Method for modeling and production planning optimization of concentrator power load based on working condition division

Abstract

The application discloses a kind of based on working condition division concentrator electric load modeling and production plan optimization method.The method first defines normal production, low production operation, three production states and corresponding load coefficients of shutdown maintenance, in combination with typical daily load curve, daily random fluctuation factor and seasonal variation factor, construct multi-layer composite annual load refinement model;Again, initial production plan is generated by state coding, and the scattered maintenance day and low production operation day are integrated into continuous period by using centralized scheduling optimization algorithm, to reduce the frequency of production state switching;Finally, the optimized production plan and load model are integrated to generate a 8760-hour refined load sequence throughout the year.The application realizes the deep coupling of production plan and electric load, significantly improves the stability and accuracy of load prediction, provides reliable data support and decision basis for power grid planning, demand side response and enterprise energy efficiency optimization, and is highly adaptable and easy to implement.

Description

Technical Field

[0001] This invention belongs to the field of power system load modeling and industrial energy collaborative optimization technology, specifically involving a method for modeling power load and optimizing production plans for ore dressing plants based on operating condition division. Background Technology

[0002] As a core industrial link in the metallurgical and mineral processing sectors, ore dressing plants are typical continuous production-type high-energy-consuming users, with electricity consumption accounting for 30%-50% of their total operating costs, and they need to maintain a 24-hour uninterrupted production mode. The power load of such enterprises exhibits distinct industry-specific characteristics: the overall operating load base is large and highly stable, but it is affected by multiple factors, resulting in complex fluctuation patterns. These include periodic short-term fluctuations caused by daily staff shift changes and fixed-time equipment maintenance, as well as phased load reductions caused by seasonal task adjustments and changes in raw material supply, and uncertain fluctuations brought about by random factors such as weather and sudden equipment malfunctions.

[0003] Against the backdrop of the current power system's transformation towards intelligent and refined dispatching, the power grid side is placing higher demands on the predictability and controllability of industrial users' loads, while enterprises themselves urgently need to reduce operating costs through energy efficiency optimization. However, traditional methods for modeling electricity loads and formulating production plans for ore processing plants have significant shortcomings and are no longer suitable for current technological and market demands. On the one hand, traditional load modeling methods often rely on historical data statistical averaging or simple time series fitting, which can only capture the macro-level trend of load changes and cannot accurately reproduce the refined load characteristics under different production conditions. Such methods do not establish a direct correlation between core production states such as normal production, low-production operation, and shutdown for maintenance and load changes. They cannot quantify the impact of production state switching on the load, nor can they integrate multi-dimensional influencing factors such as seasonal fluctuations and random disturbances. As a result, load forecasting errors generally exceed 15%, which cannot provide a reliable basis for power grid planning and demand-side response.

[0004] On the other hand, there is a serious disconnect between production planning and load modeling. Traditional production planning is often driven solely by capacity targets and equipment maintenance needs, randomly allocating maintenance days and low-production operation days, neglecting the requirements for the continuity and stability of electricity load. Dispersed switching between non-production states not only increases equipment start-up and shutdown losses and reduces production efficiency, but also causes frequent abrupt changes in the load curve, putting pressure on the power grid's frequency and voltage regulation, while simultaneously leading to low energy utilization efficiency for enterprises themselves, making it difficult to achieve energy consumption optimization goals.

[0005] With the improvement of the power system's demand-side response mechanism, the advancement of industrial energy efficiency improvement policies, and the deepening of enterprises' needs for refined management, constructing a modeling method that can deeply integrate production operation status and load fluctuation patterns to achieve coordinated optimization of production plans and electricity load has become a key technical requirement for solving the pain points of energy management in ore dressing plants and supporting the safe and stable operation of the power grid. Against this backdrop, there is an urgent need for a coordinated optimization scheme that balances the accuracy of load forecasting with the operability of production plans, filling the existing technological gap. Summary of the Invention

[0006] This invention aims to overcome the shortcomings of existing technologies and provide a method for modeling annual electricity load and co-optimizing production plans for ore dressing plants based on working condition division and centralized scheduling optimization algorithms. Through the deep coupling of refined load state modeling and intelligent scheduling of production plans, a highly stable and accurate electricity load sequence covering 8760 hours throughout the year is generated, providing reliable data support and decision-making basis for power grid planning, demand-side response strategy formulation, and enterprise energy efficiency optimization.

