A smart energy comprehensive control method, device, equipment and storage medium

Abstract

The present application relates to the technical field of energy regulation, and in particular to a method, device, equipment and storage medium for comprehensive intelligent energy regulation, wherein the method comprises obtaining production data and establishing a digital twin model based on the production data; obtaining total energy consumption data and effective energy consumption data based on the digital twin model; calculating the current energy utilization efficiency based on the total energy consumption data and the effective energy consumption data; determining whether the current energy utilization efficiency meets a first preset threshold; and obtaining an adjustment strategy based on the current energy utilization efficiency if the current energy utilization efficiency does not meet the first preset threshold. The present application has the effect of being able to automatically monitor energy consumption and perform intelligent processing.

Description

Technical Field

[0001] The present application relates to the technical field of energy regulation, and in particular to a smart energy comprehensive regulation method, device, equipment and storage medium. Background Art

[0002] In industrial production and operations, efficient energy utilization is a key factor in achieving cost savings, environmental protection, and sustainable development. Traditional energy management methods often rely on manual monitoring and periodic evaluations. This approach is not only time-consuming and labor-intensive, but also fails to accurately reflect the actual state of energy utilization in a timely manner, leading to significant energy waste and inefficiency.

[0003] Therefore, how to automatically monitor energy consumption and perform intelligent processing has become an urgent problem that needs to be solved. Summary of the Invention

[0004] In order to automatically monitor energy consumption and perform intelligent processing, the present application provides a smart energy comprehensive control method, device, equipment and storage medium.

[0005] In the first aspect, the present application provides a smart energy comprehensive control method using the following technical solutions:

[0006] A smart energy comprehensive control method, comprising:

[0007] Acquiring production data and establishing a digital twin model based on the production data;

[0008] Acquire total energy consumption data and effective energy consumption data based on the digital twin model;

[0009] Calculating current energy utilization efficiency based on the total energy consumption data and the effective energy consumption data;

[0010] Determining whether the current energy utilization efficiency meets a first preset threshold;

[0011] If the current energy utilization efficiency does not meet the first preset threshold, an adjustment strategy is obtained based on the current energy utilization efficiency.

[0012] By employing the above technical solution, production data is acquired and a digital twin model is built, enabling accurate simulation of the actual production process. As a virtual mapping, the digital twin model reflects various variables and parameters in the production process, providing a solid foundation for subsequent energy data processing. Based on the digital twin model, total and effective energy consumption data can be easily obtained, current energy efficiency can be calculated, and compared with a preset threshold, enabling rapid identification of energy utilization issues. This efficient energy management approach helps companies adjust strategies and optimize energy use in a timely manner. When current energy efficiency falls short of a preset threshold, electronic equipment automatically generates an adjustment strategy based on the current energy efficiency. Intelligent adjustment strategy development avoids the tedious and subjective nature of manual intervention, improving the accuracy and timeliness of adjustments. Furthermore, continuous monitoring of production data facilitates user understanding of production status.

[0013] This precise simulation helps to identify potential energy waste problems in advance, so that preventive measures can be taken to avoid unnecessary energy consumption.

[0014] Optionally, the adjustment strategy includes:

[0015] Determining whether there is an abnormal energy consumption location based on the digital twin model;

[0016] If the energy consumption abnormal location exists, obtaining corresponding current product data based on the energy consumption abnormal location;

[0017] Acquiring corresponding historical product data based on the abnormal energy consumption location;

[0018] Determining whether the current product data is abnormal based on the historical product data;

[0019] If the current product data has no abnormality, obtaining the corresponding first production equipment based on the abnormal energy consumption location;

[0020] outputting a maintenance signal and a production reduction signal based on the first production equipment;

[0021] If the current product data is abnormal, a stop operation signal is output based on the first production equipment.

