An energy centralized automation management system

Abstract

The present invention discloses an energy centralized automation management system, which relates to the technical field of energy management. By extracting the energy consumption data of the current and multiple groups of historical time windows, forming a data group for analysis, calculating the average energy consumption, the highest and lowest energy consumption amounts, determining the reference energy consumption data in combination with the sharing ratio of the energy consumption balance module, obtaining the energy consumption interval index through weighted calculation, and then predicting the energy consumption range. Presetting an index interval and an adjustment coefficient, matching the trend optimization coefficient according to the fluctuation ratio, and optimizing the predicted energy consumption value to achieve more accurate energy consumption prediction, exploring the law of energy consumption, and providing a basis for optimizing energy management.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and specifically to an energy centralized automation management system. Background Art

[0002] As an energy-intensive industry, the iron and steel industry has a large amount of energy consumption and various types of energy, covering coal, coke, electricity, natural gas, etc.; a large amount of energy is required in each production link, such as sintering, ironmaking, steelmaking, rolling, etc.

[0003] However, in the actual application process of the existing energy centralized automation management system in a steel plant, there are still the following deficiencies:

[0004] Regarding each process in the steel plant, there is a lack of in-depth exploration and prediction ability of energy consumption laws, and it is impossible to conduct targeted analysis based on the energy consumption performance of each process in the recent period, and construct an energy prediction report for each process in the next time period to provide accurate data for energy management in the steel plant;

[0005] At the same time, there is a lack of an effective energy consumption anomaly monitoring, early warning and processing mechanism, and it is impossible to detect energy consumption anomalies in a timely manner. There is a lack of an intelligent processing process when dealing with anomalies, resulting in low processing efficiency.

[0006] Therefore, an energy centralized automation management system is introduced. Summary of the Invention

[0007] The purpose of the present invention is to solve the problems pointed out in the background art, and to propose an energy centralized automation management system.

[0008] The purpose of the present invention can be achieved by the following technical solutions: An energy centralized automation management system, comprising:

[0009] An energy information module: collecting in real time the energy consumption data of each process in the steel plant within a set time window;

[0010] An energy consumption balance module: for different energy consumption media and energy consumption units corresponding to different processes; and then comprehensively considering the supply and consumption of energy in combination with the specific working conditions, and performing energy consumption balance processing; the specific working conditions include normal working conditions and special working conditions;

[0011] Energy consumption assessment module: Receive the energy consumption data of each process in the steel plant within the current set time window, and at the same time extract the energy consumption data of X sets of historical time windows before the current set time window; where X > 5; and comprehensively evaluate the energy consumption data of X sets of historical time windows before the set time window to obtain the energy consumption interval index xy of each process in the steel plant within the next set time window. Based on the energy consumption interval index xy of each process in the steel plant within the next set time window, obtain the energy consumption prediction range of each process in the steel plant within the next set time window, and input the energy consumption prediction range of each process in the steel plant within the next set time window into a pre-constructed report template, so as to generate an energy consumption prediction report of each process in the steel plant within the next set time window and send it to the management personnel;

[0012] Energy consumption warning module: Receive the energy consumption prediction range of each process in the steel plant within the next set time window. If the energy consumption data of a certain process within the next set time window is not within the corresponding energy consumption prediction range, trigger an energy consumption anomaly signal and execute the corresponding steps.

[0013] As a preferred embodiment of the present invention, the specific steps of energy consumption balance processing are as follows:

[0014] Normal working condition: Adopt the method of sharing and balancing based on the proportion of energy supply and actual energy consumption value over time to obtain the specific energy consumption medium supply amount in the steel plant within the current set time window, and the specific actual consumption amount of the energy consumption medium of each process corresponding energy consumption unit; calculate the proportion of the specific actual consumption amount of the energy consumption medium of each process corresponding energy consumption unit to the specific energy consumption medium supply amount to obtain the normal proportion, which is calculated by specific energy consumption medium actual consumption amount / specific energy consumption medium supply amount; and when performing energy consumption allocation subsequently, allocate according to the normal proportion calculated by each process corresponding energy consumption unit in the specific energy consumption medium;

[0015] Special working condition: When encountering a special working condition, obtain the specific process energy consumption unit corresponding to the special working condition, and extract the additional energy consumption medium consumption amount of the specific process energy consumption unit corresponding to the special working condition; and accumulate the additional energy consumption medium consumption amount with the specific energy consumption medium supply amount to obtain the corrected supply amount; calculate the proportion of the specific actual consumption amount of the energy consumption medium of each process corresponding energy consumption unit to the corrected supply amount to obtain the special proportion; which is calculated by specific energy consumption medium actual consumption amount / corrected supply amount; and when performing energy consumption allocation subsequently, allocate according to the special proportion calculated by each process corresponding energy consumption unit in the specific energy consumption medium.

