 is not equal to 0, the power generation demand deviation value exists in the m1 th time interval in the history data, the power generation demand deviation value corresponding to the m1 th time in the history data is recorded as FXDm1, FXDm1= LGDm1-LXDm1,
acquiring the maximum value of each corresponding FXDm1 when m1 is different in each interval with the generating demand deviation value, taking the maximum value as the prediction result of the generating demand deviation value of the power plant in the next time interval based on the time interval to which the current time belongs, recording the prediction result as FXDZ,
and the deviation value FXDZ of the power generation demand is calibrated once every second unit time.
The method judges the relationship between LXDM1 and LGDm1 and 0, and considers the condition that the predicted value of the power demand corresponding to the time interval does not exist or the power supply amount of the power plant corresponding to the time interval does not exist in the historical data, so that the power consumption deviation analysis module cannot accurately calculate the corresponding deviation value of the power demand; LXDm1 may be greater than or equal to LGDm1 or smaller than LGDm1, when the LXDm1 is greater than the LGDm1, the power generation demand is larger than the power supply quantity of a power plant, the power generation quantity is surplus, and when the LXDm1 is smaller than the LGDm1, the power generation demand is smaller than the power supply quantity of the power plant, the power generation quantity is not enough to support the power supply of the power plant, and the standby power quantity needs to be called to support the power supply of the power plant.
Further, the power consumption deviation analysis module obtains a power generation demand predicted value YCD and a power generation demand deviation value FXDZ of a power plant in a next time interval of the time interval to which the current time belongs, and obtains a first calibration result JZD, wherein JZD = YCD + FXDZ.
Further, in the process of generating different power generation schemes by the power generation scheme generation module, the first calibration result JZD and the total number n1 of the power generation devices are obtained, the preset minimum power generation power of each power generation device is a1, the preset maximum power generation power of each power generation device is a2,
randomly generating different power generation schemes, acquiring the power generation power corresponding to the power generation equipment numbered at different time in each power generation scheme, recording the power generation power corresponding to the power generation equipment numbered m at time T in the power generation scheme as FGTm,
the integration result of the generated power of each power generation device in each generated power generation scheme in the corresponding time interval is equal to JZD, namely
Figure 657834DEST_PATH_IMAGE005
Wherein, T1 represents the starting time of the time interval corresponding to the power generation scheme, and the time interval corresponding to the power generation scheme is the next time interval based on the time interval to which the current time belongs;
in the generated power generation equipment with the same number in each power generation scheme, when the power generation power corresponding to any two time points is compared, the power generation power corresponding to the smaller time point is greater than or equal to the power generation power corresponding to the larger time point.
In the process of generating different power generation schemes by the power generation scheme generation module, the integral result of the generated power of each power generation device in the corresponding time interval in each generated power generation scheme is limited to be equal to JZD, and when the generated power corresponding to any two time points is compared in the power generation devices with the same number in each generated power generation scheme, the generated power corresponding to a smaller time point is greater than or equal to the generated power corresponding to a larger time point.
Further, the flue gas treatment result prediction module obtains an analysis result G (x) of the relationship between the sulfur content in the tail flue gas and the power generation power in the power generation equipment and each power generation scheme obtained by the power generation scheme generation module,
the flue gas treatment result prediction module numbers each power generation scheme, records the power generation power corresponding to the power generation equipment with the number r being m when the time T in the power generation scheme as FGTmr, predicts the comprehensive sulfur content of HLr in the tail flue gas generated by the r-th power generation scheme,
Figure 326712DEST_PATH_IMAGE006
wherein, G (FGTmr) represents the sulfur content in the tail flue gas after the tail flue gas is treated by the flue gas treatment equipment, which is generated by operating the flue gas in the first unit time t2 under the condition that the mth power generation equipment keeps the power generation power FGTmr unchanged.