[0022] By adopting the above technical solution and utilizing a digital twin model, energy consumption can be monitored in real time and accurately located at the location of energy consumption anomalies, eliminating the tedious process of manual investigation and significantly improving the efficiency and accuracy of problem detection. After locating the location of the energy consumption anomaly, current product data is further acquired and compared with historical product data. Through automated comparison, it is quickly determined whether the current product data is anomaly. Based on the anomaly in the current product data, the electronic device automatically outputs a corresponding signal. If the current product data is normal, it is determined that a problem has occurred with the first production equipment, resulting in increased energy loss while maintaining production, thus reducing energy utilization. Therefore, a maintenance signal and a production reduction signal are output, prompting maintenance and appropriate production reduction on the first production equipment to prevent potential energy waste. Reducing the production output of the first production equipment can also reduce the possibility of further anomalies, thereby protecting the first production equipment. If the current product data is abnormal, a stop signal is directly output. Because the product data anomaly has already occurred, it is determined that the situation is serious, and the stop signal is output to ensure that the problematic equipment does not continue to operate and cause further energy waste or equipment damage. Through real-time monitoring and intelligent judgment, refined management of production processes and equipment is achieved, avoiding unnecessary energy waste. At the same time, it also protects production equipment and reduces the possibility of equipment damage leading to production interruption.

[0023] Optionally, before outputting a stop-operation signal based on the first production equipment, the method further includes:

[0024] Acquire a received operation instruction based on the first production device;

[0025] acquiring, based on the first production equipment, a production status before receiving the operation instruction;

[0026] Determining whether the operation instruction meets the operation requirements based on the production status;

[0027] If the operation instruction does not meet the operation requirement, obtaining a first preset delay;

[0028] within the first preset delay, determining whether the first production device receives a debugging instruction;

[0029] If the first production device receives the debugging instruction, the step of outputting a stop operation signal based on the first production device is not performed.

[0030] If the operation instruction meets the operation requirement, the step of outputting a stop operation signal based on the first production equipment is executed.

[0031] By adopting the above technical solution, the operating instructions received by the first production equipment are obtained, and based on its production status, it is determined whether the operating instructions meet the operating requirements, which can effectively avoid production anomalies caused by misoperation or erroneous instructions, and then lead to product data anomalies. When the operating instruction does not meet the operating requirements, the electronic device does not immediately perform the shutdown operation, but first obtains a preset delay. During this delay period, it is determined that the production equipment has received a debugging instruction, which means that the operating instruction may be being corrected or the equipment is undergoing necessary debugging. At this time, the electronic device will not output a stop signal, thereby avoiding unnecessary shutdowns, reducing production interruptions and energy waste. Strategy optimization helps to reduce the number and downtime of production line shutdowns, improve equipment utilization and production efficiency, and intelligent processing methods help to improve the system's degree of automation and response speed.

[0032] Optionally, the method further includes:

[0033] Get all historical energy utilization rates;

[0034] Performing trend forecasting based on the historical energy utilization rate to obtain a forecast result;

[0035] Modifying the first preset threshold based on the prediction result to obtain a modified value;

[0036] The correction value is assigned to the first preset threshold.

[0037] By adopting the above technical solution, all historical energy utilization data is obtained and analyzed, and electronic equipment can make trend predictions and gain early insight into potential changes in energy utilization efficiency. The first preset threshold is corrected based on the prediction results, that is, the energy utilization efficiency standard is dynamically adjusted rather than fixed. This dynamic adjustment mechanism can better adapt to changes in the production environment. The dynamically adjusted first preset threshold can more accurately reflect the energy utilization in actual production and can be more accurate when making abnormal judgments, that is, ensuring the timeliness and effectiveness of energy management strategies. Based on effective judgment processing, it can better help users make improvements to achieve energy conservation, emission reduction and sustainable development, and at the same time it can also reduce production costs and increase production efficiency to a certain extent.

[0038] Optionally, performing trend prediction based on the historical energy utilization rate to obtain a prediction result includes:

[0039] The calculation formula of the prediction result is: ;

[0040] Among them, k is a time point, that is, each historical energy utilization rate corresponds to a time point; Predict the result for the k+1th time point; The original data sequence The first value of is the value of the first historical energy utilization rate; a and b are known numbers.

[0041] By adopting the above technical solution and using the calculation formula, it is possible to more accurately predict the energy utilization rate at a certain point in the future. This prediction capability provides enterprises with a forward-looking management perspective, which helps to plan energy allocation and adjust production strategies in advance. The above calculation formula has relatively low requirements for data integrity and distribution characteristics, and can make predictions when there is less data or the data distribution is uncertain, which makes it highly adaptable and flexible in practical applications. Users can promptly understand the changing trends of future energy utilization rates and adjust production according to the prediction results. In addition, by revising the first preset threshold based on the accurate prediction results, more accurate results for determining abnormalities can also be obtained.