[0016] As a preferred embodiment of the present invention, obtaining the energy consumption interval index xy of each process in the steel plant within the next set time window is specifically as follows:

[0017] Extract the energy consumption data of each process in the steel plant within the current set time window and the energy consumption data of X groups of historical time windows to form an energy consumption data group corresponding to each process in the steel plant. Calculate the average value of each group of energy consumption data in the energy consumption data group corresponding to each process to obtain the average energy consumption volume corresponding to each process respectively;

[0018] At the same time, extract the highest energy consumption volume and the lowest energy consumption volume from the energy consumption data group of each process respectively, and mark the average energy consumption volume, the highest energy consumption volume and the lowest energy consumption volume of each process as xg1, xg2 and xg3 respectively;

[0019] Set the reference energy consumption data corresponding to each process as xf. Multiply the total supply volume by the sharing ratio calculated for each process in the energy consumption balance module, and use the multiplication result as the reference energy consumption data;

[0020] According to the formula Perform weighted calculation on the average energy consumption volume xg1, the highest energy consumption volume xg2 and the lowest energy consumption volume xg3 corresponding to each process respectively, so as to obtain the energy consumption interval index xy corresponding to each process in the next set time window; where a1, a2 and a3 are the influence weight factors corresponding to the average energy consumption volume xg1, the highest energy consumption volume xg2 and the lowest energy consumption volume xg3 respectively.

[0021] As a preferred implementation mode of the present invention, obtain the energy consumption prediction range of each process in the steel plant in the next set time window, specifically:

[0022] Preset the intervals where each group of indexes corresponding to the energy consumption interval index of each process are located, and set an adjustment coefficient corresponding to each interval where each group of indexes of each process is located; match the energy consumption interval index xy corresponding to each process in the next set time window with the corresponding index interval, so as to obtain the adjustment coefficient corresponding to each process in the next set time window;

[0023] Extract the reference energy consumption data xf corresponding to each process, and multiply it by the corresponding matched adjustment coefficient to obtain the adjusted predicted energy consumption value of each process in the next set time window.

[0024] As a preferred implementation mode of the present invention, obtaining the energy consumption prediction range of each process in the steel plant in the next set time window further includes:

[0025] From the energy consumption data groups corresponding to each process in the steel plant, extract the energy consumption data of each process in different time windows, and arrange them in chronological order. After the arrangement, calculate the difference between adjacent two groups of energy consumption data of each process, which is calculated by the energy consumption data on the right side of adjacent two groups - the energy consumption data on the left side of adjacent two groups. If the difference is negative, take the absolute value as the energy decrease value; if the difference is positive, take it as the energy increase value;

[0026] Sum up the energy increase values and energy decrease values of each group respectively to obtain the total energy appreciation value and total energy decrease value of each process; for the total energy appreciation value and total energy decrease value of each process, calculate the energy fluctuation ratio of each process by the ratio of total energy appreciation value / total energy decrease value;

[0027] Preset the value ranges of each group of ratios corresponding to the fluctuation ratios of each process, and set a trend optimization coefficient corresponding to each group of ratio value ranges of each process;

[0028] Extract the adjusted predicted energy consumption values of each process in the next set time window, and multiply them by the corresponding trend optimization coefficients obtained by matching to obtain the predicted optimized energy consumption values of each process in the next set time window;

[0029] At the same time, extract the reference energy consumption data corresponding to each process, and integrate it with the corresponding predicted optimized energy consumption value to obtain the energy consumption prediction range of each process in the steel plant in the next set time window. The energy consumption prediction range of each process is from the reference energy consumption data to the predicted optimized energy consumption value.

[0030] As a preferred embodiment of the present invention, trigger an energy consumption anomaly signaling and execute the corresponding steps, specifically:

[0031] M1: Extract the specific energy consumption data of the process corresponding to the triggered energy consumption anomaly signaling. If it is higher than the corresponding energy consumption prediction range, calculate the difference between the energy consumption data and the highest value within the corresponding energy consumption prediction range to obtain the energy consumption prediction excess value;

[0032] If it is lower than the corresponding energy consumption prediction range, calculate the difference between the energy consumption data and the lowest value within the corresponding energy consumption prediction range to obtain the energy consumption prediction lower value;

[0033] Preset the excess threshold and lower threshold corresponding to the energy consumption prediction excess value and energy consumption prediction lower value of each process respectively;

[0034] If the low value or the exceeded value of the energy consumption prediction corresponding to the process where the energy consumption anomaly signal is triggered is greater than the corresponding low threshold or exceeded threshold, then step M2 is executed; otherwise, it is sent to the management personnel, and the process corresponding to the energy consumption anomaly signal is monitored in the next set time window after the time point when the energy anomaly signal is triggered. If the energy consumption anomaly signal is still triggered, step M2 is also executed;

[0035] M2: Identify the location of the process corresponding to the energy consumption anomaly signal. Draw a circle with the location as the center and a set distance as the radius, and screen all technicians within the circle as the anomaly handlers;

[0036] Analyze the selection preference evaluation index gkr of each anomaly handler, and select the anomaly handler with the largest preference evaluation index gkr as the handler for the current process corresponding to the triggered energy consumption anomaly signal; and send the location of the process corresponding to the triggered energy consumption anomaly signal to the handler.