In the invention, the ratio of G (FGTmr) to T2 represents the sulfur content accumulation rate of the tail flue gas after the tail flue gas is treated by the flue gas treatment equipment in the flue gas generated at the time T under the condition that the mth power generation equipment keeps the power generation power FGTmr unchanged.
Further, when the power generation equipment management module manages the power generation equipment according to the optimal power generation scheme, the state of each power generation equipment is monitored, when the power generation power of the power generation equipment is abnormal with the power generation power corresponding to the optimal power generation scheme, early warning is given to a manager, and otherwise, early warning is not given to the manager.
When the power generation equipment management module manages the power generation equipment according to the optimal power generation scheme, the state of each power generation equipment is monitored, so that the power generation equipment is monitored and early warned in time, the power generation equipment is ensured to operate correctly, and the operation safety of the power generation equipment is ensured.
Compared with the prior art, the invention has the following beneficial effects: the method not only monitors the sulfur content in the flue gas discharged by the power plant, but also considers the condition that the sulfur content of the flue gas generated by the power generation equipment at different power generation powers is different after the flue gas is processed by the flue gas processing equipment, generates different power generation schemes according to the predicted power consumption demand of the power supply user, screens the power generation schemes by considering the total sulfur content of the tail flue gas corresponding to the different power generation schemes, manages and controls the power generation power of the power generation equipment according to the final screening result, and realizes effective management of the power generation equipment.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic structural diagram of a data intelligent monitoring system based on a secure operating system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention provides a technical solution: an intelligent data monitoring system based on a safety operating system, the system comprises: an operation data acquisition module, a flue gas treatment analysis module, an electricity demand analysis module, an electricity deviation analysis module, a power generation scheme generation module, a flue gas treatment result prediction module and a power generation equipment management module,
the operation data acquisition module acquires the power generation power of different power generation equipment corresponding to each time point in the historical data, numbers the different power generation equipment, and defaults that the specifications of the power generation equipment are the same;
the flue gas treatment and analysis module obtains the flue gas generated by operating the first unit time t2 under the condition that each power generation device in the historical data keeps unchanged power generation power P, and the sulfur content in the tail flue gas treated by the flue gas treatment device is recorded as A P Analyzing the relation between the sulfur content in the tail flue gas and the power generation power in the power generation equipment, wherein the first unit time t2 is a constant preset in a database;
the first unit time t2 in this embodiment is 5 minutes;
the flue gas processing and analyzing module obtains the power generation power P and the corresponding power generation power A of the power generation equipment in the historical data in the process of analyzing the relation between the sulfur content in the tail flue gas and the power generation power in the power generation equipment P Constructing a sulfur-containing relational data pair (P, A) P ），
The flue gas processing and analyzing module acquires each sulfur-containing relation data pair under the condition that P is different values, and marks each acquired sulfur-containing relation data pair on a corresponding coordinate point in a first plane rectangular coordinate system which is constructed by taking o as an origin, taking power generation power as an x axis and taking sulfur content in tail flue gas as a y axis,
the flue gas processing and analyzing module fits each marking point in a first plane rectangular coordinate system by using a first function model preset in a database as reference through MATLAB, and a fitting curve with the minimum sum of distances to each marking point is recorded as a final fitting result, a function corresponding to the final fitting result is recorded as G (x), wherein a1 is not less than x and not more than a2, a1 represents the preset minimum generating power of the generating equipment, a2 represents the preset maximum generating power of the generating equipment,
g (x) is the analysis result of the relationship between the sulfur content in the tail flue gas and the power generation power in the power generation equipment,
the preset first function model in the database is
Figure 241448DEST_PATH_IMAGE001
Wherein b1 is a first coefficient, b2 is a second coefficient, and b3 is a third coefficient.