[0042] Optionally, the method further includes:

[0043] Construct the original data series from historical energy utilization ;

[0044] For the original data series Accumulate to get a new sequence ;

[0045] Sequence-based Constructing a sequence generating values ​​close to the mean ,sequence ;

[0046] Construct data matrix B and vector Y;

[0047] Based on the calculation formula: , calculate the parameter vector U;

[0048] The calculation formula of parameter vector U is: ;

[0049] Based on the parameter vector U, the known numbers a and b can be extracted;

[0050] in, represents the kth historical energy utilization rate; Represents the cumulative value of the historical energy utilization rate from the first to the kth; the data matrix B is , n is the length of the original data sequence, that is, n is the number of historical energy utilization rates, and the first column of the data matrix B is , the second column of the data matrix B is all 1; the vector Y is dimensional vector, vector Y contains the original data sequence The values ​​from the second element to the last element; is the transposed matrix of B, for The inverse matrix of .

[0051] By adopting the above technical solution, the original data sequence was constructed, effectively weakening the randomness and volatility in the original data, making the development trend of the data sequence more obvious and providing a more stable data foundation for subsequent modeling. The construction of a sequence generating a value close to the mean and the calculation of the parameter vector U based on this sequence and the data matrix B and vector Y ensure that the underlying patterns in the data sequence can be accurately captured, thereby making accurate predictions. The calculation of the parameter vector U adopts the principle of least squares to ensure the stability and accuracy of the parameter solution. Through the operation of transposed and inverse matrices, the problem of numerical instability in the calculation process is effectively avoided. As a result, the accurate known numbers a and b can be extracted, making the final prediction results more accurate.

[0052] Optionally, before correcting the first preset threshold based on the prediction result to obtain a corrected value, the method further includes:

[0053] Get activity logs;

[0054] determining whether the activity log includes improvement data;

[0055] If the activity log includes improvement data, obtaining a corresponding second evaluation value based on the improvement data;

[0056] Obtaining a first evaluation value before improvement based on the activity log;

[0057] calculating a change rate based on the first evaluation value and the second evaluation value;

[0058] Determining whether the change rate is greater than a second preset threshold;

[0059] If the rate of change is greater than the second preset threshold, the step of correcting the first preset threshold based on the prediction result to obtain a corrected value is not performed;

[0060] If the rate of change is not greater than the second preset threshold, the step of correcting the first preset threshold based on the prediction result to obtain a corrected value is performed.

[0061] By employing the above technical solution, users can obtain activity logs and analyze them for improvement data, understanding whether factors affecting energy utilization have changed. These changes may stem from production process optimization, the introduction of new technologies, or adjustments to the external environment. Based on this improvement data, the electronic device can obtain the corresponding second evaluation value and compare it with the first evaluation value before the improvement to calculate the rate of change. This ensures that the electronic device fully considers the latest developments in actual production when deciding whether to adjust the first preset threshold, improving the accuracy of the decision. When the rate of change exceeds the second preset threshold, it means that energy utilization has been significantly affected. In this case, directly adjusting the first preset threshold based on the prediction results may not be appropriate. Therefore, pausing the correction step can avoid potential risks caused by blind adjustments. Furthermore, since improvements generally improve energy utilization, while without improvements, energy utilization decreases, blind adjustments may result in inaccurate determination of the energy utilization after improvement. When the rate of change is not greater than the second preset threshold, the electronic device continues to adjust the first preset threshold based on the prediction results, ensuring that the energy management strategy keeps pace with changes in the production environment. This flexibility helps users better cope with various uncertainties and maintain the effectiveness and efficiency of energy management.

[0062] In the second aspect, the present application provides a smart energy integrated control device that adopts the following technical solutions:

[0063] A smart energy comprehensive control device, comprising:

[0064] An acquisition and establishment module, configured to acquire production data and establish a digital twin model based on the production data;

[0065] A first acquisition module is used to acquire total energy consumption data and effective energy consumption data based on the digital twin model;

[0066] A first calculation module, configured to calculate current energy utilization efficiency based on the total energy consumption data and the effective energy consumption data;

[0067] a first determining module, configured to determine whether the current energy utilization efficiency meets a first preset threshold; if the current energy utilization efficiency does not meet the first preset threshold, proceeding to a second obtaining module;

[0068] The second acquisition module is configured to acquire an adjustment strategy based on the current energy utilization efficiency.

