Power plant fuel inventory early warning method and system

Abstract

The invention relates to the technical field of inventory management, in particular to a power plant fuel inventory early warning method and system. Comprising the steps of establishing a coal consumption prediction model and a plurality of monitoring periods; generating a coal consumption prediction curve in the current monitoring period according to the coal consumption prediction model, and setting a safety stock value according to the coal consumption prediction curve and the environmental parameters of the current monitoring period; acquiring fuel inventory parameters according to a preset feedback time node, and judging whether an early warning instruction is generated or not; the safe inventory value of fuel in a single monitoring period is set by generating a coal consumption prediction curve, and the safe inventory value is dynamically adjusted by periodically updating the coal consumption prediction curve and combining real-time fuel transaction environment parameters, so that the turnover efficiency of inventory fuel is improved, and meanwhile, the fuel inventory in a power plant is monitored in real time; and by analyzing the actual coal consumption, the coal consumption prediction curve and the safe inventory value are corrected in time, so that the inventory management efficiency of the power plant fuel is improved, and the overall cost of the fuel is reduced.

Description

Technical Field

[0001] This application relates to the technical field of inventory management, and particularly to a method and system for early warning of fuel inventory in power plants. Background Art

[0002] Due to the continuous increase in the demand for electricity, the demand for coal, the raw material for power generation, is also increasing. Affected by factors such as insufficient transportation capacity, uneven distribution of coal resources, and seasonal fluctuations in demand. Thermal power generation enterprises have been under great inventory pressure in their production activities.

[0003] Currently, the early warning of coal inventory in thermal power plants mainly relies on manual experience to set corresponding safety inventory values. The monitoring and early warning strategies for fuel inventory are relatively rough. During natural storage and handling, coal will undergo weathering, oxidation, and loss, resulting in a decline in quality. At present, the method of setting safety inventory values relying on manual experience leads to a low level of coal inventory management and a high overall operation and maintenance cost of fuel.

[0004] Application Content The purpose of this application is: To solve the above technical problems, this application provides a method and system for early warning of fuel inventory in power plants, aiming to improve the inventory management efficiency of power plant fuel and reduce the overall cost of fuel.

[0005] In some embodiments of this application, by generating a coal consumption prediction curve, the safety inventory value of fuel within a single monitoring period is set, and by periodically updating the coal consumption prediction curve and combining real-time fuel trading environment parameters, the safety inventory value is dynamically adjusted to improve the turnover efficiency of inventory fuel, avoid the decline in quality caused by long-term stacking of fuel, thereby improving the inventory management efficiency of power plant fuel and reducing the overall cost of fuel.

[0006] In some embodiments of this application, by collecting the operation data of the power plant, the fuel inventory in the power plant is monitored in real time, and by analyzing the actual coal consumption, the coal consumption prediction curve and the safety inventory value are corrected in a timely manner to avoid problems such as inventory shortage affecting the operation of the power plant, thereby increasing the overall cost of fuel.

[0007] In some embodiments of this application, a method for early warning of fuel inventory in power plants is provided, including: Establishing a coal consumption prediction model and multiple monitoring periods; Generating a coal consumption prediction curve for the current monitoring period according to the coal consumption prediction model, and setting a safety inventory value according to the coal consumption prediction curve and the environmental parameters of the current monitoring period; Obtaining fuel inventory parameters according to a preset feedback time node and determining whether to generate a warning instruction; Among them, the preset feedback time node includes: Setting multiple time intervals within the current monitoring period: Establish a time interval sequence T, T = (t1, t2…t i …t n ), where t i is the i-th time interval; n is the number of time intervals; Set the end time node of each time interval as the feedback time node.

[0008] In some embodiments of the present application, setting the safety inventory value includes: Generate the coal consumption prediction value for each time interval within the current monitoring period according to the coal consumption prediction curve; Establish a coal consumption prediction value sequence B, B = (b1, b2…b i …b n ), where b i is the coal consumption prediction value for the i-th time interval within the current monitoring period; Set the coal consumption evaluation value c according to the coal consumption prediction value B and the environmental parameters of the current monitoring period; Set the compensation coefficient r according to the coal consumption evaluation value c; Generate the primary safety inventory value K1 for the current monitoring period, K1 = r * k'1, where k'1 is the preset first inventory reference value; Generate the secondary safety inventory value K2 for the current monitoring period, K2 = r * k'2, where k'2 is the preset second inventory reference value; and k'1 < k'2.