The power consumption demand analysis module acquires the change rate of the number of power supply users corresponding to the power supply quantity of the power plant in each time interval in historical data and the power consumption of each power supply user, analyzes the relation between the change rate of the number of the power supply users and the time interval to obtain a first relation result, and then predicts the power generation demand quantity of the power plant in the next time interval based on the time interval to which the current time belongs according to the first relation result, wherein the time length corresponding to each time interval is equal to a second unit time length t3, and the second unit time length t3 is a preset constant in a database;
in the process of analyzing the relation between the change rate of the number of power supply users and the time interval, the power consumption demand analysis module acquires the change rate of the number of the power supply users corresponding to the power supply amount of the power plant in each time interval and the power consumption of each power supply user in historical data, records the change rate of the number of the power supply users corresponding to the power supply amount of the power plant in the kth time interval as YHk, wherein k is an integer greater than or equal to 0,
the YHk corresponding to each time interval k with the same integer part in the k/a3 result is divided into one group to obtain a group consisting of the [ k/a3] +1 group of time intervals corresponding to the change rate, which is marked as the [ k/a3] +1 group,
the power consumption requirement analysis module respectively acquires a first type data pair corresponding to each element in each set, records the first type data pair corresponding to the elements YHk in the [ k/a3] +1 set as ([ k% a3], YHk), wherein [ k/a3] represents an integer part in a k/a3 result, and [ k% a3] represents a decimal part in the k/a3 result,
the second unit duration in this embodiment is one month, a3 equals 12,
if there are 24 time intervals, numbered 0 to 23,
since the integer part of the result of dividing 12 by any number from 0 to 11 is 0, the power supply user data change rate corresponding to the 0 th to 11 th time intervals is the first group,
since the integer part of the result obtained by dividing 12 by any number of 12 to 23 is 1, the power supply user data change rate corresponding to the 12 th to 23 th time intervals is the second group;
the power consumption demand analysis module takes o1 as an origin, the remainder of the time interval number divided by a3 as an x1 axis and the power supply user number change rate as a y1 axis to construct a second plane rectangular coordinate system,
will be [ k/a3]]Marking corresponding coordinate points in the second rectangular plane coordinate system by the first type data pairs corresponding to each element in the +1 set respectively, connecting two adjacent marked coordinate points in the second rectangular plane coordinate system according to the sequence from small to large of the x-axis coordinate, and marking the piecewise function corresponding to the obtained broken line as the [ k/a3] th]Function HB of change rate of number of +1 power supply users along time interval [k/a3]+1 （x1）；
The electricity demand analysis module records the number of the time interval to which the current time belongs as kd to obtain a first relation result HBC (x 1), wherein the HBC (x 1) is a piecewise function, x1 is more than or equal to 0 and less than or equal to a3-1,
Figure 995777DEST_PATH_IMAGE007
an example of the addition of functions in this embodiment is as follows:
if the function a corresponds to U1 (x) = x +1 in the interval [0,2], the corresponding function in the interval (2,3 ] is U1 (x) =2x-1;
if the function b is the corresponding function in the interval [0,3] is U2 (x) = x +2;
the function obtained by adding the function A and the function B is U1 (x) + U2 (x),
when x ∈ [0,2], U1 (x) + U2 (x) =2x +3,
when x ∈ (2,3 ], U1 (x) + U2 (x) =3 x +1;
the one-year duration is an integral multiple of t3, the number of time intervals corresponding to each year is equal, a3 represents the number of time intervals corresponding to one year, the number of power supply users at the end time point of the kth time interval is denoted as YHJk, the number of power supply users at the start time point of the kth time interval is denoted as YHQk, YHk = (YHJk-YHQk)/YHQk, and YHQk is greater than 0.