[0069] In a third aspect, the present application provides an electronic device that adopts the following technical solution:

[0070] An electronic device comprises a processor coupled to a memory; the processor is configured to execute a computer program stored in the memory, so that the electronic device performs the method described in the first aspect.

[0071] In a fourth aspect, the present application provides a computer-readable storage medium that employs the following technical solutions:

[0072] A computer-readable storage medium includes a computer program or instructions. When the computer program or instructions are executed on a computer, the computer is caused to execute the method according to the first aspect.

[0073] The technical solution of this application obtains corresponding data by establishing a digital twin model, calculates energy utilization efficiency based on the corresponding data, and further implements adjustment strategies to achieve the effect of automatically monitoring energy consumption and performing intelligent processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is a flow chart of the smart energy comprehensive control method according to an embodiment of the present application.

[0075] Figure 2 This is a block diagram of the smart energy comprehensive control device according to an embodiment of the present application.

[0076] Figure 3 It is a block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0077] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, technical users in this field can make non-creative modifications to the present embodiment as needed, but as long as they are within the scope of the claims of the present application, they are protected by the patent law.

[0078] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments of this application, all other embodiments obtained by users of ordinary skill in the art without making any creative efforts are within the scope of protection of this application.

[0079] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document, unless otherwise specified, generally indicates an "or" relationship between the related objects.

[0080] The present application discloses a method for integrated smart energy control. This method can be executed by an electronic device. The electronic device can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be, but is not limited to, a smartphone, a tablet computer, or a desktop computer.

[0081] The embodiment of the present application discloses a smart energy comprehensive control method. Figure 1 ,A smart energy comprehensive control method includes the following main processes (S100~S500):

[0082] Step S100, acquiring production data and establishing a digital twin model based on the production data;

[0083] Step S200: acquiring total energy consumption data and effective energy consumption data based on the digital twin model;

[0084] Step S300, calculating the current energy utilization efficiency based on the total energy consumption data and the effective energy consumption data;

[0085] Step S400, determining whether the current energy utilization efficiency meets the first preset threshold; if the current energy utilization efficiency does not meet the first preset threshold, proceeding to step S500;

[0086] Step S500: obtaining an adjustment strategy based on the current energy utilization efficiency.

[0087] The electronic device obtains production data and then builds a digital twin model based on the production data, which can improve the convenience of users viewing production data and quickly and intuitively understand the production situation. The electronic device obtains total energy consumption data and effective energy data consumption based on the digital twin model, and then calculates the current energy utilization efficiency, that is, divides the effective energy consumption data by the total energy consumption data to obtain the current energy utilization efficiency. The electronic device then determines whether the current energy utilization efficiency meets the first preset threshold, that is, compares the current energy utilization efficiency with the first preset threshold. If the current energy utilization efficiency does not meet the first preset threshold, it indicates that the effective utilization of energy at this time is low, so the electronic device obtains an adjustment strategy based on the current energy utilization efficiency. This allows production adjustments to avoid energy waste.

[0088] Specifically, the adjustment strategy includes: judging whether there is an abnormal energy consumption location based on the digital twin model; if there is an abnormal energy consumption location, obtaining the corresponding current product data based on the abnormal energy consumption location; obtaining the corresponding historical product data based on the abnormal energy consumption location; judging whether the current product data is abnormal based on the historical product data; if the current product data is not abnormal, obtaining the corresponding first production equipment based on the abnormal energy consumption location; outputting a maintenance signal and a production reduction signal based on the first production equipment; if the current product data is abnormal, outputting a stop work signal based on the first production equipment.

[0089] As an optional implementation of the embodiment of the present application, the electronic device determines whether there is an abnormal energy consumption location based on the digital twin model. If there is an energy consumption location, the electronic device obtains the corresponding current product data based on the abnormal energy consumption location. The electronic device obtains the corresponding historical product data based on the abnormal energy consumption location, and then determines whether the current product data is abnormal based on the historical product data. If the current product data is not abnormal, it indicates that the production of the product is not affected. It can be understood that the effective energy has not changed, but the invalid energy consumption has increased. Therefore, the electronic device obtains the corresponding first production equipment based on the abnormal energy consumption location, and then the electronic device outputs a maintenance signal and a production reduction signal based on the first production equipment. Because the invalid energy consumption has increased, it is determined that the first production equipment used to produce the product has an abnormality, so the maintenance signal is output to enable relevant personnel to check and maintain it. At the same time, in order to reduce the possibility of a rapid deterioration of the situation of the first production equipment, a production reduction signal is also output to reduce the working and operating time of the first production equipment. If the current product data is abnormal, it is determined that the situation is more serious, so it affects the effective energy consumption. Therefore, the electronic device outputs a stop work signal to stop the first production equipment.

