Intelligent energy-saving control system and method for electrical equipment based on load prediction

Abstract

The invention discloses an intelligent energy-saving control system and method for electrical equipment based on load prediction, and the system comprises a strategy generation module which is used for obtaining the combination modes of a plurality of electrical equipment corresponding to different historical work tasks, and carrying out the energy consumption analysis. Obtaining a combination mode of the electrical equipment with the lowest equipment combination score under different work tasks as an equipment combination strategy of the corresponding work task, and constructing a corresponding relation table between the historical work tasks and the equipment combination strategies; and the strategy matching module is used for obtaining an equipment combination strategy corresponding to the real-time work task through a corresponding relation table between the historical work task and the equipment combination strategy after the real-time work task is analyzed, and running the corresponding electrical equipment. According to the method, the corresponding relation table between the historical work tasks and the equipment combination strategies is constructed, and the equipment operation combination is dynamically adjusted by using the real-time work tasks, so that the overall energy consumption is remarkably reduced, the control process is optimized, and the energy utilization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment energy conservation, and specifically refers to an intelligent energy-saving control system and method for electrical equipment based on load prediction. Background Art

[0002] With the wide improvement of energy conservation awareness in people's cognition, the accompanying demand is for the energy-saving control of electrical equipment. The following problems have been found in the research on the existing energy-saving control of electrical equipment:

[0003] 1. Most of the existing energy-saving control links occur after the equipment runs, that is, the energy-saving purpose is achieved by controlling the running time of the electrical equipment.

[0004] 2. The energy consumption prediction of electrical equipment only considers the equipment's own power and running time, and does not fully utilize the data generated during the energy-saving process to optimize the energy consumption prediction of electrical equipment.

[0005] In an existing energy regulation system, method, equipment, medium and product for a digital and low-carbon building, the system includes a smart energy center and a power grid. The smart energy center is used to adjust the control parameters of the electrical equipment in the space passed by the movement trajectory of the personnel in the building based on a regulation strategy constructed based on the building environment data, the detected personnel body feeling data, and the minimum electrical energy consumption required by the electrical equipment to reach the target comfort level; generate building energy demand prediction data based on the building environment data, electrical equipment control parameters, historical electricity consumption change data of the building, and the electricity demand input by the building manager, and feedback it to the power grid. However, this invention fails to optimize the power grid by using the data generated during the energy-saving process.

[0006] In summary, there is an urgent need for a new technical solution for an intelligent energy-saving control method, system and terminal for electrical equipment based on load prediction to solve the above technical problems. Summary of the Invention

[0007] The purpose of the present invention is to provide an intelligent energy-saving control system and method for electrical equipment based on load prediction. The present invention solves the problem of how to accurately control and significantly reduce the energy consumption of electrical equipment by predicting the load demand in advance and optimizing the equipment combination and control parameters accordingly during the use of electrical equipment.

[0008] To achieve this purpose, an intelligent energy-saving control system for electrical equipment based on load prediction designed by the present invention includes:

[0009] The strategy generation module is used to obtain various electrical equipment combination methods corresponding to different historical work tasks of electrical equipment, conduct energy consumption analysis on the various electrical equipment combination methods corresponding to different work tasks, obtain the combination method of electrical equipment with the lowest equipment combination score in each historical work task, and construct a correspondence table between historical work tasks and equipment combination strategies according to the combination method of electrical equipment with the lowest equipment combination score in each historical work task of electrical equipment;

[0010] The strategy matching module is used to obtain multi-dimensional operation monitoring data of electrical equipment at the work site, parse the real-time work task of the electrical equipment according to the multi-dimensional operation monitoring data of the electrical equipment, calculate the matching degree between the real-time work task of the electrical equipment and the historical work tasks, thereby determine the matching task in the historical work tasks, query the correspondence table between historical work tasks and equipment combination strategies according to the matching task to obtain the equipment combination strategy corresponding to the real-time work task, and operate the corresponding electrical equipment according to the equipment combination strategy corresponding to the real-time work task.