[0009] In some embodiments of the present application, generating the coal consumption evaluation value c includes: c = e1 * Q1 * b i + e2 * Q2 * (b i - b') 2 + e3 * Q3 * µ i * j i where e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; e3 is the preset third weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; Q3 is the preset third fixed coefficient; b' is the average value of all data in the established coal consumption prediction value sequence B; θ is the number of environmental disturbance indicators; µ i is the influence factor of the i-th environmental disturbance indicator; j i is the reference value of the i-th environmental disturbance indicator within the current monitoring period.

[0010] In some embodiments of the present application, determining whether to generate a warning instruction includes: Obtain the fuel inventory parameters at the current feedback time node;​ Generate the remaining fuel quantity k at the current feedback time node according to the fuel inventory parameter; Generate the actual coal consumption sequence D, D = (d1, d2…d i …d n1 ), where n1 is the number of time intervals between the start time node of the current monitoring period and the current feedback time node; d i is the actual coal consumption value in the i-th time interval within the current monitoring period; Generate the coal consumption deviation value f; Judge whether to generate a correction instruction according to the coal consumption deviation value f; Judge whether to correct the primary safety inventory value K1 and the secondary safety inventory value K2 according to the correction instruction; Set the correction coefficient m according to the coal consumption deviation value f; Judge whether to generate a warning instruction according to the correction coefficient m and the remaining fuel quantity k.

[0011] In some embodiments of the present application, generating the coal consumption deviation value f includes: f = e4 * Q4 * (d i - b i) 2 + e5 * Q5 * Y(i) * (d i - b i) ; Wherein, e4 is a preset fourth weight coefficient; e5 is a preset fifth weight coefficient; Q4 is a preset fourth fixed coefficient; Q5 is a preset fifth fixed coefficient; Y(i) is a selection coefficient; if (d i - b i ) > 0, Y(i) = 1; if (d i - b i ) < 0, Y(i) = 0.

[0012] In some embodiments of the present application, when judging whether to generate a correction instruction, it includes: Preset the first deviation evaluation value threshold F1 and the second deviation evaluation value threshold F2; If f < F1, no correction instruction is generated at the current feedback time node; If F1 ≤ f < F2, a primary correction instruction is generated at the current feedback time node, and the coal consumption prediction curve within the current monitoring period is updated according to the primary correction instruction; If f ≥ F2, a secondary correction instruction is generated at the current feedback time node, and the coal consumption prediction model is corrected according to the secondary correction instruction.

[0013] In some embodiments of the present application, judging whether to generate a warning instruction includes: If m*k≥K2, no warning instruction is generated at the current feedback time node; If K1≤m*k<K2, a first-level warning instruction is generated at the current feedback time node; If m*k<K1, a second-level warning instruction is generated at the current feedback time node.

[0014] In some embodiments of the present application, a power plant fuel inventory warning system is provided, including: A central control unit for establishing a coal consumption prediction model and multiple monitoring periods; A monitoring unit for collecting fuel inventory parameters according to a preset feedback time node; The central control unit includes: A first processing module for generating a coal consumption prediction curve within the current monitoring period according to the coal consumption prediction model; A second processing module for setting a safety inventory value according to the coal consumption prediction curve and the environmental parameters of the current monitoring period; A warning module for determining whether to generate a warning instruction according to the fuel inventory parameters; A third processing module for setting multiple time intervals within the current monitoring period: Establish a time interval sequence T, T=(t1, t2…t i …t n ), where t i is the i-th time interval; n is the number of time intervals; Set the end time node of each time interval as the feedback time node.