[0090] As an optional implementation of the embodiment of the present application, before the first production device outputs a stop signal, the method further includes: obtaining a received operating instruction based on the first production device; obtaining a production status before receiving the operating instruction based on the first production device; determining whether the operating instruction meets operating requirements based on the production status; if the operating instruction does not meet the operating requirements, obtaining a first preset delay; within the first preset delay, determining whether the first production device has received a debugging instruction; if the first production device has received the debugging instruction, not executing the step of outputting a stop signal based on the first production device. If the operating instruction meets the operating requirements, executing the step of outputting a stop signal based on the first production device.

[0091] Before the electronic device outputs a stop signal based on the first production device, the electronic device obtains a received operation instruction based on the first production device, then obtains the production status before receiving the operation instruction based on the first production device, and then determines whether the operation instruction meets the operation requirements based on the production status. If the operation instruction does not meet the operation requirements, it indicates that an erroneous operation has occurred. Therefore, the electronic device obtains a first preset delay and then determines whether the first production device has received a debugging instruction within the first preset delay. If the first production device receives the debugging instruction, it indicates that the first electronic device has adjusted from the erroneous operation and returned to the normal production process. Therefore, the electronic device does not execute the step of outputting a stop signal based on the first production device, that is, the erroneous operation is temporary, so there is no need to stop working after the adjustment. If the operation instruction meets the operation requirements, the step of outputting a stop signal is executed. Because a problem has occurred in product production, but it is not caused by erroneous operation of the production device, it indicates that a major problem has occurred, so production needs to be stopped.

[0092] As an optional implementation method of an embodiment of the present application, all historical energy utilization rates are obtained; trend prediction is performed based on the historical energy utilization rates to obtain prediction results; the first preset threshold is corrected based on the prediction results to obtain a corrected value; and the corrected value is assigned to the first preset threshold.

[0093] The electronic device obtains all historical energy utilization rates and then performs a trend forecast based on the historical energy utilization rates to obtain a forecast result. Based on the forecast result, the electronic device modifies the first preset threshold value to obtain a revised value, which is then assigned to the first preset threshold value. Because energy utilization rates may vary at different points in time due to equipment aging and production changes during the production process, the first preset threshold value needs to be adjusted to better determine whether the current energy utilization rate is reasonable, thereby ensuring more accurate judgment.

[0094] Specifically, trend prediction is performed based on historical energy utilization to obtain prediction results, including: The calculation formula of the prediction result is:

[0095] (Formula 1);

[0096] Among them, k is a time point, that is, each historical energy utilization rate corresponds to a time point; Predict the result for the k+1th time point; The original data sequence The first value of is the value of the first historical energy utilization rate; a and b are known numbers.

[0097] Formula 1 can be used to calculate the subsequent predicted value at the current time point. By using the calculated predicted value to correct the first preset threshold, the judgment result can be made more accurate. Moreover, the predicted value calculated by Formula 1 is more accurate and consistent with production conditions. Because there are many uncertainties in the production process, that is, there are many cases of raw data, which can easily lead to low reliability of the raw data. However, in this case, the result calculated by Formula 1 can be more consistent with actual changes, that is, the predicted value obtained is more accurate. Moreover, the calculation method of Formula 1 has a certain adaptability even if the raw data is nonlinear data or real data.