[0011] The beneficial effects of the present invention are as follows:

[0012] 1) The intelligent energy-saving control system and method for electrical equipment based on load prediction of the present invention utilize the energy consumption analysis of electrical equipment combination methods under different historical work tasks to generate equipment combination strategies, match and execute the strategies according to real-time work tasks, and monitor and rematch the execution process, realizing the screening of the optimal electrical equipment combination operation in different work scenarios, avoiding the limitations of the single-duration control energy-saving method after equipment operation, fully considering the influence of work task diversity on energy consumption, effectively reducing the overall energy consumption of electrical equipment, improving the energy-saving effect, enhancing the rationality and efficiency of electrical equipment operation at the same time, optimizing the energy-saving control process of electrical equipment, improving the energy utilization efficiency, and promoting the transformation of electrical equipment energy-saving control from traditional methods to intelligent and precise directions;

[0013] 2) The present invention screens the optimal combination method as the equipment combination strategy through historical data analysis and environmental parameter simulation, and can adjust the operation in combination with real-time energy price fluctuations, significantly reducing energy consumption and enterprise electricity costs;

[0014] 3) Introduce equipment reliability and maintenance cost assessment to avoid hidden expenses caused by frequent equipment failures or high maintenance costs;

[0015] 4) Use machine learning algorithms to classify work tasks and extract features, quickly match equipment combination strategies, reduce manual intervention, and improve production efficiency;

[0016] 5) The dynamic monitoring and anomaly handling mechanism ensures stable task execution, prevents faults such as abnormal voltage and equipment overheating, and reduces the risk of downtime;

[0017] 6) Support the equipment service life update strategy, maintain long-term energy efficiency by replacing aging equipment, and extend the system life cycle. Description of the Drawings

[0018] Figure 1 It is a structural block diagram of an intelligent energy-saving control system for electrical equipment based on load prediction provided by an embodiment of the present invention;

[0019] Figure 2 It is a flow block diagram of an intelligent energy-saving control method for electrical equipment based on load prediction provided by an embodiment of the present invention. Detailed Embodiments

[0020] The present invention will be further described in detail below with reference to the drawings and specific embodiments:

[0021] Embodiment 1:

[0022] As Figures 1-2 shown, an intelligent energy-saving control system for electrical equipment based on load prediction includes:

[0023] The policy generation module is used to obtain various electrical equipment combination methods corresponding to different historical work tasks of electrical equipment, perform energy consumption analysis on various electrical equipment combination methods corresponding to different work tasks, obtain the combination method of electrical equipment with the lowest equipment combination score in each historical work task, and construct a correspondence table between historical work tasks and equipment combination strategies according to the combination method of electrical equipment with the lowest equipment combination score in each historical work task of electrical equipment;

[0024] The policy matching module is used to obtain multi-dimensional operation monitoring data of electrical equipment at the work site, analyze the real-time work task of electrical equipment according to the multi-dimensional operation monitoring data of electrical equipment, calculate the matching degree between the real-time work task of electrical equipment and historical work tasks, thereby determine the matching task in historical work tasks, query the correspondence table between historical work tasks and equipment combination strategies according to the matching task to obtain the equipment combination strategy corresponding to the real-time work task, and operate the corresponding electrical equipment according to the equipment combination strategy corresponding to the real-time work task.

[0025] The present invention utilizes the energy consumption analysis of the electrical equipment combination modes under different historical work tasks to generate equipment combination strategies, matches and executes the equipment combination strategies according to the real-time work tasks, and monitors and re-matches the execution process, realizing the operation of accurately selecting the electrical equipment combination mode with the lowest energy consumption under different work tasks, avoiding the limitations of the single-duration control energy-saving mode after the equipment operation, fully considering the influence of work task diversity on energy consumption, effectively reducing the overall energy consumption of electrical equipment, improving the energy-saving effect, enhancing the rationality and efficiency of the electrical equipment operation at the same time, optimizing the energy-saving control process of electrical equipment, improving the energy utilization efficiency, and promoting the transformation of the energy-saving control of electrical equipment from the traditional mode to the intelligent and precise direction.

[0026] The work tasks include refrigeration, heating, etc.