[0015] In some embodiments of the present application, the second processing module is further configured to: Generate coal consumption prediction values for each time interval within the current monitoring period according to the coal consumption prediction curve; Establish a coal consumption prediction value sequence B, B=(b1, b2…b i …b n ), where b i is the coal consumption prediction value of the i-th time interval within the current monitoring period; Set a coal consumption evaluation value c according to the coal consumption prediction value B and the environmental parameters of the current monitoring period; Set a compensation coefficient r according to the coal consumption evaluation value c; Generate a first-level safety inventory value K1 for the current monitoring period, K1=r*k'1, where k'1 is a preset first inventory reference value; Generate a second-level safety inventory value K2 for the current monitoring period, K2=r*k'2, where k'2 is a preset second inventory reference value; and k'1<k'2; Among them, generating the coal consumption evaluation value c includes: c = e1 * Q1 * b i + e2 * Q2 * (b i - b') 2 + e3 * Q3 * µ i * j i Wherein, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; e3 is a preset third weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; Q3 is a preset third fixed coefficient; b' is the average value of all data in the established coal consumption prediction value sequence B; θ is the number of environmental disturbance indicators; µ i is the influence factor of the i-th environmental disturbance indicator; j i is the reference value of the i-th environmental disturbance indicator in the current monitoring period.

[0016] In some embodiments of the present application, the warning module is further configured to: Obtain the fuel inventory parameter at the current feedback time node; Generate the remaining fuel quantity k at the current feedback time node according to the fuel inventory parameter; Generate an actual coal consumption quantity sequence D, D = (d1, d2…d i …d n1 ), where n1 is the number of time intervals between the start time node and the current feedback time node of the current monitoring period; d i is the actual coal consumption value of the i-th time interval in the current monitoring period; Generate a coal consumption deviation value f; f = e4 * Q4 * (d i - b i) 2 + e5 * Q5 * Y(i) * (d i - b i) ; Wherein, e4 is a preset fourth weight coefficient; e5 is a preset fifth weight coefficient; Q4 is a preset fourth fixed coefficient; Q5 is a preset fifth fixed coefficient; Y(i) is a selection coefficient; if (d i - b i ) > 0, Y(i) = 1; if (d i - b i ) < 0, Y(i) = 0; Judge whether to generate a correction instruction according to the coal consumption deviation value f; Judge whether to correct the primary safety inventory value K1 and the secondary safety inventory value K2 according to the correction instruction; ​Set the correction coefficient m according to the coal consumption deviation value f; If m*k≥K2, no warning command is generated at the current feedback time node; If K1≤m*k<K2, a first-level warning command is generated at the current feedback time node; If m*k<K1, a second-level warning command is generated at the current feedback time node.

[0017] Compared with the prior art, the beneficial effects of the power plant fuel inventory warning method and system in the embodiments of the present application are as follows: By generating a coal consumption prediction curve to set the safety inventory value of fuel within a single monitoring period, and by periodically updating the coal consumption prediction curve and combining real-time fuel trading environment parameters, the safety inventory value is dynamically adjusted, improving the turnover efficiency of the inventory fuel, avoiding the quality decline of fuel caused by long-term accumulation, thereby improving the inventory management efficiency of power plant fuel and reducing the overall cost of fuel.

[0018] By collecting the operation data of the power plant, the fuel inventory in the power plant is monitored in real time, and by analyzing the actual coal consumption, the coal consumption prediction curve and the safety inventory value are corrected in time to avoid the problem that the inventory shortage affects the operation of the power plant, thereby improving the overall cost of fuel. Description of the Drawings

[0019] Figure 1 It is a schematic flowchart of a power plant fuel inventory warning method in the preferred embodiment of the embodiments of the present application. Detailed Embodiments

[0020] The following further describes in detail the specific embodiments of the present application with reference to the drawings and embodiments. The following embodiments are used to illustrate the present application, but are not used to limit the scope of the present application.

[0021] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the present application.

[0022] The terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0023] In the description of the present application, it should be noted that unless otherwise clearly specified and limited, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be a direct connection or an indirect connection through an intermediate medium, and it may be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0024] As Figure 1 shown, a method for warning of fuel inventory in a power plant according to a preferred embodiment of the embodiment of the present application includes: S101: Establish a coal consumption prediction model and multiple monitoring cycles; S102: Generate a coal consumption prediction curve within the current monitoring cycle according to the coal consumption prediction model, and set a safety inventory value according to the coal consumption prediction curve and the environmental parameters of the current monitoring cycle; S103: Obtain fuel inventory parameters according to a preset feedback time node, and determine whether to generate a warning instruction; Among them, the preset feedback time node includes: Set multiple time intervals within the current monitoring cycle: Establish a time interval sequence T, T = (t1, t2... t i ... t n ), where t i is the i-th time interval; n is the number of time intervals; Set the end time node of each time interval as the feedback time node.