[0007] To achieve the above objectives, this invention provides a method for modeling power load and optimizing production plans in a mineral processing plant based on operating condition division, comprising the following steps: S1. Construct a multi-layered composite annual power load refinement model for the ore dressing plant. The model integrates at least typical daily load curves, daily random fluctuation factors, seasonal variation factors, and load coefficients based on different production states. S2 defines three typical production states: normal production, low-production operation, and shutdown for maintenance, and generates an initial annual production plan based on the state code. S3, a centralized scheduling optimization algorithm is used to reorganize and centrally arrange the shutdown and maintenance days and low-production operation days in the initial annual production plan to generate an optimized annual production plan, wherein the optimization algorithm aims to reduce the frequency of production state switching. S4, integrate the optimized annual production plan with the multi-layer composite annual electricity load refinement model to generate a refined electricity load sequence of 8760 hours throughout the year; S5 outputs load forecast results and production plan distribution, providing data support for power grid planning, demand-side response, and enterprise energy efficiency optimization.

[0008] Furthermore, in step S1, the construction of a refined model of the multi-layered composite annual electricity load of the ore dressing plant includes: Define a 24-dimensional normalized load factor array as a typical daily load curve to characterize the periodic variation of the baseline load for each time period of the day. Define a daily random fluctuation factor to simulate daily load fluctuations caused by random factors; Define a seasonal variation factor to simulate the seasonal trend changes in the annual load; A fixed state load factor is assigned to each of the three states: normal production, low production operation, and shutdown for maintenance.

[0009] Furthermore, the formula for calculating the daily stochastic volatility factor is as follows: , where d is the date in a year; the formula for calculating the seasonal variation factor is: .

[0010] Furthermore, the step of centrally scheduling shutdown and maintenance days using a centralized scheduling optimization algorithm includes: Identify all downtime / maintenance days in the initial plan and calculate the total number of days T; If T is less than or equal to the first threshold, then all shutdown maintenance days will be arranged as a continuous maintenance window. If T is greater than the first threshold, the shutdown maintenance day will be allocated to two or more consecutive maintenance windows, with each window scheduled in a different time period within the year.

[0011] Furthermore, the first threshold is 8 days; when T is greater than 8 days, the shutdown maintenance days are divided into two consecutive maintenance periods with similar numbers of days, and are respectively arranged in the first half and the second half of the year.

[0012] Furthermore, the step of integrating and optimizing low-productivity operating days using a centralized scheduling optimization algorithm includes: Identify all low-productivity operating days in the initial plan and count the total number of days D. L ; According to D L Given a preset baseline window number of days, determine the number P of consecutive low-production operation windows; The scattered low-production operating days are integrated into P continuous operating periods, and each continuous operating period is ensured to have no overlap with the optimized shutdown and maintenance window in terms of time.

[0013] Furthermore, the number P of the continuous low-production operation windows is given by the formula It is determined that 20 days is the base number of days for each window, and the scattered low-yield operating days are integrated into P consecutive time periods, satisfying the following conditions: T low During low-production operation periods, T maintenance This period is for maintenance, while ensuring that the number of normal production days after optimization is no less than 250 days.

[0014] Furthermore, the calculation formula for generating the refined electricity load sequence for 8760 hours throughout the year is as follows: Among them, P base (h) represents the baseline load (MW) for hour h, Fstate (d) represents the load factor of the production status on day d, V daily (d) is the daily stochastic volatility factor, S seasonal (d) represents the seasonal factor; h represents the hour of the day, and d represents the date of the year.

[0015] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-mentioned method for modeling annual power load and co-optimizing production plan of ore dressing plant based on working condition division and centralized scheduling optimization algorithm.

[0016] On the other hand, a computer-readable storage medium stores a computer program that, when executed by a processor, implements the above-described method for modeling annual power load and co-optimizing production plans for a mineral processing plant based on working condition partitioning and centralized scheduling optimization algorithms.