[0027] In the above technical solution, in the strategy generation module, the method of obtaining the combination mode of the electrical equipment with the lowest equipment combination score in each historical work task and constructing the correspondence table between the historical work tasks and the equipment combination strategies is as follows:

[0028] For the combination modes of different electrical equipment for different work tasks, simulate the operation under different environmental parameters, and the environmental parameters at least include temperature, humidity and air pressure;

[0029] Record the energy consumption data of different combination modes of electrical equipment under different environmental parameters under the same work task, and calculate the average energy consumption and energy consumption fluctuation range of each combination mode;

[0030] Obtain the equipment reliability and maintenance cost of each combination mode;

[0031] Based on the average energy consumption, energy consumption fluctuation range, reliability and maintenance cost, obtain the equipment combination score F corresponding to different work tasks i The formula for determining the lowest equipment combination strategy is:

[0032] F i = min(a*E avg_i +b*ΔE i +c*(1 - R i ) + d*C i );

[0033] Among them, F i represents the equipment combination score, E avg_i is the average energy consumption of the i-th combination mode, ΔE i is the energy consumption fluctuation range of the i-th combination mode, R i is the equipment reliability of the i-th combination mode, C iis the maintenance cost of the i-th combination method, a is the preset weight coefficient of the average energy consumption, b is the preset weight coefficient of the energy consumption fluctuation range, c is the preset weight coefficient of the equipment reliability, and d is the preset weight coefficient of the maintenance cost; by adjusting the weight coefficients to highlight the importance of different factors, select F i The combination with the minimum value is used as the equipment combination strategy, and the use of the formula for determining the equipment combination strategy occurs before the combination of electrical equipment.

[0034] The combination with the lowest equipment combination score under each work task is used as the equipment combination strategy for the corresponding work task, thereby constructing a correspondence table between historical work tasks and equipment combination strategies.

[0035] In the embodiment, there are n types of electrical equipment combination methods, and each combination is simulated and operated under m different environmental parameters, E ij represents the energy consumption of the i-th combination under the j-th environment, and the expression of E avg_i is:

[0036]

[0037] Among them, the energy consumption fluctuation range αE of the i-th combination method i = max(E ij ) - min(E ij ).

[0038] According to the formula for determining the equipment combination strategy, select the combination with the lowest F i under each work task as the equipment combination strategy for the corresponding work task.

[0039] In one embodiment, after calculating the average energy consumption and energy consumption fluctuation range of each combination method, for the combination methods with an average energy consumption difference within a%, further analyze the reliability and maintenance cost of the corresponding equipment, which can further simplify the calculation process, where a represents the allowable range or threshold of the average energy consumption difference and is used to define the tolerance of the energy consumption difference.

[0040] This embodiment realizes the scientific determination of the equipment combination strategy by simulating the operation of the electrical equipment combination under different environmental parameters, processing the energy consumption data, and comprehensively considering the equipment reliability and maintenance cost. Through multi-dimensional analysis, it avoids the one-sidedness of selecting the equipment combination only based on a single energy consumption index, ensures that the selected combination is not only energy-saving in actual operation, but also has good stability and low maintenance cost, further improves the reliability and sustainability of the energy-saving control, provides a strong guarantee for the long-term stable and energy-saving operation of the electrical equipment, and optimizes the generation mechanism of the equipment combination strategy.

[0041] In the above technical solution, in the strategy matching module, the method for determining the matching task of the real-time work task in the historical work tasks is:

[0042] Collect multi-dimensional operation monitoring data of electrical equipment at the work site based on a preset sensor network. The multi-dimensional operation monitoring data includes: environmental parameters, electrical equipment operation parameters, and electrical equipment status information data;

[0043] Use machine learning algorithms to extract features and classify the multi-dimensional operation monitoring data of the electrical equipment to obtain real-time work tasks and the operation parameters of the electrical equipment;

[0044] Calculate the matching degree based on the real-time work task and the historical work task, thereby determine the matching task in the historical work tasks, query the corresponding relationship table between the historical work tasks and the equipment combination strategy according to the matching task to obtain the equipment combination strategy corresponding to the real-time work task, and operate the corresponding electrical equipment using the operation parameters of the electrical equipment;

[0045] The matching degree is calculated using a vector space model.