[0025] Specifically, generate a training data packet according to the historical operation parameters of the power plant, generate a coal consumption prediction model according to the training result, and set the duration of a single monitoring cycle according to the prediction accuracy of the coal consumption prediction model and the historical operation parameters of the power plant, so as to improve the accuracy of the coal consumption prediction curve.

[0026] Specifically, the duration of each time interval within a single monitoring cycle is the same, and the duration of the time interval is preferably one day, and the specific value of the duration can be set according to the historical operation parameters of the power plant.

[0027] Specifically, setting the safety inventory value includes: Generate the coal consumption prediction value of each time interval within the current monitoring cycle according to the coal consumption prediction curve; Establish a coal consumption prediction value sequence B, B = (b1, b2... b i ... b n ), where b i is the coal consumption prediction value of the i-th time interval within the current monitoring cycle; Set the coal consumption evaluation value c according to the predicted coal consumption value B and the environmental parameters in the current monitoring period; Set the compensation coefficient r according to the coal consumption evaluation value c; Generate the primary safety inventory value K1 for the current monitoring period, K1 = r * k'1, where k'1 is the preset first inventory reference value; Generate the secondary safety inventory value K2 for the current monitoring period, K2 = r * k'2, where k'2 is the preset second inventory reference value; and k'1 < k'2.

[0028] Specifically, determine the first inventory reference value and the second inventory reference value based on the historical operation parameters of the power plant and manual experience.

[0029] Specifically, the first inventory reference value refers to the minimum inventory value that needs to be guaranteed within a single order cycle considering the transportation time. That is, the inventory value generated based on the longest historical transportation duration and the average daily coal consumption.

[0030] Specifically, the second inventory reference value is obtained by adding a fluctuation inventory value to the first inventory reference value. The fluctuation inventory value is the inventory value set to account for possible time delays during transportation.

[0031] Specifically, establish a mapping relationship between the coal consumption evaluation value and the compensation coefficient based on the historical operation parameters of the power plant. The greater the coal consumption evaluation value, the greater the corresponding compensation coefficient.

[0032] Specifically, generating the coal consumption evaluation value c includes: c = e1 * Q1 * b i + e2 * Q2 * (b i - b') 2 + e3 * Q3 * µ i * j i where e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; e3 is the preset third weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; Q3 is the preset third fixed coefficient; b' is the average value of all data in the coal consumption prediction value sequence B; θ is the number of environmental disturbance indicators; µ i is the influence factor of the i-th environmental disturbance indicator; j i is the reference value of the i-th environmental disturbance indicator in the current monitoring period.

[0033] Specifically, normalize all parameters in the model through the preset first fixed coefficient, second fixed coefficient, and third fixed coefficient, so that each parameter is within the same value range.

[0034] Specifically, the environmental disturbance index is set based on the fuel trading environment parameters within the current monitoring period, including but not limited to, fuel price trends, fuel transportation cost change trends, transportation duration change trends of transportation routes, fuel inventory cost change trends, and other parameters.

[0035] Specifically, the larger the coal consumption evaluation value, the more inventory fuel needs to be ensured in the power plant during the current monitoring period to avoid interference caused by external environment changes. For example, problems such as increased procurement prices due to price increases and increased transportation duration caused by blocked transportation routes, reducing the overall cost of fuel and ensuring the stable operation of the power plant.

[0036] Specifically, the smaller the coal consumption evaluation value, the less inventory fuel needs to be maintained during the current monitoring period, thereby reducing the overall inventory pressure.

[0037] It can be understood that in the above embodiments, by generating a coal consumption prediction curve, the safety inventory value of fuel within a single monitoring period is set, and by periodically updating the coal consumption prediction curve and combining real-time fuel trading environment parameters, the safety inventory value is dynamically adjusted to improve the turnover efficiency of inventory fuel, avoid quality degradation of fuel caused by long-term accumulation, thereby improving the inventory management efficiency of power plant fuel and reducing the overall cost of fuel.