[0037] As a preferred implementation manner of the present invention, analyzing the selection preference evaluation index gkr of each anomaly handler specifically includes:

[0038] Obtain the distance between each anomaly handler and the location; at the same time, obtain the historical processing times of each anomaly handler, and extract the same ones as the process corresponding to the triggered energy consumption anomaly signal from the specific types of processes corresponding to each processing in the historical processing times and count the times as the triggered process processing times;

[0039] Obtain the duration used for each processing in the triggered process processing times; and calculate the average value to obtain the average solution time of each anomaly handler;

[0040] Mark the distance, the triggered process processing times, and the average solution time of each anomaly handler as gh1, gh2, and gh3 respectively;

[0041] Extract the low value or the exceeded value of the energy consumption prediction corresponding to the triggered energy consumption anomaly signal, and calculate the difference from the corresponding low threshold or exceeded threshold, energy consumption prediction low value - low threshold or energy consumption prediction exceeded value - exceeded threshold, and mark the calculated difference as the anomaly degree value;

[0042] Preset the value range of each group of degree values corresponding to the anomaly degree value of each process, and set that each value range of each group of degree values corresponds to a reference value set; where the reference value set includes a reference distance, a reference triggered process processing times, and a reference average solution time;

[0043] Match the abnormal degree value of the process corresponding to the currently triggered energy consumption abnormal signal with the corresponding degree value range to obtain the reference distance, the reference number of triggered process treatments, and the reference average solution time for the process corresponding to the currently triggered energy consumption abnormal signal, and mark them as gy1, gy2, and gy3 respectively;

[0044] According to the formula Perform weighted calculation on the distance gh1, the number of triggered process treatments gh2, and the average solution time gh3 of each abnormal handler to obtain the preferred evaluation index gkr of each abnormal handler; where c1, c2, and c3 are the influence weight factors corresponding to the distance gh1, the number of triggered process treatments gh2, and the average solution time gh3 respectively.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] By extracting the energy consumption data of the current and multiple groups of historical time windows, forming a data group for analysis, calculating the average energy consumption, the highest and lowest energy consumption, determining the reference energy consumption data in combination with the sharing ratio of the energy consumption balance module, obtaining the energy consumption interval index through weighted calculation, and then predicting the energy consumption range. Preset the index interval and adjustment coefficient, match the trend optimization coefficient according to the fluctuation ratio, optimize the predicted energy consumption value, realize more accurate energy consumption prediction, explore the law of energy consumption, and provide a basis for optimizing energy management;

[0047] By receiving the energy consumption prediction range, comparing with the actual energy consumption data, timely discovering energy consumption anomalies and triggering energy consumption abnormal signals, calculating the difference between the predicted energy consumption exceeding or falling below the threshold value and comparing with the threshold value, quantifying the degree of anomaly, continuously monitoring minor anomalies, and promptly handling serious anomalies to avoid energy waste and equipment damage;

[0048] By screening nearby technicians according to the process location, comprehensively considering factors such as the distance, the historical number of treatments, and the time used for treatment, calculating the preferred evaluation index, selecting the person with the largest index to handle the anomaly, and at the same time, matching the reference value set according to the degree of anomaly to ensure that the selection of the handler is suitable for the degree of anomaly, improving the efficiency of anomaly handling and reducing losses;

[0049] By performing energy consumption balance processing for different energy consumption media and different process energy consumption units under normal and special working conditions. Under normal working conditions, it is shared according to the ratio of the energy supply and the actual energy consumption value over time; for special working conditions, considering the additional consumption of energy consumption media, calculating the corrected supply amount and then sharing according to the special ratio to ensure reasonable energy consumption allocation for each process and solve the problem of energy consumption balance. Description of the Drawings

[0050] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the drawings.