[0098] Construct the original data series from historical energy utilization ;

[0099] For the original data series Accumulate to get a new sequence ;

[0100] Sequence-based Constructing a sequence generating values ​​close to the mean ,sequence ;

[0101] Construct data matrix B and vector Y;

[0102] Based on the calculation formula: , calculate the parameter vector U;

[0103] The calculation formula of parameter vector U is: ;

[0104] Based on the parameter vector U, the known numbers a and b can be extracted;

[0105] in, represents the kth historical energy utilization rate; Represents the cumulative value of the historical energy utilization rate from the first to the kth; the data matrix B is , n is the length of the original data sequence, that is, n is the number of historical energy utilization rates, and the first column of the data matrix B is , the second column of the data matrix B is all 1; the vector Y is dimensional vector, vector Y contains the original data sequence The values ​​from the second element to the last element of ; is the transposed matrix of B, for The inverse matrix of . And, since is based on and The mean of , so the sequence The length of the sequence will be longer than Less 1, that is, for the sequence Calculation, .

[0106] For example, if the original data sequence , cumulatively generate the sequence The calculation is: , , , and so on, until the calculation reaches .

[0107] Generate a sequence close to the mean The calculation is: , , and so on, until the calculation reaches .

[0108] For constructing the data matrix B, the data matrix B is: .

[0109] For constructing vector Y, vector Y is: .

[0110] Afterwards, you can use Calculate the parameter vector U. After solving U, we can get a and b.

[0111] As an optional implementation manner of an embodiment of the present application, before correcting the first preset threshold based on the prediction result to obtain the corrected value, it also includes: obtaining an activity log; determining whether the activity log includes improvement data; if the activity log includes improvement data, obtaining a corresponding second evaluation value based on the improvement data; obtaining the first evaluation value before improvement based on the activity log; calculating the rate of change based on the first evaluation value and the second evaluation value; determining whether the rate of change is greater than the second preset threshold; if the rate of change is greater than the second preset threshold, not executing the step of correcting the first preset threshold based on the prediction result to obtain the corrected value; if the rate of change is not greater than the second preset threshold, executing the step of correcting the first preset threshold based on the prediction result to obtain the corrected value.

[0112] Before the electronic device corrects the first preset threshold based on the prediction result to obtain the corrected value, the electronic device obtains an activity log that records various changes in the production process. The electronic device determines whether the activity log includes improvement data. If the activity log includes improvement data, the electronic device obtains the corresponding second evaluation value based on the improvement data. The electronic device then also obtains the first evaluation value before the improvement and calculates the rate of change, that is, the difference obtained by subtracting the first evaluation value from the second evaluation value, and then divides the difference by the first evaluation value to obtain the rate of change. The electronic device determines whether the rate of change is greater than the second preset threshold. If the rate of change is greater than the second preset threshold, it indicates that there is a significant improvement in production at this time. The improvement includes but is not limited to the update of production equipment and the improvement of production process. In the case of significant improvement, the electronic device does not perform the step of correcting the first preset threshold based on the prediction result, so as to tighten management of the improved situation instead of relaxing management. This is also an evaluation of the improvement effect.

[0113] Figure 2 This is a structural block diagram of a smart energy comprehensive control device 600 provided in an embodiment of the present application, such as Figure 2 As shown, the smart energy comprehensive control device 600 includes:

[0114] An acquisition and establishment module 601 is used to acquire production data and establish a digital twin model based on the production data;

[0115] A first acquisition module 602 is configured to acquire total energy consumption data and effective energy consumption data based on the digital twin model;

[0116] A first calculation module 603 is configured to calculate the current energy utilization efficiency based on the total energy consumption data and the effective energy consumption data;

[0117] The first determination module 604 is used to determine whether the current energy utilization efficiency meets the first preset threshold; if the current energy utilization efficiency does not meet the first preset threshold, the process proceeds to the second acquisition module 605;

[0118] The second acquisition module 605 is configured to acquire an adjustment strategy based on the current energy utilization efficiency.

[0119] Specifically, the second acquisition module 605 includes:

[0120] The first judgment submodule is used to judge whether there is an abnormal energy consumption location based on the digital twin model; if there is an abnormal energy consumption location, the corresponding current product data is obtained based on the abnormal energy consumption location;

[0121] A first acquisition submodule is used to acquire corresponding historical product data based on the abnormal location of energy consumption;

[0122] The second judgment submodule is used to judge whether the current product data is abnormal based on the historical product data; if the current product data is normal, the corresponding first production equipment is obtained based on the location of the abnormal energy consumption; if the current product data is abnormal, the first production equipment outputs a stop signal;

[0123] The first output submodule is configured to output a maintenance signal and a production reduction signal based on the first production equipment.