[0046] Specifically: construct the feature vectors of the historical work task and the real-time work task respectively, calculate the similarity between the real-time work task and the historical work task using cosine similarity, calculate the similarity between the real-time work task and all historical work tasks, and select the historical work task corresponding to the highest value as the matching task.

[0047] The status information data of the electrical equipment refers to the working status of the electrical equipment, that is, the start and stop of the electrical equipment.

[0048] This embodiment uses the existing sensor network of the sensor device to collect data and the feature extraction and classification of the existing machine learning algorithm to achieve the accurate identification of real-time work tasks and the efficient matching of equipment combination strategies. It overcomes the problem that it is difficult to quickly and accurately determine work tasks and adapt equipment combinations in the traditional way, improves the timeliness and accuracy of equipment operation, enables electrical equipment to quickly respond to work task changes, ensures operation in a suitable combination at the right time, reduces energy waste caused by equipment idling or unreasonable operation, and enhances the dynamic adaptability of energy-saving control.

[0049] In the above technical solution, it also includes a strategy monitoring module that monitors the execution process of the equipment combination strategy, and the method of re-running strategy matching based on the completion progress of the real-time work task is as follows:

[0050] Real-time monitor the operation parameters of the electrical equipment. The operation parameters at least include voltage, current, power, and the temperature and vibration of the equipment;

[0051] Based on the operating parameters, determine whether there are any abnormalities in the execution process of the equipment combination strategy corresponding to the real-time work task when operating the corresponding electrical equipment. When there are abnormalities in the execution process, adjust the operating parameters of the electrical equipment or switch the equipment combination strategy;

[0052] If the equipment combination strategy is switched, obtain the real-time completion progress of the work task and re-match the equipment combination strategy;

[0053] The method for switching the equipment combination strategy: Obtain the real-time completion progress of the work task, re-obtain the multi-dimensional operation monitoring data at the work site, parse the updated real-time work task based on the multi-dimensional operation monitoring data, calculate the matching degree between the updated real-time work task and the historical work task, thereby determine the updated matching task in the historical work tasks, query the corresponding relationship table between the historical work tasks and the equipment combination strategy according to the updated matching task to obtain the equipment combination strategy corresponding to the updated real-time work task, and operate the corresponding electrical equipment according to the equipment combination strategy corresponding to the updated real-time work task.

[0054] Abnormal situations include voltage abnormalities, current abnormalities, power abnormalities, equipment failures, equipment vibration abnormalities, and equipment temperature abnormalities. The method for determining an abnormality is as follows: When the voltage, current, and power do not work according to the rated data, and the values of the equipment temperature and vibration are outside the normal range, and the equipment itself has a fault detection and alarm function, an alarm signal will be emitted when a fault occurs. Among them, the rated data of voltage, current, and power, and the normal range of equipment temperature and vibration can be determined from the historical work task data.

[0055] This embodiment realizes the effective control of the execution process of the equipment combination strategy by using the real-time monitoring of the operating parameters of electrical equipment, abnormal judgment and processing, and the re-matching of the strategy combined with the work task progress. It can timely detect equipment operation abnormalities and make adjustments to ensure the stable operation of the equipment. At the same time, it can flexibly adjust the strategy according to the task progress to ensure that the equipment always operates in the optimal combination, avoiding increased energy consumption caused by equipment failures or task changes, improving the real-time performance and flexibility of energy-saving control, and optimizing equipment operation management.

[0056] In this embodiment, obtaining the real-time completion progress of the work task and re-matching the equipment combination strategy can avoid resource waste. Once it is detected that some equipment has completed its predetermined tasks and is no longer needed in the future, the most optimized equipment combination strategy will be re-matched. This method can not only ensure the effective utilization of resources, avoid unnecessary equipment idling and waste, but also flexibly adjust the resource allocation according to the actual work progress, thereby maximizing the overall work efficiency. In addition, this dynamic adjustment mechanism also supports a rapid response to sudden situations or task changes, further enhancing the adaptability and flexibility of the system.