[0051] Figure 1 This is the principle block diagram of the present invention. Specific embodiments

[0052] Next, the technical solutions of the present invention will be clearly and completely described in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0053] Please refer to Figure 1 As shown, an energy centralized automation management system includes an energy information module, an energy consumption balance module, an energy consumption assessment module, and an energy consumption warning module;

[0054] Energy information module: Deploy intelligent meters in the energy consumption units of each process in the steel plant; for example, sintering, ironmaking, steelmaking, and rolling, etc.; the intelligent meters have been strictly screened and tested, with high-precision data acquisition capabilities, and can accurately obtain the energy consumption information of each process energy consumption unit during operation, such as the electricity consumption, gas flow, etc.; real-time collection of the energy consumption data of each process in the steel plant within a set time window; the set time window can be set to 24h, that is, the energy consumption situation will be comprehensively sorted out and adjusted once a day;

[0055] Energy consumption balance module: For different energy consumption media and the energy consumption units corresponding to different processes; the energy consumption media include electricity, natural gas, steam, etc.; combined with the specific situation, comprehensively consider the supply and consumption of energy, and perform energy consumption balance processing; the specific situation includes normal working conditions and special working conditions;

[0056] Specifically:

[0057] Normal working conditions: Adopt the method of sharing and balancing based on the energy supply in time and the proportion of the actual energy consumption value, that is, obtain the specific energy consumption medium supply amount in the steel plant within the current set time window, and the specific energy consumption medium actual consumption amount of each process corresponding energy consumption unit;

[0058] Calculate the proportion of the specific energy consumption medium actual consumption amount of each process corresponding energy consumption unit in the specific energy consumption medium supply amount to obtain the normal proportion, that is, calculate by specific energy consumption medium actual consumption amount / specific energy consumption medium supply amount;

[0059] And during subsequent energy consumption allocation, allocate according to the normal proportion calculated by each process corresponding energy consumption unit in the specific energy consumption medium;

[0060] Ensure that a reasonable allocation is obtained among the energy consumption units corresponding to each process according to the actual consumption situation.

[0061] Special working conditions: When encountering special working conditions, including but not limited to sudden factors such as equipment failures and temporary production adjustments, which cause abnormal energy consumption in the energy consumption units corresponding to a certain process. Obtain the specific process energy consumption units corresponding to the special working conditions, and extract the additional energy consumption medium consumption of the specific process energy consumption units corresponding to the special working conditions.

[0062] Accumulate the additional energy consumption medium consumption and the specific energy consumption medium supply volume to obtain the corrected supply volume.

[0063] Calculate the proportion of the actual consumption of the specific energy consumption medium of the energy consumption units corresponding to each process in the corrected supply volume to obtain the special proportion; that is, calculate according to the actual consumption of the specific energy consumption medium / the corrected supply volume.

[0064] And when performing energy consumption allocation subsequently, allocate according to the special proportion calculated by the energy consumption units corresponding to each process in the specific energy consumption medium.

[0065] To cope with the energy consumption changes brought about by special working conditions and ensure the stable operation of the energy system of the entire steel plant.

[0066] The above is illustrated by an example.

[0067] For example, assume that the total electricity supply volume of a steel plant in a day is 10,000 degrees, and the actual electricity consumption of the energy consumption units corresponding to processes A, B, and C are 3,000 degrees, 4,000 degrees, and 3,000 degrees respectively.

[0068] Calculate the conventional proportion of each partition unit:

[0069] Conventional proportion of partition unit A = 3000 ÷ 10000 = 0.3;

[0070] Conventional proportion of partition unit B = 4000 ÷ 10000 = 0.4;

[0071] Conventional proportion of partition unit C = 3000 ÷ 10000 = 0.3;

[0072] If the energy consumption unit corresponding to process C has an unexpected equipment failure and there is additional energy loss in its sub - pipe line. Assume that this failure causes the energy consumption unit corresponding to process C to consume an additional 500 degrees of electricity. Originally expected to consume 3000 degrees, and now the actual consumption becomes 3500 degrees.

[0073] When recalculating the sharing ratio at this time, the special working conditions of the sub - pipe of the energy consumption unit corresponding to process C need to be considered. Taking electricity as an example, the recalculated special proportion is:

[0074] Special proportion of partition unit A = 3000 ÷ (3000 + 4000 + 3500) ≈ 0.286;

[0075] Special proportion of B partition unit = 4000÷(3000 + 4000 + 3500) ≈ 0.381;

[0076] Special proportion of C partition unit = 3500÷(3000 + 4000 + 3500) ≈ 0.333;

[0077] Energy consumption assessment module: Receive the energy consumption data of each process in the steel plant within the current set time window, and at the same time extract the energy consumption data of X sets of historical time windows before the current set time window; where X > 5, and the specific value is set by technical personnel; and comprehensively evaluate to obtain the energy consumption interval index xy of each process in the steel plant within the next set time window. Based on the energy consumption interval index xy of each process in the steel plant within the next set time window, obtain the energy consumption prediction range of each process in the steel plant within the next set time window, and input the energy consumption prediction range of each process in the steel plant within the next set time window into the pre-constructed report template, so as to generate the energy consumption prediction report of each process in the steel plant within the next set time window and send it to the management personnel;

[0078] Specifically:

[0079] Extract the energy consumption data of each process in the steel plant within the current set time window and the energy consumption data of X sets of historical time windows to form an energy consumption data group corresponding to each process in the steel plant, calculate the average value of each set of energy consumption data in the energy consumption data group corresponding to each process, and obtain the average energy consumption corresponding to each process;

[0080] At the same time, extract the highest energy consumption and the lowest energy consumption from the energy consumption data group of each process respectively, and mark the average energy consumption, the highest energy consumption and the lowest energy consumption of each process as xg1, xg2 and xg3 respectively;

[0081] Set the reference energy consumption data corresponding to each process as xf, multiply the total supply amount by the sharing ratio calculated in the energy consumption balance module, and use the multiplication result as the reference energy consumption data;

[0082] According to the formula Perform weighted calculation on the average energy consumption xg1, the highest energy consumption xg2 and the lowest energy consumption xg3 corresponding to each process respectively, so as to obtain the energy consumption interval index xy corresponding to each process within the next set time window; where a1, a2 and a3 are the influence weight factors corresponding to the average energy consumption xg1, the highest energy consumption xg2 and the lowest energy consumption xg3 respectively;

[0083] It should be noted that by performing weighted calculations on the average energy consumption, the maximum energy consumption, and the minimum energy consumption according to the formula, the energy consumption interval index can be obtained, which can more accurately predict the energy consumption interval of each process within the next set time window; this prediction function helps the steel plant to make good energy plans in advance, reasonably arrange energy procurement, equipment operation, and production plans, which can not only avoid energy waste but also prevent production from being affected by insufficient energy supply.

[0084] Preset the intervals where each group of indices corresponding to the energy consumption interval indices of each process are located, and set that each interval where each group of indices of each process is located corresponds to an adjustment coefficient; the range of the adjustment coefficient is set between 0.841 - 1.285, and specific settings are made for each process, and the higher the energy consumption interval index, the higher the corresponding adjustment coefficient obtained; match the energy consumption interval index xy corresponding to each process within the next set time window with the corresponding index interval, so as to obtain the adjustment coefficient corresponding to each process within the next set time window;

[0085] Extract the reference energy consumption data xf corresponding to each process, and multiply it by the corresponding obtained adjustment coefficient to obtain the adjusted predicted energy consumption value of each process within the next set time window;

[0086] It should be noted that presetting the index intervals corresponding to the energy consumption interval indices of each process and matching the adjustment coefficients can finely adjust the energy consumption prediction according to different energy consumption interval index situations. Since the higher the energy consumption interval index, the higher the corresponding adjustment coefficient, this differential setting fully considers different degrees of energy consumption changes, making the finally calculated adjusted predicted energy consumption value more in line with the actual possible energy consumption situation. Compared with the simple original prediction, it can provide a more accurate energy consumption prediction result, providing a reliable basis for the energy planning and management of the steel plant.

[0087] Extract the energy consumption data of each process in different time windows from the energy consumption data groups corresponding to each process in the steel plant, and arrange them in chronological order. After the arrangement is completed, calculate the difference between adjacent two groups of energy consumption data of each process, which is calculated by the energy consumption data on the right of adjacent two groups - the energy consumption data on the left of adjacent two groups. If the difference is negative, take the absolute value as the energy decrease value, and if the difference is positive, take it as the energy increase value;

[0088] Sum up the energy increase values and energy decrease values of each group respectively to obtain the total energy increase value and total energy decrease value of each process; for the total energy increase value and total energy decrease value of each process, calculate the fluctuation ratio of each process through the ratio, that is, the total energy increase value / the total energy decrease value;

[0089] Preset the value range of each group of ratios corresponding to the fluctuation ratio of each process, and set that each group of ratio value ranges of each process corresponds to a trend optimization coefficient; the range of the trend optimization coefficient is set between 0.835 - 1.147, and specific settings are made for each process, and the higher the fluctuation ratio, the higher the corresponding trend optimization coefficient obtained;

[0090] Extract the adjusted predicted energy consumption values of each process within the next set time window, and multiply them by the corresponding trend optimization coefficients obtained by matching to get the predicted optimized energy consumption values of each process within the next set time window;

[0091] At the same time, extract the reference energy consumption data corresponding to each process, and integrate it with the corresponding predicted optimized energy consumption value to obtain the energy consumption prediction range of each process in the steel plant within the next set time window. The energy consumption prediction range of each process is from the reference energy consumption data to the predicted optimized energy consumption value;

[0092] It should be noted that the value range corresponding to the fluctuation ratio of each process is preset, and a trend optimization coefficient is matched for each group of value ranges; since the higher the fluctuation ratio, the higher the corresponding trend optimization coefficient, such a setting can adjust the energy consumption prediction more accurately according to the degree of energy consumption fluctuation; by multiplying the adjusted predicted energy consumption value by the trend optimization coefficient to obtain the predicted optimized energy consumption value, the energy consumption prediction can better fit the actual energy consumption fluctuation trend of each process, improving the accuracy and reliability of the energy consumption prediction.