[0017] The beneficial effects of this invention are: High level of load modeling sophistication: By integrating typical daily load curves, daily random fluctuation factors, seasonal variation factors, and production status load coefficients, a multi-layer composite load model is constructed, which can accurately depict the load fluctuation pattern under the 24-hour continuous production mode of the concentrator, covering multi-dimensional features such as periodic fluctuations, random fluctuations, and seasonal fluctuations.

[0018] Production planning and load coordination optimization: The innovative centralized scheduling optimization algorithm integrates scattered maintenance days and low-production operation days into continuous time periods, significantly reducing the frequency of production status switching and greatly improving the stability and predictability of the annual load sequence.

[0019] Its application value is wide-ranging: the generated 8760-hour refined load sequence can provide reliable data input for long-term power grid planning and demand-side response strategy formulation, while also providing scientific decision support for the production scheduling optimization and energy conservation and consumption reduction of the concentrator itself.

[0020] Highly convenient to implement: The model is developed based on general simulation platforms such as MATLAB. The parameters are adjustable and highly adaptable. It can be flexibly adjusted according to the production scale and operating procedures of different ore dressing plants, and has good prospects for promotion and application. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is a flowchart of the load model construction process according to an embodiment of the present invention; Figure 2 This is a typical daily load curve according to an embodiment of the present invention; Figure 3 This is an annual load curve diagram of an embodiment of the present invention; Figure 4 This is a distribution map of the annual production plan according to an embodiment of the present invention; Figure 5 This is a pie chart showing the percentage of annual production status in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0024] This application provides a method for modeling annual electricity load and coordinating production planning optimization in a mineral processing plant based on operating condition partitioning and centralized scheduling optimization algorithms, including the following steps: 1) Construct a refined model of the annual power load of a multi-layered composite ore dressing plant; 2) Define production status codes and generate the initial annual production plan; 3) Employ centralized scheduling optimization algorithms to optimize and reorganize production plans; 4) Integrate production planning and load models to generate annual load sequences and perform simulation analysis.

[0025] Furthermore, the implementation of step 1) includes the following specific steps: 1.1) Construct a typical daily load curve.

[0026] Define a 24-dimensional normalized load coefficient array `base_curve` to characterize the baseline load variation pattern of the ore dressing plant during different time periods within a 24-hour day. This curve needs to reflect periodic load fluctuations caused by factors such as shift changes and fixed-period equipment maintenance.

[0027] 1.2) Introducing daily stochastic volatility factor and seasonal variation factor To simulate the uncertainty of the load, a daily stochastic fluctuation factor is introduced. , It is used to simulate the ±5% load fluctuation caused by random factors such as weather and equipment status every day.

[0028] At the same time, seasonal variation factors are introduced. , A sine function is used to simulate the seasonal load trend of ±5% throughout the year.

[0029] 1.3) Define production status and its load factor Based on the actual operating procedures of the ore dressing plant, three typical production states and their corresponding load factors are defined: normal production state (load factor of 1.0), low production operation state (load factor of 0.4), and shutdown for maintenance state (load factor of 0.05).

[0030] Furthermore, the implementation of step 2) includes the following specific steps: 2.1) Initialize the annual production plan Initialize the production status for the entire 365 days of the year as "normal production". Randomly allocate T days (T is usually 10-15 days) as "downtime / maintenance days" within the year, and randomly allocate approximately D days from the remaining days. L Heaven (D) L Typically, 50 days are designated as "low-production operation days".

[0031] 2.2) State coding representation The daily production status is encoded using status numbers (such as 1, 2, 3) to form an initial annual production status sequence.

[0032] Furthermore, step 3) is implemented as follows: 3.1) Centralized scheduling of shutdown and maintenance days. Identify all initial downtime / maintenance dates. Set centralized scheduling rules: When the total number of maintenance days T ≤ 8, all maintenance days are rearranged into a single consecutive maintenance window. The starting date is given as D. start Randomly selected within the available time window throughout the year, with the end date being D. end ,have , When the total number of maintenance days T > 8, the maintenance days are divided into two consecutive maintenance periods, scheduled in the first and second halves of the year respectively. , , Where [T / 2] is rounded down, the start date of each maintenance period is randomly selected, and the end date is determined according to the formula.