The power demand analysis module judges the relation between [ (kd-1)/a 3] and [ kd/a3] in the process of predicting the power generation demand of the power plant in the next time interval of the time interval to which the current time belongs,
when [ (kd-1)/a 3] = [ kd/a3], then a first rate deviation value HBP is obtained,
Figure 936051DEST_PATH_IMAGE008
wherein YHk represents the change rate of the power supply amount of the power plant corresponding to the number of power supply users in the k 1-th time interval, HBC ([ k1% a3 ]) represents the change rate of the number of power supply users obtained by substituting [ k1% a3] into x1 in HBC (x 1),
when [ (kd-1)/a 3] +1= [ kd/a3], then a first rate deviation value HBP is obtained,
Figure 459436DEST_PATH_IMAGE009
predicting the change rate YHYkd, YHYkd = HBC ([ kd% a3 ]) -HBP of the power supply user number in the kd-th time interval corresponding to the power supply amount of the power plant, predicting the change rate YHY (kd + 1), YHY (kd + 1) = HBC ([ (kd + 1)% a3 ]) -HBP of the power supply user number in the kd + 1-th time interval corresponding to the power supply amount of the power plant,
acquiring the power supply amount of a power plant corresponding to the number of power supply users YHJ (kd-1) at the end time point of the kd-1 time interval, predicting the power supply amount of the power plant corresponding to the number of power supply users YHJ (kd + 1) at the end time point of the kd +1 time interval, and YHJ (kd + 1) = YHJ (kd-1) × (YHYkd + 1) (YHY (kd + 1) + 1);
the power consumption analysis module obtains average power consumption of each user in corresponding time intervals in historical data among power supply users with the quantity changing in the time intervals, the average power consumption is marked as E1, the power supply users with the quantity changing include newly added users and logout users in the corresponding time intervals, the power consumption analysis module obtains the increase rate of the power consumption of the same power supply user in the historical data in two adjacent time intervals with the power consumption not being 0, calculates the average value of the increase rates of the power consumption corresponding to all the users, the average value is marked as g, and the average power consumption of each power supply user in the remaining power supply users except the power supply user with the quantity changing in kd-1 time intervals is obtained and is marked as E,
the power demand analysis module obtains a predicted value YCD of the power plant power generation demand in the next time interval based on the time interval to which the current time belongs,
YCD=YHJ(kd-1)*(YHYkd+1)*E*(g+1)+(YHJ(kd+1)-YHJ(kd-1)*(YHYkd+1))*E1。
the power consumption deviation analysis module is used for predicting a power generation demand deviation value of a power plant in the next time interval based on the time interval to which the current time belongs according to a power generation demand predicted value of the power plant in the next time interval based on the time interval to which the current time belongs, which is obtained by the power consumption demand analysis module, in combination with power consumption predicted values in different time intervals and corresponding power supply quantities of the power plant in historical data, and calibrating the power generation demand of the power plant in the next time interval based on the time interval to which the current time belongs to obtain a first calibration result JZD;
the power consumption deviation analysis module obtains power consumption demand predicted values and corresponding power plant power supply amounts in different time intervals in the historical data, records the power consumption demand predicted value corresponding to the m 1-th time interval in the historical data as LXDM1, records the power plant power supply amount corresponding to the m 1-th time interval in the historical data as LGDm1,
if the predicted value of the electricity demand amount corresponding to the m 1-th time interval does not exist in the historical data, the situation that LXDm1=0 is judged,
if the power supply amount of the power plant corresponding to the m 1-th time interval in the historical data does not exist, judging that LGDm1=0,
when LXDm1 × LGDm1=0, it is determined that there is no deviation value of the power generation demand amount in the m 1-th time interval in the history data,
when LXDm1 x LGDm1 is not equal to 0, the power generation demand deviation value exists in the m1 th time interval in the history data, the power generation demand deviation value corresponding to the m1 th time in the history data is recorded as FXDm1, FXDm1= LGDm1-LXDm1,
acquiring the maximum value of each corresponding FXDm1 when m1 is different in each interval with the generating demand deviation value, taking the maximum value as the prediction result of the generating demand deviation value of the power plant in the next time interval based on the time interval to which the current time belongs, recording the prediction result as FXDZ,
and the deviation value FXDZ of the power generation demand is calibrated once every second unit time.
The power consumption deviation analysis module obtains a power generation demand predicted value YCD and a power generation demand deviation value FXDZ of a power plant in a next time interval based on a time interval to which the current time belongs, and obtains a first calibration result JZD, wherein JZD = YCD + FXDZ.