[0093] Energy consumption warning module: Receive the energy consumption prediction range of each process in the steel plant within the next set time window. If the energy consumption data of a certain process within the next set time window is not within the corresponding energy consumption prediction range, trigger an energy consumption anomaly signal and execute the corresponding steps;

[0094] Specifically:

[0095] M1: Extract the specific energy consumption data of the process corresponding to the triggered energy consumption anomaly signal. If it is higher than the corresponding energy consumption prediction range, calculate the difference between the energy consumption data and the highest value within the corresponding energy consumption prediction range to obtain the energy consumption prediction excess value;

[0096] If it is lower than the corresponding energy consumption prediction range, calculate the difference between the energy consumption data and the lowest value within the corresponding energy consumption prediction range to obtain the energy consumption prediction lower value;

[0097] Preset the excess threshold and the lower threshold corresponding to the energy consumption prediction excess value and the energy consumption prediction lower value of each process respectively;

[0098] If the low - value or high - value of the energy consumption prediction corresponding to the process where the energy consumption anomaly signal is triggered is greater than the corresponding low - value threshold or high - value threshold, then step M2 is executed. Otherwise, it is sent to the management staff, and the process corresponding to the energy consumption anomaly signal is monitored within the next set time window after the time point when the energy anomaly signal is triggered. If the energy consumption anomaly signal is still triggered, step M2 is also executed;

[0099] M2: Identify the location of the process corresponding to the energy consumption anomaly signal. Draw a circle with the location as the center and a set distance as the radius, and screen all technicians within the circle as anomaly handlers; obtain the distance between each anomaly handler and the location;

[0100] At the same time, obtain the historical handling times of each anomaly handler. From the specific process types corresponding to each handling in the historical handling times, extract those that are the same as the process corresponding to the energy consumption anomaly signal triggered and count the number of times as the triggered process handling times;

[0101] Obtain the time taken for each handling in the triggered process handling times; the time taken is timed from when the technician arrives at the process location until the handling is completed; and calculate the average value to obtain the average solution time of each anomaly handler;

[0102] Mark the distance, triggered process handling times, and average solution time of each anomaly handler as gh1, gh2, and gh3 respectively;

[0103] Extract the low - value or high - value of the energy consumption prediction corresponding to the energy consumption anomaly signal triggered, and calculate the difference from the corresponding low - value threshold or high - value threshold, that is, energy consumption low - value - low - value threshold or energy consumption high - value - high - value threshold, and mark the calculated difference as the anomaly degree value;

[0104] Preset the value range of each group of degree values corresponding to the anomaly degree value of each process, and set that each group of degree value ranges corresponds to a reference value set; where the reference value set includes reference distance, reference triggered process handling times, and reference average solution time; the higher the anomaly degree value, the shorter the corresponding reference distance, the higher the triggered process handling times, and the shorter the reference average solution time in the reference value set;

[0105] Match the anomaly degree value of the current process corresponding to the triggered energy consumption anomaly signal with the corresponding degree value range, so as to obtain the reference distance, reference triggered process handling times, and reference average solution time of the current process corresponding to the triggered energy consumption anomaly signal, and mark them as gy1, gy2, and gy3 respectively;

[0106] According to the formula Perform a weighted calculation on the travel distance gh1, the number of trigger process treatments gh2, and the average resolution time gh3 of each exception handler to obtain the preferred evaluation index gkr of each exception handler; where c1, c2, and c3 are the influence weight factors corresponding to the travel distance gh1, the number of trigger process treatments gh2, and the average resolution time gh3, respectively.

[0107] Select the exception handler with the largest preferred evaluation index gkr as the handler for the process corresponding to the currently triggered energy consumption exception signal; and send the location of the process corresponding to the triggered energy consumption exception signal to the handler.

[0108] It should be noted that the energy consumption prediction ranges of each process are received in real time and compared with the actual energy consumption data. Once the energy consumption data of a certain process exceeds the prediction range, an energy consumption exception signal is immediately triggered, which can timely detect energy consumption anomalies and avoid potential problems such as energy waste and equipment damage caused by undetected energy consumption anomalies, ensuring the stability of energy use in the production process of the steel plant.

[0109] Preset a set of reference values corresponding to the exception level values of each process. The higher the exception level, the shorter the corresponding reference travel distance, the higher the number of trigger process treatments, and the shorter the reference average resolution time, so that the selection of the handler matches the exception level. In the case of a high exception level, the most suitable handler can be quickly matched to ensure that the exception problem is solved timely and effectively, reducing the losses caused by abnormal energy consumption.