[0038] In the preferred embodiment of the present application, determining whether to generate a warning instruction includes: Obtaining the fuel inventory parameters at the current feedback time node; Generating the remaining fuel quantity k at the current feedback time node according to the fuel inventory parameters; Generating an actual coal consumption quantity sequence D, D = (d1, d2…d i …d n1 ), where n1 is the number of time intervals between the start time node of the current monitoring period and the current feedback time node; d i is the actual coal consumption value in the i-th time interval within the current monitoring period; Generating a coal consumption deviation value f; Determining whether to generate a correction instruction according to the coal consumption deviation value f; Determining whether to correct the primary safety inventory value K1 and the secondary safety inventory value K2 according to the correction instruction; Setting a correction coefficient m according to the coal consumption deviation value f; Determining whether to generate a warning instruction according to the correction coefficient m and the remaining fuel quantity k.

[0039] Specifically, generating the coal consumption deviation value f includes: f = e4 * Q4 * (di -b i) 2 +e5*Q5* Y(i)*(d i -b i) ; Among them, e4 is a preset fourth weight coefficient; e5 is a preset fifth weight coefficient; Q4 is a preset fourth fixed coefficient; Q5 is a preset fifth fixed coefficient; Y(i) is a selection coefficient; if (d i -b i ) > 0, Y(i) = 1; if (d i -b i ) < 0, Y(i) = 0.

[0040] Specifically, the normalization processing of all parameters in the model is carried out through the preset fourth fixed coefficient and fifth fixed coefficient, so that each parameter in the model is within the same value range.

[0041] Specifically, the larger the coal consumption evaluation value is, it indicates that the current power plant has a greater demand for coal consumption, and shows an increasing trend, and the corresponding correction coefficient is smaller.

[0042] Specifically, judging whether to generate a warning instruction includes: If m*k ≥ K2, no warning instruction is generated at the current feedback time node; If K1 ≤ m*k < K2, a first-level warning instruction is generated at the current feedback time node; If m*k < K1, a second-level warning instruction is generated at the current feedback time node.

[0043] Specifically, the first-level warning instruction means that the current fuel inventory has fallen below the safety threshold, and the speed of replenishing fuel is lower than the coal consumption speed, and the replenishment operation needs to be completed within the preset order cycle. The second-level warning instruction means that the current fuel inventory cannot meet the stable operation of the power plant, and there may be a problem of fuel shortage, and replenishment operation needs to be carried out immediately.

[0044] It can be understood that in the above embodiments, by collecting the operation data of the power plant, the fuel inventory in the power plant is monitored in real time, and by analyzing the actual coal consumption, the coal consumption prediction curve and the safety inventory value are corrected in time, so as to avoid the problem that the inventory shortage affects the operation of the power plant, thereby improving the overall cost of fuel.

[0045] In the preferred embodiment of the embodiment of the present application, when judging whether to generate a correction instruction, it includes: Presetting a first deviation evaluation value threshold F1 and a second deviation evaluation value threshold F2; If f < F1, no correction instruction is generated at the current feedback time node; If F1 ≤ f < F2, a first-level correction instruction is generated at the current feedback time node, and the coal consumption prediction curve within the current monitoring period is updated according to the first-level correction instruction. If f ≥ F2, a second-level correction instruction is generated at the current feedback time node, and the coal consumption prediction model is corrected according to the second-level correction instruction.

[0046] Specifically, the first-level correction instruction means that the deviation between the coal consumption prediction curve within the current monitoring period and the actual coal consumption curve is too large, the fuel consumption speed exceeds the expectation, and it is necessary to re-predict based on the current power plant operation parameters and update the monitoring period.

[0047] The first-level safety inventory value and the second-level safety inventory value are re-established according to the new coal consumption prediction curve.

[0048] Specifically, the second-level correction value means that there is a serious deviation between the coal consumption prediction curve within the current monitoring period and the actual coal consumption, the fuel consumption speed far exceeds the expectation, and the current coal consumption prediction model can no longer provide accurate prediction data. It is necessary to generate new training data based on the operation parameters of the power plant within the current monitoring period combined with historical operation parameters, optimize and iterate the original coal consumption prediction model, and regenerate a new coal consumption prediction curve according to the corrected coal consumption prediction model.