The power generation scheme generation module acquires a first calibration result JZD and the total number n1 of the power generation equipment and generates different power generation schemes with the total power generation amount as the first calibration result;
in the process of generating different power generation schemes by the power generation scheme generation module, acquiring a first calibration result JZD and the total number n1 of power generation equipment, wherein the preset minimum power generation power of each power generation equipment is a1, the preset maximum power generation power of each power generation equipment is a2,
randomly generating different power generation schemes, acquiring the generated power corresponding to the power generation equipment numbered at different time in each power generation scheme, recording the generated power corresponding to the power generation equipment numbered m at time T in the power generation scheme as FGTm,
the result of integration of the generated power of the individual power generation devices in the respective time interval in each generated power generation scheme is equal to JZD, i.e.
Figure 312117DEST_PATH_IMAGE005
Wherein T1 represents the starting time of a time interval corresponding to the power generation scheme, and the time interval corresponding to the power generation scheme is the next time interval based on the time interval to which the current time belongs;
in the generated power generation equipment with the same number in each power generation scheme, when the power generation power corresponding to any two time points is compared, the power generation power corresponding to the smaller time point is greater than or equal to the power generation power corresponding to the larger time point.
The flue gas treatment result prediction module respectively calculates the total sulfur content corresponding to each power generation scheme obtained by the power generation scheme generation module according to the relationship between the sulfur content in the tail flue gas and the power generation power in the power generation equipment, and selects the power generation scheme with the minimum total sulfur content as the optimal power generation scheme;
the flue gas treatment result prediction module obtains an analysis result G (x) of the relationship between the sulfur content in the tail flue gas and the power generation power in the power generation equipment and each power generation scheme obtained by the power generation scheme generation module,
the flue gas treatment result prediction module numbers each power generation scheme, records the power generation power corresponding to the power generation equipment with the number r being m when the time T in the power generation scheme as FGTmr, predicts the comprehensive sulfur content of HLr in the tail flue gas generated by the r-th power generation scheme,
Figure 553742DEST_PATH_IMAGE006
wherein, G (FGTmr) represents the sulfur content in the tail flue gas after the tail flue gas is treated by the flue gas treatment equipment, which is generated by operating the flue gas in the first unit time t2 under the condition that the mth power generation equipment keeps the power generation power FGTmr unchanged.
And the power generation equipment management module manages the power generation equipment according to the optimal power generation scheme and controls the power generation power of each equipment.
When the power generation equipment management module manages the power generation equipment according to the optimal power generation scheme, the state of each power generation equipment is monitored, when the power generation power of the power generation equipment is abnormal to the power generation power corresponding to the optimal power generation scheme, early warning is carried out on a manager, otherwise, early warning is not carried out on the manager.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described above, or equivalents may be substituted for elements thereof. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (9)

1. An intelligent data monitoring system based on a safe operating system, which is characterized by comprising: an operation data acquisition module, a flue gas treatment analysis module, an electricity demand analysis module, an electricity deviation analysis module, a power generation scheme generation module, a flue gas treatment result prediction module and a power generation equipment management module,
the operation data acquisition module acquires the power generation power of different power generation equipment corresponding to each time point in the historical data, numbers the different power generation equipment, and defaults that the specifications of the power generation equipment are the same;
the flue gas treatment and analysis module obtains the flue gas generated by operating the first unit time t2 under the condition that each power generation device in the historical data keeps unchanged power generation power P, and the sulfur content in the tail flue gas treated by the flue gas treatment device is recorded as A P Analyzing the relation between the sulfur content in the tail flue gas and the power generation power in the power generation equipment, wherein the first unit time t2 is a preset constant in a database;
the power consumption demand analysis module acquires the change rate of the number of power supply users corresponding to the power supply quantity of the power plant in each time interval in historical data and the power consumption of each power supply user, analyzes the relation between the change rate of the number of the power supply users and the time interval to obtain a first relation result, and then predicts the power generation demand quantity of the power plant in the next time interval based on the time interval to which the current time belongs according to the first relation result, wherein the time length corresponding to each time interval is equal to a second unit time length t3, and the second unit time length t3 is a preset constant in a database;