[0110] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only the specific implementation manners. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An energy centralized automation management system, characterized in that, Including: Energy information module: Real-time collect the energy consumption data of each process in the steel plant within the set time window. Energy consumption balance module: For different energy consumption media and the energy consumption units corresponding to different processes; combined with the specific working conditions, comprehensively consider the supply and consumption of energy, and perform energy consumption balance processing; the specific working conditions include normal working conditions and special working conditions. The specific steps of the energy consumption balance processing are as follows: Normal working conditions: Adopt the method of sharing and balancing based on the energy supply over time and the proportion of the actual energy consumption value to obtain the specific energy consumption medium supply volume in the steel plant within the current set time window, and the specific actual consumption volume of the energy consumption medium of each process corresponding energy consumption unit; calculate the proportion of the specific actual consumption volume of the energy consumption medium of each process corresponding energy consumption unit to the specific energy consumption medium supply volume to obtain the normal proportion, calculated as specific actual consumption volume of energy consumption medium / specific energy consumption medium supply volume; and when performing energy consumption allocation subsequently, allocate according to the normal proportion calculated by each process corresponding energy consumption unit in the specific energy consumption medium. Special working conditions: In case of special working conditions, obtain the specific process energy consumption unit corresponding to the special working conditions, and extract the additional energy consumption medium consumption volume of the specific process energy consumption unit corresponding to the special working conditions; and accumulate the additional energy consumption medium consumption volume with the specific energy consumption medium supply volume to obtain the corrected supply volume; calculate the proportion of the specific actual consumption volume of the energy consumption medium of each process corresponding energy consumption unit to the corrected supply volume to obtain the special proportion; calculated as specific actual consumption volume of energy consumption medium / corrected supply volume; and when performing energy consumption allocation subsequently, allocate according to the special proportion calculated by each process corresponding energy consumption unit in the specific energy consumption medium. Energy consumption evaluation module: Receive the energy consumption data of each process in the steel plant within the current set time window, and at the same time extract the X sets of historical time window energy consumption data before the current set time window; where X > 5; and comprehensively evaluate the X sets of historical time window energy consumption data before the set time window to obtain the energy consumption interval index xy of each process in the steel plant within the next set time window. Based on the energy consumption interval index xy of each process in the steel plant within the next set time window, obtain the energy consumption prediction range of each process in the steel plant within the next set time window, and input the energy consumption prediction range of each process in the steel plant within the next set time window into the pre-constructed report template, so as to generate the energy consumption prediction report of each process in the steel plant within the next set time window and send it to the management personnel. Obtain the energy consumption interval index xy of each process in the steel plant within the next set time window, specifically: Extract the energy consumption data of each process in the steel plant within the current set time window and the energy consumption data of X sets of historical time windows to form the energy consumption data group corresponding to each process in the steel plant, and calculate the average value of each group of energy consumption data in the energy consumption data group corresponding to each process to obtain the average energy consumption volume corresponding to each process. Extract the maximum energy consumption and the minimum energy consumption from the energy consumption data groups of each process respectively, and mark the average energy consumption, the maximum energy consumption and the minimum energy consumption of each process as xg1, xg2 and xg3 respectively; Set the reference energy consumption data corresponding to each process as xf. Multiply the total supply quantity by the apportionment ratio calculated for each process in the energy consumption balance module, and use the multiplication result as the reference energy consumption data; According to the formula , weighted calculations are performed on the average energy consumption xg1, the maximum energy consumption xg2, and the minimum energy consumption xg3 corresponding to each process, so as to obtain the energy consumption interval index xy corresponding to each process within the next set time window; where a1, a2, and a3 are the influence weight factors corresponding to the average energy consumption xg1, the maximum energy consumption xg2, and the minimum energy consumption xg3 respectively; Obtain the energy consumption prediction range of each process in the steel plant in the next set time window, specifically: Preset the intervals where the groups of indices corresponding to the energy consumption interval indices of each process are located, and set an adjustment coefficient for each interval where the group of indices of each process is located; Match the energy consumption interval index xy corresponding to each process in the next set time window with the corresponding index interval, so as to obtain the adjustment coefficient corresponding to each process in the next set time window; Extract the reference energy consumption data xf corresponding to each process, and multiply it by the corresponding matched adjustment coefficient to obtain the adjusted predicted energy consumption value of each process in the next set time window; Energy consumption warning module: Receive the energy consumption prediction range of each process in the steel plant in the next set time window. If the energy consumption data of a certain process in the next set time window is not within the corresponding energy consumption prediction range, trigger an energy consumption anomaly signal and execute the corresponding steps.