[0049] Based on another preferred embodiment of a power plant fuel inventory warning method in any of the above preferred embodiments, a power plant fuel inventory warning method is provided in this preferred embodiment, including: A central control unit for establishing a coal consumption prediction model and multiple monitoring periods; A monitoring unit for collecting fuel inventory parameters according to a preset feedback time node; The central control unit includes: A first processing module for generating a coal consumption prediction curve within the current monitoring period according to the coal consumption prediction model; A second processing module for setting a safety inventory value according to the coal consumption prediction curve and the environmental parameters of the current monitoring period; An early warning module for judging whether to generate an early warning instruction according to the fuel inventory parameters; A third processing module for setting multiple time intervals within the current monitoring period: Establish a time interval sequence T, T = (t1, t2…t i …t n ), where t i is the i-th time interval; n is the number of time intervals; Set the end time node of each time interval as the feedback time node.

[0050] In the preferred embodiment of the present application, the second processing module is further used for: Generate coal consumption prediction values for each time interval within the current monitoring period according to the coal consumption prediction curve; Establish a sequence B of coal consumption prediction values, B = (b1, b2…b i …b n )), where b i is the coal consumption prediction value for the i-th time interval within the current monitoring period; Set the coal consumption evaluation value c according to the coal consumption prediction value B and the environmental parameters of the current monitoring period; Set the compensation coefficient r according to the coal consumption evaluation value c; Generate the primary safety inventory value K1 for the current monitoring period, K1 = r * k'1, where k'1 is the preset first inventory reference value; Generate the secondary safety inventory value K2 for the current monitoring period, K2 = r * k'2, where k'2 is the preset second inventory reference value; and k'1 < k'2; Among them, generating the coal consumption evaluation value c includes: c = e1 * Q1 * b i + e2 * Q2 * (b i - b') 2 + e3 * Q3 * µ i * j i ) Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; e3 is the preset third weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; Q3 is the preset third fixed coefficient; b' is the average value of all data in the sequence B of coal consumption prediction values; θ is the number of environmental disturbance indicators; µ i is the influence factor of the i-th environmental disturbance indicator; j i is the reference value of the i-th environmental disturbance indicator within the current monitoring period.

[0051] In the preferred embodiment of the present application, it is characterized in that the early warning module is further used for: Obtain the fuel inventory parameters at the current feedback time node; Generate the remaining fuel quantity k at the current feedback time node according to the fuel inventory parameters; Generate a sequence D of actual coal consumption amounts, D = (d1, d2…d i …d n1 ), where n1 is the number of time intervals between the start time node and the current feedback time node of the current monitoring period; d i is the actual coal consumption value for the i-th time interval within the current monitoring period; Generate the coal consumption deviation value f; f = e4 * Q4 * (d i -b i) 2 +e5 * Q5 * Y(i) * (d i -b i) ; Wherein, e4 is a preset fourth weight coefficient; e5 is a preset fifth weight coefficient; Q4 is a preset fourth fixed coefficient; Q5 is a preset fifth fixed coefficient; Y(i) is a selection coefficient; if (d i -b i ) > 0, Y(i) = 1; if (d i -b i ) < 0, Y(i) = 0; Judge whether to generate a correction instruction according to the coal consumption deviation value f; Judge whether to correct the primary safety inventory value K1 and the secondary safety inventory value K2 according to the correction instruction; Set a correction coefficient m according to the coal consumption deviation value f; If m * k ≥ K2, no warning instruction is generated at the current feedback time node; If K1 ≤ m * k < K2, a primary warning instruction is generated at the current feedback time node; If m * k < K1, a secondary warning instruction is generated at the current feedback time node.

[0052] According to the first concept of the present application, by generating a coal consumption prediction curve, the safety inventory value of the fuel within a single monitoring period is set, and by periodically updating the coal consumption prediction curve and combining real-time fuel trading environment parameters, the safety inventory value is dynamically adjusted, improving the turnover efficiency of the inventory fuel, avoiding the quality decline of the fuel caused by long-term accumulation, thereby improving the inventory management efficiency of the power plant fuel and reducing the overall cost of the fuel.