the power consumption deviation analysis module is used for predicting a power generation demand deviation value of a power plant in the next time interval based on the time interval to which the current time belongs according to a power generation demand predicted value of the power plant in the next time interval based on the time interval to which the current time belongs, which is obtained by the power consumption demand analysis module, in combination with power consumption predicted values in different time intervals and corresponding power supply quantities of the power plant in historical data, and calibrating the power generation demand of the power plant in the next time interval based on the time interval to which the current time belongs to obtain a first calibration result JZD;
the power generation scheme generation module acquires a first calibration result JZD and the total number n1 of the power generation equipment and generates different power generation schemes with the total power generation amount as the first calibration result;
the flue gas treatment result prediction module respectively calculates the total sulfur content corresponding to each power generation scheme obtained by the power generation scheme generation module according to the relationship between the sulfur content in the tail flue gas and the power generation power in the power generation equipment, and selects the power generation scheme with the minimum total sulfur content as the optimal power generation scheme;
and the power generation equipment management module manages the power generation equipment according to the optimal power generation scheme and controls the power generation power of each equipment.
2. The intelligent data monitoring system based on the safe operating system as claimed in claim 1, wherein: the flue gas processing and analyzing module obtains the power generation power P and the corresponding power generation power A of the power generation equipment in the historical data in the process of analyzing the relation between the sulfur content in the tail flue gas and the power generation power in the power generation equipment P Constructing a sulfur-containing relational data pair (P, A) P ），
The flue gas processing and analyzing module acquires each sulfur-containing relation data pair under the condition that P is different values, and marks each acquired sulfur-containing relation data pair on a corresponding coordinate point in a first plane rectangular coordinate system which is constructed by taking o as an original point, taking power generation as an x axis and taking sulfur content in tail flue gas as a y axis,
the flue gas processing and analyzing module fits each marking point in a first plane rectangular coordinate system by using a first function model preset in a database as reference through MATLAB, and a fitting curve with the minimum sum of distances to each marking point is recorded as a final fitting result, a function corresponding to the final fitting result is recorded as G (x), wherein a1 is not less than x and not more than a2, a1 represents the preset minimum generating power of the generating equipment, a2 represents the preset maximum generating power of the generating equipment,
g (x) is the analysis result of the relationship between the sulfur content in the tail flue gas and the power generation power in the power generation equipment,
the preset first function model in the database is
Figure 724774DEST_PATH_IMAGE002
Wherein b1 is a first coefficient, b2 is a second coefficient, and b3 is a third coefficient.
3. The intelligent data monitoring system based on the safe operating system as claimed in claim 1, wherein: in the process of analyzing the relation between the change rate of the number of power supply users and the time interval, the power consumption demand analysis module acquires the change rate of the number of the power supply users corresponding to the power supply amount of the power plant in each time interval and the power consumption of each power supply user in historical data, records the change rate of the number of the power supply users corresponding to the power supply amount of the power plant in the kth time interval as YHk, wherein k is an integer greater than or equal to 0,
the YHk corresponding to each time interval k with the same integer part in the k/a3 result is divided into one group to obtain a [ k/a3] +1 group set of change rates corresponding to the time intervals, which is marked as a [ k/a3] +1 set,
the power consumption requirement analysis module respectively acquires a first type data pair corresponding to each element in each set, records the first type data pair corresponding to the elements YHk in the [ k/a3] +1 set as ([ k% a3], YHk), wherein [ k/a3] represents an integer part in a k/a3 result, and [ k% a3] represents a decimal part in the k/a3 result,
the power consumption demand analysis module takes o1 as an origin, the remainder of the time interval number divided by a3 as an x1 axis and the power supply user number change rate as a y1 axis to construct a second plane rectangular coordinate system,
will be [ k/a3]]Marking corresponding coordinate points in the second rectangular plane coordinate system by the first type data pairs corresponding to each element in the +1 set respectively, connecting two adjacent marked coordinate points in the second rectangular plane coordinate system according to the sequence from small to large of the x-axis coordinate, and marking the piecewise function corresponding to the obtained broken line as the [ k/a3] th]Function HB of change rate of number of +1 power supply users along time interval [k/a3]+1 （x1）；
The electricity demand analysis module records the number of the time interval to which the current time belongs as kd to obtain a first relation result HBC (x 1), wherein the HBC (x 1) is a piecewise function, x1 is more than or equal to 0 and less than or equal to a3-1,
Figure DEST_PATH_IMAGE004A
the one-year duration is an integral multiple of t3, the number of time intervals corresponding to each year is equal, a3 represents the number of time intervals corresponding to one year, the number of power supply users at the end time point of the kth time interval is designated as YHJk, the number of power supply users at the start time point of the kth time interval is designated as YHQk, YHk = (YHJk-YHQk)/YHQk, and YHQk is greater than 0.