[0033] 3.2) Integrate and optimize low-production operation days. Based on the total number of low-production operation days D L Determine the optimal number of consecutive windows: , The base number of days for each window is 20. The scattered low-yield operating days are reorganized into P consecutive operating periods. Constraints must be observed during the optimization process: , Ensure low-production operation time T low With maintenance period T maintenance No overlap.

[0034] 3.3) Integrate and optimize low-production operation days. The optimized production plan is verified by an iterative search algorithm to ensure that the new arrangement does not conflict with any hard constraints (such as the minimum number of normal production days ≥ 250 days), and finally a stable annual production plan is obtained by transforming from "random and scattered" to "centralized and orderly".

[0035] Furthermore, the implementation of step 4) includes: 4.1) Integrated calculation generates hourly load. Based on the optimized annual production plan, and combining the typical daily load curve, daily stochastic fluctuation factor, and seasonal variation factor, the load value for each hour out of the 8760 hours in the whole year is calculated using the following formula: , Among them, P base (h) represents the baseline load (MW) for hour h, F state (d) is the load factor of the production status on day d.

[0036] 4.2) Integrated calculation generates hourly load. Based on the generated load data, key indicators are calculated and output, such as the number of days of normal production, the number of days of low-production operation, the number of days of shutdown for maintenance, the annual daily maximum / minimum / average load, and the annual load rate. By plotting annual load curves, typical daily load curves, annual production plan distribution maps, and status percentage maps, the load characteristics and optimization effects are visualized and analyzed.

[0037] A specific embodiment is a collaborative optimization method for annual electricity load modeling and production planning of a typical ore dressing plant with a processing capacity of 5 million tons / year. The overall process is as follows: Figure 1 As shown.

[0038] This embodiment is based on a typical ore dressing plant with a processing capacity of 5 million tons per year, whose basic load parameters are set as follows: average load 8.5 MW, installed capacity 10.2 MW, and minimum sustaining load 0.5 MW. The ore dressing plant operates on a 24-hour continuous production system, with fixed shift handover and equipment maintenance periods during the day.

[0039] 1) Construct a refined model of the annual power load of a multi-layered composite ore dressing plant 1.1) Construct a typical daily load curve According to the production and operation procedures of the ore dressing plant, its typical daily load curve is defined as a 24-dimensional normalized load coefficient array base_curve, with the following specific values: base_curve = [0.95, 0.98, 1.00, 0.98, 0.95, 0.96, 0.98, 0.97, 0.90,0.98, 1.00, 0.98, 0.95, 0.96, 0.13, 0.14,0.89, 0.95, 0.98, 1.00, 0.98, 0.97,0.96, 0.95] The curve reflects the periodic pattern of a slight decrease in load during shift changes at 8:00 and 16:00 each day, and a significant decrease in load to its lowest value during the fixed maintenance period from 14:00 to 15:00.

[0040] 1.2) Introduce daily stochastic fluctuation factor and seasonal variation factor.

[0041] To simulate the uncertainties in actual operation, two factors are introduced: Daily stochastic fluctuations: , Simulates ±5% fluctuations caused by random factors such as daily weather and equipment status.

[0042] Seasonal variations: , A sine function is used to simulate the seasonal trend of ±5% throughout the year.

[0043] 1.3) Define the production status and its load factor.

[0044] The annual production of the ore dressing plant is abstracted into three states, and corresponding load factors are assigned:

[0045] 2) Define production status codes and generate the initial annual production plan.

[0046] 2.1) Initialize the annual production plan.

[0047] In the MATLAB environment, an array of length 365 is initialized, and all dates are preset to normal production (state 1). Then, 15 days are randomly selected from the entire year to be set as shutdown for maintenance (state 3), and then 50 days are randomly selected from the remaining days to be set as low-production operation (state 2). This step generates an initial production plan with a random and dispersed state distribution.

[0048] 3) The production plan is optimized and reorganized using a centralized scheduling optimization algorithm.

[0049] 3.1) Centralized scheduling of shutdown and maintenance days.

[0050] Identify all dates in the initial plan that are in state 3 (shutdown for maintenance). In this embodiment, the total maintenance days T = 15 (greater than 8), therefore a multi-period centralized strategy is executed: The 15-day maintenance period is divided into two phases. The first phase of maintenance, T1, has 7 days (15 / 2) and the second phase, T2, has 8 days (15-7).