2. An energy centralized automation management system according to claim 1, characterized in that, Obtaining the energy consumption prediction range of each process in the steel plant in the next set time window also includes: Extract the energy consumption data of each process in different time windows from the energy consumption data groups corresponding to each process in the steel plant, and arrange them in chronological order. After the arrangement, calculate the difference between the adjacent two groups of energy consumption data of each process, which is calculated by the energy consumption data on the right of the adjacent two groups - the energy consumption data on the left of the adjacent two groups. If the difference is negative, take the absolute value as the energy decrease value. If the difference is positive, take it as the energy increase value; Sum up the energy increase values and energy decrease values of each group respectively to obtain the total energy increase value and total energy decrease value of each process; For the total energy increase value and total energy decrease value of each process, calculate the fluctuation ratio of each process by the ratio of total energy increase value / total energy decrease value; Preset the value ranges of the groups of ratios corresponding to the fluctuation ratios of each process, and set a trend optimization coefficient for each value range of the groups of ratios of each process; Extract the adjusted predicted energy consumption value of each process in the next set time window, and multiply it by the corresponding matched trend optimization coefficient to obtain the predicted optimized energy consumption value of each process in the next set time window; At the same time, extract the reference energy consumption data corresponding to each process, and integrate it with the corresponding predicted optimized energy consumption value to obtain the energy consumption prediction range of each process in the steel plant in the next set time window. The energy consumption prediction range of each process is from the reference energy consumption data to the predicted optimized energy consumption value.

3. The energy centralized automation management system according to claim 2, characterized in that, Trigger the energy consumption anomaly signal and execute the corresponding steps, specifically: M1: Extract the specific energy consumption data of the process corresponding to the triggered energy consumption anomaly signal. If it is higher than the corresponding energy consumption prediction range, calculate the difference between the energy consumption data and the highest value within the corresponding energy consumption prediction range to obtain the energy consumption prediction excess value; If it is lower than the corresponding energy consumption prediction range, calculate the difference between the energy consumption data and the lowest value within the corresponding energy consumption prediction range to obtain the energy consumption prediction deficit value; Preset the excess threshold and deficit threshold corresponding to the energy consumption prediction excess value and energy consumption prediction deficit value of each process respectively; If the energy consumption prediction deficit value or energy consumption prediction excess value of the process corresponding to the triggered energy consumption anomaly signal is greater than the corresponding deficit threshold or excess threshold, execute step M2. Otherwise, send it to the management staff, and monitor the next set time window after the time point when the energy consumption anomaly signal is triggered for the process corresponding to the energy consumption anomaly signal. If the energy consumption anomaly signal is still triggered, also execute step M2; M2: Identify the location of the process corresponding to the triggered energy consumption anomaly signal. Draw a circle with the location as the center and a set distance as the radius, and screen all technicians within the circle as anomaly handling personnel; Analyze the selection preference evaluation index gkr of each anomaly handling personnel, and select the anomaly handling personnel with the largest selection preference evaluation index gkr as the handling personnel for the current process corresponding to the triggered energy consumption anomaly signal; and send the location of the process corresponding to the triggered energy consumption anomaly signal to the handling personnel.

4. An energy centralized automation management system according to claim 3, characterized in that, Analyze the selection preference evaluation index gkr of each anomaly handling personnel, specifically: Obtain the distance between each anomaly handling personnel and the location; at the same time, obtain the historical handling times of each anomaly handling personnel. From the specific types of processes corresponding to each handling in the historical handling times, extract those that are the same as the process corresponding to the triggered energy consumption anomaly signal and count the number as the triggered process handling times; Obtain the duration of each handling in the triggered process handling times; and calculate the average value to obtain the average solving time of each anomaly handling personnel; Mark the distance, triggered process handling times, and average solving time of each anomaly handling personnel as gh1, gh2, and gh3 respectively; Extract the energy consumption prediction deficit value or energy consumption prediction excess value corresponding to the triggered energy consumption anomaly signal, and calculate the difference from the corresponding deficit threshold or excess threshold, energy consumption prediction deficit value - deficit threshold or energy consumption prediction excess value - excess threshold, and mark the calculated difference as the anomaly degree value; Preset the value range of each group of degree values corresponding to the anomaly degree value of each process, and set a reference value set corresponding to each value range of each group of degree values; Among them, the reference value set includes the reference distance, reference triggered process handling times, and reference average solving time; Match the anomaly degree value of the current process corresponding to the triggered energy consumption anomaly signal with the corresponding value range of the degree value, so as to obtain the reference distance, reference triggered process handling times, and reference average solving time of the current process corresponding to the triggered energy consumption anomaly signal, and mark them as gy1, gy2, and gy3 respectively; According to the formula , the weighted calculation is performed on the travel distance gh1, the number of trigger process treatments gh2, and the average solution time gh3 of each exception handler to obtain the preferred evaluation index gkr of each exception handler; where c1, c2, and c3 are the influence weight factors corresponding to the travel distance gh1, the number of trigger process treatments gh2, and the average solution time gh3 respectively.

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