[0057] In the above technical solution, in the policy generation module, the device combination policy further includes obtaining the service life of the device, replacing the electrical devices in the device combination policy with electrical devices of different service lives, re-performing the energy consumption analysis to obtain a new device combination policy, and constructing a correspondence table between historical work tasks and device combination policies under different service lives. The specific process is as follows:

[0058] For different combination methods of electrical devices for different work tasks, perform simulation operations under different environmental parameters and different service lives;

[0059] Record the energy consumption data of different combination methods of electrical devices under the same work task under different environmental parameters and different service lives, and calculate the average energy consumption and energy consumption fluctuation range of each combination method;

[0060] Obtain the device reliability and maintenance cost of each combination method;

[0061] Based on the average energy consumption, energy consumption fluctuation range, reliability, and maintenance cost, obtain the F corresponding to different work tasks i The formula for determining the device combination strategy with the lowest F is:

[0062] F i = min(a * E avg_i + b * ΔE i + c * (1 - R i ) + d * C i );

[0063] Among them, F i represents the device combination score, E avg_i is the average energy consumption of the i-th combination method, ΔE i is the energy consumption fluctuation range of the i-th combination method, R i is the device reliability of the i-th combination method, C i is the maintenance cost of the i-th combination method, a is the preset weight coefficient of the average energy consumption, b is the preset weight coefficient of the energy consumption fluctuation range, c is the preset weight coefficient of the device reliability, and d is the preset weight coefficient of the maintenance cost;

[0064] For each work task, the combination with the lowest device combination score among the electrical devices with different lives in each combination method is used as the device combination strategy for the corresponding work task, thereby constructing a correspondence table between historical work tasks and device combination policies under different service lives.

[0065] This embodiment realizes the dynamic optimization of the equipment combination strategy by considering the service life of the equipment and updating the equipment replacement and energy consumption analysis based on it. It breaks the limitation of the fixed equipment combination, enables the strategy to be adjusted in a timely manner with the aging and performance changes of the equipment, ensures the energy-saving effect during long-term operation, reduces the increase in energy consumption caused by equipment life problems, extends the overall service life of the equipment, reduces the equipment replacement cost, and improves the long-term effectiveness and economy of energy-saving control.

[0066] In the above technical solution, in the strategy generation module, the obtained equipment combination strategy is verified, and the verification at least includes verifying whether the electrical equipment combined based on the equipment combination strategy can complete the work task.

[0067] This embodiment realizes the reliability guarantee of the equipment combination strategy by verifying the obtained equipment combination strategy. It ensures that the work task can be completed even when the equipment combination score is the lowest, avoids task interruption or energy waste caused by strategy mistakes, improves the stability and practicality of energy-saving control, provides reliable technical support for practical applications, and enhances the user's trust in energy-saving control.

[0068] In the above technical solution, in the strategy matching module, according to the equipment combination strategy corresponding to the real-time work task, the corresponding electrical equipment is operated, and during the operation, the operating electrical equipment is adjusted according to the current energy supply situation and price fluctuations. The specific adjustment method is as follows:

[0069] According to the energy supply quantity and real-time price, calculate the equipment operation cost under the equipment combination strategy that is matched, find the minimum value of the equipment operation cost, and use the minimum value of the equipment operation cost as the target to control the operation of the electrical equipment under the equipment combination strategy.

[0070] This embodiment realizes the coordinated optimization of energy saving and cost control by calculating the equipment operation cost using the energy supply and price fluctuation information and adjusting the equipment operation accordingly. On the premise of ensuring the completion of the work task, the equipment operation is flexibly adjusted according to the energy market situation, the energy procurement cost is reduced, the economic benefit of energy utilization is improved, the energy-saving control is more in line with the actual economic environment, and the market adaptability and comprehensive benefit of the energy-saving control are enhanced.

[0071] In the above technical solution, the method of re-running the policy matching module based on the completion progress of the real-time work task is as follows: Based on the impact of the preheating and cooling times of electrical equipment on energy consumption, when switching electrical equipment, the start and stop times of the electrical equipment are controlled by combining the characteristics of the electrical equipment and the current work task progress. Electrical equipment usually needs to go through preheating and cooling processes when starting and stopping. For example, some equipment needs to be preheated before starting to reach the best working state, and needs to be cooled after stopping to avoid damage. These processes themselves consume energy, and if the equipment is started and stopped frequently, it will cause additional energy consumption. By reasonably arranging the start and stop times of the equipment, the equipment is prevented from frequently entering the preheating and cooling states. For example, if the equipment is about to start the next task soon after completing the current task, then the stop time of the equipment can be delayed, or the equipment can be started in advance and put into a standby state.