[0051] Within the time windows of the first quarter (days 1-90) and the third quarter (days 181-270), a starting date (D) is randomly selected respectively. start1 D start2 This generates two consecutive maintenance windows: First issue: D end1 = D start1 +7-1 Second Issue: D end2 = D start2 +8-1 The originally randomly distributed maintenance dates will be adjusted to fall within these two consecutive windows.

[0052] 3.2) Integrate and optimize low-production operation days.

[0053] Identify all dates in the initial plan where the status is 2 (low-productivity operation), totaling D days. L =50. Calculate the optimal number of continuous windows P = min(3, (50 / 20)) = 2.

[0054] The scattered 50 days of low-production operation are integrated into two consecutive periods.

[0055] When scheduling these two consecutive time periods, it is necessary to ensure that they do not overlap with the two maintenance windows identified in step 3.1 in time. An iterative search algorithm is used to find suitable starting points for these two low-productivity periods on the annual timeline that do not conflict with maintenance.

[0056] 3.3) Iterative verification and feasibility assurance.

[0057] After optimizing the algorithm, it was verified and ensured that the number of normal production days in the optimized annual production plan was no less than 250 days. The final result was as follows: Figure 4The annual production plan distribution map shown shows that maintenance (red blocks) and low-production operation (yellow blocks) are distributed in a concentrated and continuous manner, while normal production (green blocks) dominates (approximately 300 days).

[0058] 4) Integrate production planning and load models to generate annual load sequences and perform simulation analysis.

[0059] 4.1) Integrated calculation generates hourly load.

[0060] Based on the optimized production plan and the load model established in step 1, the annual load value P(t) for 8760 hours is calculated hourly using the following formula: , Among them, P base (h) = average load * base_curve(h), where h is the hour of the day (0-23) and d is the date of the year (1-365).

[0061] 4.2) Conduct simulation verification and visualization analysis.

[0062] Run the above model in MATLAB to generate load data. Key simulation results are shown in the table below:

[0063] Visualization output includes: Figure 2 This is the electricity load curve of a mineral processing plant for 8760 hours throughout the year. The blue area represents the actual hourly load: the overall load fluctuates around the average load of the red dotted line and does not exceed the maximum load (installed capacity) of the green dotted line, which is consistent with the electricity consumption characteristics of continuous production. The multiple large troughs in the curve correspond to short-term maintenance from 14:00 to 15:00 every day, as well as concentrated and continuous shutdown maintenance and low-production operation periods, which intuitively reflect the electricity consumption pattern of the mineral processing plant, which is mainly based on normal production and supplemented by short-term / concentrated load reduction.

[0064] Figure 3 During the daytime (1-14 hours), the load remains stable at around 8MW. A significant trough occurs between 14-16 hours (the load drops sharply to about 1MW), corresponding to the daily shutdown and maintenance period from 14:00 to 15:00. After 16:00, the load quickly recovers to normal levels and then remains stable and fluctuates. This perfectly matches the operating conditions set in the code for daily continuous production and fixed-period maintenance, reflecting the periodic changes in daily electricity consumption.

[0065] Figure 4 and Figure 5Of the 365 days in a year, the green area is in normal production for about 300 days (over 82%), which is the core production mode; the yellow area operates at low production for about 50 days (13.7%), and the red area is shut down for maintenance for 10-15 days (2.7%-4.1%). Both low production and maintenance are concentrated in consecutive time periods, such as low production periods in cycles 1 and 3, and maintenance periods in cycles 1 and 4. This shows that the concentrator's planned structure, which prioritizes normal production and concentrates low production / maintenance, perfectly matches the simulation's setting of at least 250 days of normal production, about 50 days of low production, and 10-15 days of maintenance.

[0066] This embodiment successfully applied the method described in this invention to construct a refined annual electricity load model for a 5 million-ton-scale ore dressing plant and optimize its production plan. Simulation results show that this method can effectively generate load sequences that conform to actual operating patterns and significantly improve the stability and predictability of the load curve by centrally scheduling abnormal production periods. The data and charts output by the model can be directly used for long-term load forecasting of the power grid, demand-side response strategy formulation, and the optimization of the enterprise's own production and energy management, verifying the practicality and effectiveness of this invention.