[0053] According to the second concept of the present application, by collecting the operation data of the power plant, the fuel inventory in the power plant is monitored in real time, and by analyzing the actual coal consumption, the coal consumption prediction curve and the safety inventory value are corrected in a timely manner, avoiding the problem that the inventory shortage affects the operation of the power plant, thereby improving the overall cost of the fuel.

[0054] The above are only the preferred embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present application, several improvements and substitutions can be made, and these improvements and substitutions should also be regarded as the protection scope of the present application.

Claims

1. A power plant fuel inventory early warning method, characterized in that: Including: Establish a coal consumption prediction model and multiple monitoring cycles; Generate a coal consumption prediction curve for the current monitoring cycle according to the coal consumption prediction model, and set a safety inventory value according to the coal consumption prediction curve and the environmental parameters of the current monitoring cycle; Obtain fuel inventory parameters according to a preset feedback time node, and determine whether to generate a warning instruction; Among them, the preset feedback time node includes: Set multiple time intervals within the current monitoring cycle: Establish a time interval sequence T, T=(t1, t2…t i …t n ), where t i is the i-th time interval; n is the number of time intervals; Set the end time node of each time interval as the feedback time node.

2. The power plant fuel inventory early warning method according to claim 1, characterized in that: Setting the safety inventory value includes: Generate coal consumption prediction values for each time interval within the current monitoring cycle according to the coal consumption prediction curve; Establish coal consumption prediction value series B, B=(b1,b2…b i …b n ), where b i is the coal consumption forecast value of the ith time interval in the current monitoring period; Set a coal consumption evaluation value c according to the coal consumption prediction value B and the environmental parameters of the current monitoring cycle; Set a compensation coefficient r according to the coal consumption evaluation value c; Generate a primary safety inventory value K1 for the current monitoring cycle, K1 = r * k'1, where k'1 is a preset first inventory reference value; Generate a secondary safety inventory value K2 for the current monitoring cycle, K2 = r * k'2, where k'2 is a preset second inventory reference value; and k'1 < k'2.

3. The power plant fuel inventory early warning method according to claim 2, characterized in that: Generating the coal consumption evaluation value c includes: c=e1*Q1* b i ]+e2*Q2* (b i -b') 2 ]+e3*Q3*[ µ i *j i ] Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; e3 is the preset third weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; Q3 is the preset third fixed coefficient; b' is the average value of all data in the coal consumption prediction value series B; θ is the number of environmental disturbance indicators; µ i is the influencing factor of the i-th environmental disturbance index; j i It is the reference value of the i-th environmental disturbance index in the current monitoring period.

4. The power plant fuel inventory early warning method according to claim 3, characterized in that: Determining whether to generate a warning instruction includes: Obtain the fuel inventory parameters at the current feedback time node; Generate the remaining fuel quantity k at the current feedback time node according to the fuel inventory parameters; Generate the actual coal consumption series D, D = (d1, d2…d i …d n1 ), where n1 is the number of time intervals between the start time node of the current monitoring cycle and the current feedback time node; d i is the actual coal consumption value of the i-th time interval in the current monitoring period; Generate a coal consumption deviation value f; Determine whether to generate a correction instruction according to the coal consumption deviation value f; Determine whether to correct the primary safety inventory value K1 and the secondary safety inventory value K2 according to the correction instruction; Set a correction coefficient m according to the coal consumption deviation value f; Determine whether to generate a warning instruction according to the correction coefficient m and the remaining fuel quantity k.

5. The power plant fuel inventory early warning method according to claim 4, characterized in that: Generating the coal consumption deviation value f includes: f=e4*Q4* (d i -b i) 2 ]+e5*Q5* Y(i)*(d i -b i) ]; Among them, e4 is the preset fourth weight coefficient; e5 is the preset fifth weight coefficient; Q4 is the preset fourth fixed coefficient; Q5 is the preset fifth fixed coefficient; Y(i) is the selection coefficient; if (d i -b i )>0,Y(i)=1; if (d i -b i )<0,Y(i)=0.

6. The power plant fuel inventory early warning method according to claim 5, characterized in that: When determining whether to generate a correction instruction, it includes: Preset a first deviation evaluation value threshold F1 and a second deviation evaluation value threshold F2; If f < F1, no correction instruction is generated at the current feedback time node; If F1 ≤ f < F2, generate a primary correction instruction at the current feedback time node, and update the coal consumption prediction curve within the current monitoring cycle according to the primary correction instruction; If f ≥ F2, generate a secondary correction instruction at the current feedback time node, and correct the coal consumption prediction model according to the secondary correction instruction.