[0072] In this embodiment, by comprehensively considering the preheating and cooling times of electrical equipment and the work task progress, the energy consumption during equipment switching is reduced. The start and stop times of the equipment are reasonably arranged, avoiding frequent start and stop and unnecessary energy consumption, improving the energy utilization efficiency of equipment operation, further optimizing the energy-saving control details, reducing energy waste, and enhancing the overall energy-saving effect.

[0073] Embodiment 2:

[0074] An intelligent energy-saving control method for electrical equipment based on load prediction, comprising the following steps:

[0075] Step 1: Obtain various electrical equipment combination modes corresponding to different historical work tasks of the electrical equipment, perform energy consumption analysis on the various electrical equipment combination modes corresponding to different work tasks, obtain the combination mode of the electrical equipment with the lowest equipment combination score in each historical work task, and construct a correspondence table between the historical work tasks and the equipment combination strategy according to the combination mode of the electrical equipment with the lowest equipment combination score in each historical work task of the electrical equipment.

[0076] Step 2: Obtain multi-dimensional operation monitoring data at the work site, then parse the real-time work task of the electrical equipment according to the multi-dimensional operation monitoring data, calculate the matching degree between the real-time work task of the electrical equipment and the historical work tasks, thereby determine the matching task among the historical work tasks, query the correspondence table between the historical work tasks and the equipment combination strategy according to the matching task to obtain the equipment combination strategy corresponding to the real-time work task, and operate the corresponding electrical equipment according to the equipment combination strategy corresponding to the real-time work task.

[0077] In Step 1, by analyzing different work tasks and their corresponding electrical equipment combination modes in historical data, an equipment combination strategy based on the lowest energy consumption is formulated, providing an optimized basic solution for subsequent tasks.

[0078] In step 2, by quickly analyzing the requirements of the current work task and operating the corresponding electrical equipment according to the matched optimal equipment combination strategy, it ensures the efficient completion of the task while reducing the time and resource waste caused by equipment mismatch. It helps to apply the pre-established equipment combination strategy to the actual operation, ensuring that each real-time work task can obtain the optimal resource allocation, and improving the execution efficiency and flexibility.

[0079] Embodiment 3:

[0080] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0081] Embodiment 4:

[0082] A computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0083] Similarly, it should be noted that the terminal device in this embodiment corresponds to the intelligent energy-saving control method of the electrical equipment based on load prediction described above. Therefore, for the content not specifically described in the terminal device of this embodiment, such as but not limited to function definition, working principle, and technical effect, etc., reference can be made to the intelligent energy-saving control method of the electrical equipment based on load prediction described above, and details are not elaborated herein.

[0084] An intelligent energy-saving control method, system, and terminal for electrical equipment based on load prediction in the embodiments of the present invention utilize the energy consumption analysis of the electrical equipment combination methods under different historical work tasks to generate an equipment combination strategy, match and execute the strategy according to the real-time work task, and monitor and re-match the execution process, realizing the precise selection of the electrical equipment combination with the lowest energy consumption for operation in different work scenarios, avoiding the limitations of the single-duration control energy-saving method after equipment operation, fully considering the impact of work task diversity on energy consumption, effectively reducing the overall energy consumption of electrical equipment, improving the energy-saving effect, enhancing the rationality and efficiency of electrical equipment operation, optimizing the energy-saving control process of electrical equipment, improving the energy utilization efficiency, and promoting the transformation of the energy-saving control of electrical equipment from traditional methods to intelligent and precise directions.

[0085] The content not described in detail in this specification belongs to the prior art well-known to those skilled in the art. Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0086] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0087] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0088] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than limit the scope of its protection. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that after reading the present invention, various changes, modifications or equivalent replacements can still be made to the specific implementation manners of the invention. However, these changes, modifications or equivalent replacements are all within the scope of protection of the pending claims of the invention.