4. The intelligent data monitoring system based on the safe operating system as claimed in claim 3, wherein: the power consumption demand analysis module judges the relation between [ (kd-1)/a 3] and [ kd/a3] in the process of predicting the power generation demand of the power plant in the next time interval of the time interval to which the current time belongs,
when [ (kd-1)/a 3] = [ kd/a3], then a first rate deviation value HBP is obtained,
Figure DEST_PATH_IMAGE006A
wherein YHk represents the change rate of the power supply amount of the power plant corresponding to the number of power supply users in the k 1-th time interval, HBC ([ k1% a3 ]) represents the change rate of the number of power supply users obtained by substituting [ k1% a3] into x1 in HBC (x 1),
when [ (kd-1)/a 3] +1= [ kd/a3], then a first rate deviation value HBP is obtained,
Figure DEST_PATH_IMAGE008A
predicting the change rate YHYkd and YHYkd = HBC ([ kd% a3 ]) -HBP of the power supply user number in the kd-th time interval corresponding to the power supply amount of the power plant, predicting the change rate YHY (kd + 1) of the power supply user number in the kd + 1-th time interval corresponding to the power supply amount of the power plant, YHY (kd + 1) = HBC ([ (kd + 1)% a3 ]) -HBP,
acquiring the power supply amount of a power plant corresponding to the number of power supply users YHJ (kd-1) at the end time point of the kd-1 time interval, predicting the power supply amount of the power plant corresponding to the number of power supply users YHJ (kd + 1) at the end time point of the kd +1 time interval, and YHJ (kd + 1) = YHJ (kd-1) × (YHYkd + 1) (YHY (kd + 1) + 1);
the power consumption analysis module obtains average power consumption of each user in corresponding time intervals in historical data among power supply users with the quantity changing in the time intervals, the average power consumption is marked as E1, the power supply users with the quantity changing include newly added users and logout users in the corresponding time intervals, the power consumption analysis module obtains the increase rate of the power consumption of the same power supply user in the historical data in two adjacent time intervals with the power consumption not being 0, calculates the average value of the increase rates of the power consumption corresponding to all the users, the average value is marked as g, and the average power consumption of each power supply user in the remaining power supply users except the power supply user with the quantity changing in kd-1 time intervals is obtained and is marked as E,
the power demand analysis module obtains a predicted value YCD of the power generation demand of the power plant in the next time interval based on the time interval to which the current time belongs,
YCD=YHJ(kd-1)*(YHYkd+1)*E*(g+1)+(YHJ(kd+1)-YHJ(kd-1)*(YHYkd+1))*E1。
5. the intelligent data monitoring system based on the safe operating system as claimed in claim 4, wherein: the power consumption deviation analysis module obtains power consumption demand predicted values and corresponding power plant power supply amounts in different time intervals in the historical data, records the power consumption demand predicted value corresponding to the m 1-th time interval in the historical data as LXDM1, records the power plant power supply amount corresponding to the m 1-th time interval in the historical data as LGDm1,
if the predicted value of the electricity demand amount corresponding to the m 1-th time interval does not exist in the historical data, the situation that LXDm1=0 is judged,
if the power supply amount of the power plant corresponding to the m 1-th time interval in the historical data does not exist, LGDm1=0 is judged,
when LXDm1 × LGDm1=0, it is determined that there is no deviation value of the power generation demand amount in the m 1-th time interval in the history data,
when LXDm1 x LGDm1 is not equal to 0, judging that a power generation demand deviation value exists in the m1 th time interval in the history data, recording the power generation demand deviation value corresponding to the m1 th time in the history data as FXDm1, and recording FXDm1= LGDm1-LXDm1,