7. The power plant fuel inventory early warning method according to claim 5, characterized in that: Determining whether to generate a warning instruction includes: If m * k ≥ K2, no warning instruction is generated at the current feedback time node; If K1 ≤ m * k < K2, generate a primary warning instruction at the current feedback time node; If m * k < K1, generate a secondary warning instruction at the current feedback time node.

8. A power plant fuel inventory early warning system, using the power plant fuel inventory early warning method according to any one of claims 1 to 7, characterized in that: Including: A central control unit for establishing a coal consumption prediction model and multiple monitoring cycles; A monitoring unit for collecting fuel inventory parameters according to a preset feedback time node; The central control unit includes: A first processing module for generating a coal consumption prediction curve for the current monitoring cycle according to the coal consumption prediction model; A second processing module for setting a safety inventory value according to the coal consumption prediction curve and the environmental parameters of the current monitoring cycle; A warning module for determining whether to generate a warning instruction according to the fuel inventory parameters; A third processing module for setting multiple time intervals within the current monitoring cycle: Establish a time interval sequence T, T=(t1, t2…t i …t n ), where t i is the i-th time interval; n is the number of time intervals; Set the end time node of each time interval as the feedback time node.

9. The power plant fuel inventory early warning system according to claim 8, characterized in that: The second processing module is further used for: Generate coal consumption prediction values for each time interval within the current monitoring period according to the coal consumption prediction curve; Establish coal consumption prediction value series B, B=(b1,b2…b i …b n ), where b i is the coal consumption forecast value of the ith time interval in the current monitoring period; Set the coal consumption evaluation value c according to the coal consumption prediction value B and the environmental parameters of the current monitoring period; Set the compensation coefficient r according to the coal consumption evaluation value c; Generate the primary safety inventory value K1 for the current monitoring period, K1 = r * k'1, where k'1 is the preset first inventory reference value; Generate the secondary safety inventory value K2 for the current monitoring period, K2 = r * k'2, where k'2 is the preset second inventory reference value; and k'1 < k'2; Among them, generating the coal consumption evaluation value c includes: c=e1*Q1* b i ]+e2*Q2* (b i -b') 2 ]+e3*Q3*[ µ i *j i ] Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; e3 is the preset third weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; Q3 is the preset third fixed coefficient; b' is the average value of all data in the coal consumption prediction value series B; θ is the number of environmental disturbance indicators; µ i is the influencing factor of the i-th environmental disturbance index; j i It is the reference value of the i-th environmental disturbance index in the current monitoring period.

10. The power plant fuel inventory early warning system according to claim 9, characterized in that: The warning module is also used for: Obtain the fuel inventory parameters at the current feedback time node; Generate the remaining fuel quantity k at the current feedback time node according to the fuel inventory parameters; Generate the actual coal consumption series D, D = (d1, d2…d i …d n1 ), where n1 is the number of time intervals between the start time node of the current monitoring cycle and the current feedback time node; d i is the actual coal consumption value of the i-th time interval in the current monitoring period; Generate the coal consumption deviation value f; f=e4*Q4* (d i -b i) 2 ]+e5*Q5* Y(i)*(d i -b i) ]; Among them, e4 is the preset fourth weight coefficient; e5 is the preset fifth weight coefficient; Q4 is the preset fourth fixed coefficient; Q5 is the preset fifth fixed coefficient; Y(i) is the selection coefficient; if (d i -b i )>0,Y(i)=1; if (d i -b i )<0,Y(i)=0; Judge whether to generate a correction instruction according to the coal consumption deviation value f; Judge whether to correct the primary safety inventory value K1 and the secondary safety inventory value K2 according to the correction instruction; Set the correction coefficient m according to the coal consumption deviation value f; If m * k ≥ K2, no warning instruction is generated at the current feedback time node; If K1 ≤ m * k < K2, a primary warning instruction is generated at the current feedback time node; If m * k < K1, a secondary warning instruction is generated at the current feedback time node.

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