[0090] The content not described in detail in this specification belongs to the prior art well-known to those of ordinary skill in the art.

Claims

1. An intelligent energy-saving control system for electrical equipment based on load prediction, characterized in that, Including: The policy generation module is used to obtain various electrical equipment combination methods corresponding to different historical work tasks of electrical equipment, perform energy consumption analysis on various electrical equipment combination methods corresponding to different work tasks, obtain the combination method of electrical equipment with the lowest equipment combination score in each historical work task, and construct a correspondence table between historical work tasks and equipment combination strategies according to the combination method of electrical equipment with the lowest equipment combination score in each historical work task of electrical equipment; The policy matching module is used to obtain multi-dimensional operation monitoring data of electrical equipment at the work site, parse the real-time work task of the electrical equipment according to the multi-dimensional operation monitoring data of the electrical equipment, calculate the matching degree between the real-time work task of the electrical equipment and the historical work tasks, thereby determine the matching task in the historical work tasks, query the correspondence table between the historical work tasks and the equipment combination strategies according to the matching task to obtain the equipment combination strategy corresponding to the real-time work task, and operate the corresponding electrical equipment according to the equipment combination strategy corresponding to the real-time work task.

2. The intelligent energy-saving control system for electrical equipment based on load prediction according to claim 1, wherein In the policy generation module, the method for obtaining the combination method of electrical equipment with the lowest equipment combination score in each historical work task and constructing the correspondence table between historical work tasks and equipment combination strategies is as follows: For the combination methods of different electrical equipment for different work tasks, perform simulation operations under different environmental parameters, and the environmental parameters at least include temperature, humidity, and air pressure; Record the energy consumption data of different combination methods of electrical equipment under different environmental parameters under the same work task, and calculate the average energy consumption and energy consumption fluctuation range of each combination method; Obtain the equipment reliability and maintenance cost of each combination method; Based on the average energy consumption, energy consumption fluctuation range, reliability, and maintenance cost, obtain the equipment combination strategy with the lowest equipment combination score corresponding to different work tasks. The equipment combination strategy determination formula is: F i = min(a * E avg_i + b * ΔE i + c * (1 - R i ) + d * C i ) Among them, F i represents the combined score of the devices, E avg_i is the average energy consumption of the i-th combination method, ΔE i is the energy consumption fluctuation range of the i-th combination method, R i is the device reliability of the i-th combination method, C i is the maintenance cost of the i-th combination method, a is the preset weight coefficient of the average energy consumption, b is the preset weight coefficient of the energy consumption fluctuation range, c is the preset weight coefficient of the device reliability, and d is the preset weight coefficient of the maintenance cost; The combination with the lowest score of the equipment combination method under each work task is used as the equipment combination strategy under the corresponding work task, thereby constructing a correspondence table between historical work tasks and equipment combination strategies.

3. The intelligent energy-saving control system of an electrical device based on load prediction according to claim 1, wherein In the policy matching module, the method for determining the matching task in the historical work tasks according to the real-time work task is as follows: Collect multi-dimensional operation monitoring data of electrical equipment at the work site based on a preset sensor network. The multi-dimensional operation monitoring data includes: environmental parameters, electrical equipment operation parameters, and electrical equipment status information data; Use machine learning algorithms to perform feature extraction and classification on the collected multi-dimensional operation monitoring data to obtain the real-time work task and the operation parameters of the electrical equipment; Calculate the matching degree based on the real-time work task and the historical work tasks, thereby determine the matching task in the historical work tasks, query the correspondence table between the historical work tasks and the equipment combination strategies according to the matching task to obtain the equipment combination strategy corresponding to the real-time work task, and operate the corresponding electrical equipment using the operation parameters of the electrical equipment; The matching degree is calculated using the vector space model.