acquiring the maximum value of each corresponding FXDm1 when m1 is different in each interval with the deviation value of the power generation demand, taking the maximum value as the prediction result of the deviation value of the power generation demand of the power plant in the next time interval based on the time interval to which the current time belongs, and recording the prediction result as FXDZ,
and the deviation value FXDZ of the power generation demand is calibrated once every second unit time.
6. The intelligent data monitoring system based on the safe operating system as claimed in claim 5, wherein: the power consumption deviation analysis module obtains a power generation demand predicted value YCD and a power generation demand deviation value FXDZ of a power plant in a next time interval based on a time interval to which the current time belongs, and obtains a first calibration result JZD, wherein JZD = YCD + FXDZ.
7. The intelligent data monitoring system based on the safe operating system as claimed in claim 1, wherein: in the process of generating different power generation schemes by the power generation scheme generation module, acquiring a first calibration result JZD and the total number n1 of power generation equipment, wherein the preset minimum power generation power of each power generation equipment is a1, the preset maximum power generation power of each power generation equipment is a2,
randomly generating different power generation schemes, acquiring the power generation power corresponding to the power generation equipment numbered at different time in each power generation scheme, recording the power generation power corresponding to the power generation equipment numbered m at time T in the power generation scheme as FGTm,
the result of integration of the generated power of the individual power generation devices in the respective time interval in each generated power generation scheme is equal to JZD, i.e.
Figure DEST_PATH_IMAGE010A
Wherein T1 represents the starting time of a time interval corresponding to the power generation scheme, and the time interval corresponding to the power generation scheme is the next time interval based on the time interval to which the current time belongs;
in the generated power generation equipment with the same number in each power generation scheme, when the power generation power corresponding to any two time points is compared, the power generation power corresponding to the smaller time point is greater than or equal to the power generation power corresponding to the larger time point.
8. The intelligent data monitoring system based on the safe operating system as claimed in claim 2, wherein: the flue gas treatment result prediction module obtains an analysis result G (x) of the relationship between the sulfur content in the tail flue gas and the power generation power in the power generation equipment and each power generation scheme obtained by the power generation scheme generation module,
the flue gas treatment result prediction module numbers each power generation scheme, records the power generation power corresponding to the power generation equipment with the number r being m when the time T in the power generation scheme as FGTmr, predicts the comprehensive sulfur content of HLr in the tail flue gas generated by the r-th power generation scheme,
Figure DEST_PATH_IMAGE012A
wherein, G (FGTmr) represents the sulfur content in the tail flue gas after the tail flue gas is treated by the flue gas treatment equipment, which is generated by operating the flue gas in the first unit time t2 under the condition that the mth power generation equipment keeps the power generation power FGTmr unchanged.
9. The intelligent data monitoring system based on the safe operating system as claimed in claim 1, wherein: when the power generation equipment management module manages the power generation equipment according to the optimal power generation scheme, the state of each power generation equipment is monitored, when the power generation power of the power generation equipment is abnormal with the power generation power corresponding to the optimal power generation scheme, early warning is carried out on a manager, and otherwise, early warning is not carried out on the manager.
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