[0067] The methods described above in this invention can be implemented on electronic devices by writing MATLAB scripts or more general computer programs (such as Python, C++). Furthermore, the logical instructions in the above methods can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0068] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the method for co-optimizing the annual power load modeling and production plan of the ore dressing plant provided by the above methods.

[0069] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for co-optimizing the annual power load modeling and production plan of a mineral processing plant provided by the methods described above.

[0070] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for modeling power load and optimizing production planning in a mineral processing plant based on operating condition division, characterized in that, Includes the following steps: S1. Construct a multi-layered composite annual power load refinement model for the ore dressing plant. The model integrates at least typical daily load curves, daily random fluctuation factors, seasonal variation factors, and load coefficients based on different production states. S2 defines three typical production states: normal production, low-production operation, and shutdown for maintenance, and generates an initial annual production plan based on the state code. S3, a centralized scheduling optimization algorithm is used to reorganize and centrally arrange the shutdown and maintenance days and low-production operation days in the initial annual production plan to generate an optimized annual production plan, wherein the optimization algorithm aims to reduce the frequency of production state switching. S4, integrate the optimized annual production plan with the multi-layer composite annual electricity load refinement model to generate a refined electricity load sequence of 8760 hours throughout the year; S5 outputs load forecast results and production plan distribution, providing data support for power grid planning, demand-side response, and enterprise energy efficiency optimization.

2. The method according to claim 1, characterized in that, In step S1, the construction of a refined model of the multi-layered composite annual electricity load of the ore dressing plant includes: Define a 24-dimensional normalized load factor array as a typical daily load curve to characterize the periodic variation of the baseline load for each time period of the day. Define a daily random fluctuation factor to simulate daily load fluctuations caused by random factors; Define a seasonal variation factor to simulate the seasonal trend changes in the annual load; A fixed state load factor is assigned to each of the three states: normal production, low production operation, and shutdown for maintenance.

3. The method according to claim 2, characterized in that, The formula for calculating the daily stochastic volatility factor is as follows: , where d is the date in a year; the formula for calculating the seasonal variation factor is: .

4. The method according to claim 1, characterized in that, The steps for centrally scheduling shutdown and maintenance days using a centralized scheduling optimization algorithm include: Identify all downtime / maintenance days in the initial plan and calculate the total number of days T; If T is less than or equal to the first threshold, then all shutdown maintenance days will be arranged as a continuous maintenance window. If T is greater than the first threshold, the shutdown maintenance day will be allocated to two or more consecutive maintenance windows, with each window scheduled in a different time period within the year.

5. The method according to claim 4, characterized in that, The first threshold is 8 days; when T is greater than 8 days, the shutdown maintenance days are divided into two consecutive maintenance periods with similar number of days, and are respectively arranged in the first half and the second half of the year.

6. The method according to claim 1 or 4, characterized in that, The steps for integrating and optimizing low-yield operating days using a centralized scheduling optimization algorithm include: Identify all low-productivity operating days in the initial plan and count the total number of days D. L ; According to D L Given a preset baseline window number of days, determine the number P of consecutive low-production operation windows; The scattered low-production operating days are integrated into P continuous operating periods, and each continuous operating period is ensured to have no overlap with the optimized shutdown and maintenance window in terms of time.

7. The method according to claim 6, characterized in that, The number P of consecutive low-production operation windows is given by the formula It is determined that 20 days is the base number of days for each window, and the scattered low-yield operating days are integrated into P consecutive time periods, satisfying the following conditions: T low During low-production operation periods, T maintenance This period is for maintenance, while ensuring that the number of normal production days after optimization is no less than 250 days.

8. The method according to claim 1, characterized in that, The calculation formula for generating the refined electricity load sequence for 8760 hours throughout the year is as follows: Among them, P base (h) represents the baseline load (MW) for hour h, F state (d) represents the load factor of the production status on day d, V daily (d) is the daily stochastic volatility factor, S seasonal (d) represents the seasonal factor; h represents the hour of the day, and d represents the date of the year.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for modeling annual power load and co-optimizing production plan of ore dressing plant based on working condition division and centralized scheduling optimization algorithm as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for modeling annual power load and co-optimizing production plan of ore dressing plant based on working condition division and centralized scheduling optimization algorithm as described in any one of claims 1 to 8.

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