4. The intelligent energy-saving control system for electrical equipment based on load prediction according to claim 1, wherein It further includes a policy monitoring module. The method for the policy monitoring module to monitor the execution process of the device combination policy and re-run the policy matching based on the real-time completion progress of the work task is as follows: Monitor the operating parameters of electrical equipment in real time. The operating parameters at least include voltage, current, power, as well as the temperature and vibration of the equipment; Based on the operating parameters, determine whether there is an abnormality in the execution process when the device combination policy corresponding to the real-time work task operates the corresponding electrical equipment. When there is an abnormality in the execution process, adjust the operating parameters of the electrical equipment or switch the device combination policy; If the device combination policy is switched, obtain the real-time completion progress of the work task and re-match the device combination policy; The method for switching the device combination policy: obtain the real-time completion progress of the work task, re-obtain the multi-dimensional operation monitoring data of the work site, analyze the updated real-time work task according to the multi-dimensional operation monitoring data, calculate the matching degree between the updated real-time work task of the electrical equipment and the historical work task, so as to determine the updated matching task in the historical work task, query the corresponding relationship table between the historical work task and the device combination policy according to the updated matching task to obtain the device combination policy corresponding to the updated real-time work task of the electrical equipment, and operate the corresponding electrical equipment according to the device combination policy corresponding to the updated real-time work task of the electrical equipment.

5. The intelligent energy-saving control system for electrical equipment based on load prediction according to claim 2, wherein In the policy generation module, the device combination policy further includes obtaining the service life of the equipment, replacing the electrical equipment in the device combination policy with electrical equipment of different service lives, re-performing the energy consumption analysis to obtain a new device combination policy, and constructing a corresponding relationship table between the historical work task and the device combination policy under different service lives. The specific process is as follows: For different combination methods of electrical equipment for different work tasks, perform simulation operations under different environmental parameters and different service lives; Record the energy consumption data of different combination methods of electrical equipment under the same work task under different environmental parameters and different service lives, and calculate the average energy consumption and the energy consumption fluctuation range of each combination method; Obtain the equipment reliability and maintenance cost of each combination method; Based on the average energy consumption, energy consumption fluctuation range, reliability and maintenance cost, obtain the combination with the lowest equipment combination score among the combinations of electrical equipment with different lives for each work task as the device combination policy for the corresponding work task, so as to construct a corresponding relationship table between the historical work task and the device combination policy under different service lives.

6. The intelligent energy-saving control system for electrical equipment based on load prediction according to claim 1, characterized in that, In the policy generation module, verify the obtained device combination policy. The verification at least includes verifying whether the electrical equipment combined based on the device combination policy can complete the work task.

7. The intelligent energy-saving control system for electrical equipment based on load prediction according to claim 1, wherein, In the policy matching module, adjust the operating electrical equipment according to the current energy supply situation and price fluctuations during the operation of the corresponding electrical equipment according to the device combination policy corresponding to the real-time work task. The specific adjustment method is as follows: According to the energy supply quantity and real-time price, calculate the operation cost of the equipment under the matched equipment combination strategy, and obtain the minimum value of the equipment operation cost. Use the minimum value of the equipment operation cost as the target to control the operation of the electrical equipment under the equipment combination strategy.

8. An intelligent energy-saving control method for electrical equipment based on load prediction, characterized in that, It includes the following steps: Step 1: Obtain various electrical equipment combination methods corresponding to different historical work tasks of the electrical equipment, conduct energy consumption analysis on the various electrical equipment combination methods corresponding to different work tasks, obtain the combination method of the electrical equipment with the lowest equipment combination score in each historical work task, and construct a correspondence table between the historical work tasks and the equipment combination strategy according to the combination method of the electrical equipment with the lowest equipment combination score in each historical work task of the electrical equipment. Step 2: Obtain multi-dimensional operation monitoring data at the work site, then parse the real-time work task of the electrical equipment according to the multi-dimensional operation monitoring data, calculate the matching degree between the real-time work task of the electrical equipment and the historical work tasks, thereby determine the matching task in the historical work tasks, query the correspondence table between the historical work tasks and the equipment combination strategy according to the matching task to obtain the equipment combination strategy corresponding to the real-time work task, and operate the corresponding electrical equipment according to the equipment combination strategy corresponding to the real-time work task.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it realizes the steps of the method described in claim 8.

10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, it realizes the steps of the method described in claim 8.

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