[0124] In this optional embodiment, the smart energy comprehensive control device 600 further includes:

[0125] A second acquisition submodule is configured to acquire a received operation instruction based on the first production device before outputting a stop operation signal based on the first production device;

[0126] A third acquisition submodule is configured to acquire, based on the first production equipment, a production status before receiving the operation instruction;

[0127] A third judgment submodule is used to judge whether the operation instruction meets the operation requirements based on the production status; if the operation instruction does not meet the operation requirements, obtain a first preset delay;

[0128] The fourth determination submodule is configured to determine, within a first preset delay time, whether the first production device has received a debugging instruction; if the first production device has received the debugging instruction, the step of outputting a stop signal based on the first production device is not performed. If the operation instruction meets the operation requirements, the step of outputting a stop signal based on the first production device is performed.

[0129] In this optional embodiment, the smart energy comprehensive control device 600 further includes:

[0130] The fourth acquisition submodule is used to obtain all historical energy utilization rates;

[0131] A first prediction submodule is used to perform trend prediction based on historical energy utilization to obtain a prediction result;

[0132] A first correction submodule, configured to correct the first preset threshold based on the prediction result to obtain a correction value;

[0133] The first assignment submodule is configured to assign the correction value to a first preset threshold.

[0134] Specifically, the first prediction submodule includes:

[0135] The first calculation submodule is used to calculate the prediction result. The calculation formula of the prediction result is: ; Where k is a time point, that is, each historical energy utilization rate corresponds to a time point; Predict the result for the k+1th time point; The original data sequence The first value of is the value of the first historical energy utilization rate; a and b are known numbers.

[0136] In this optional embodiment, the smart energy comprehensive control device 600 further includes:

[0137] The first construction submodule is used to construct the original data sequence from the historical energy utilization rate ;

[0138] The first accumulation submodule is used to accumulate the original data sequence Accumulate to get a new sequence ;

[0139] The first generation submodule is used to generate Constructing a sequence generating values ​​close to the mean ,sequence ;

[0140] The second construction submodule is used to construct the data matrix B and vector Y;

[0141] The second calculation submodule is used to calculate the parameter vector U. The calculation formula is: After deformation, ;

[0142] The first extraction submodule is used to extract the known numbers a and b based on the parameter vector U; wherein, represents the kth historical energy utilization rate; Represents the cumulative value of the historical energy utilization rate from the first to the kth; the data matrix B is , n is the length of the original data sequence, that is, n is the number of historical energy utilization rates, and the first column of the data matrix B is , the second column of the data matrix B is all 1; the vector Y is dimensional vector, vector Y contains the original data sequence The values ​​from the second element to the last element of ; is the transposed matrix of B, for The inverse matrix of .

[0143] In this optional embodiment, the smart energy comprehensive control device 600 further includes:

[0144] A fifth acquisition submodule, configured to acquire an activity log before correcting the first preset threshold value based on the prediction result to obtain a corrected value;

[0145] a fifth judgment submodule, configured to judge whether the activity log includes improvement data; if the activity log includes improvement data, obtaining a corresponding second evaluation value based on the improvement data;

[0146] a sixth acquisition submodule, configured to acquire a first evaluation value before improvement based on the activity log;

[0147] a third calculation submodule, configured to calculate a change rate based on the first evaluation value and the second evaluation value;

[0148] The sixth judgment submodule is used to determine whether the rate of change is greater than a second preset threshold; if the rate of change is greater than the second preset threshold, the step of correcting the first preset threshold based on the prediction result to obtain a corrected value is not executed; if the rate of change is not greater than the second preset threshold, the step of correcting the first preset threshold based on the prediction result to obtain a corrected value is executed.

[0149] Figure 3 This is a structural block diagram of an electronic device 700 provided in an embodiment of the present application. The electronic device 700 may be a mobile phone, tablet computer, PC, server, etc. Figure 3 As shown, electronic device 700 includes memory 701, processor 702, and communication bus 703. Memory and processor 702 are connected via communication bus 703. Memory 701 stores a computer program that can be loaded by processor 702 and executed as described above for the smart energy integrated control method.

[0150] The memory 701 can be used to store instructions, programs, codes, code sets, or instruction sets. The memory 701 can include a program storage area and a managed data storage area. The program storage area can store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the smart energy integrated control method provided in the above embodiment. The managed data storage area can store managed data involved in the smart energy integrated control method provided in the above embodiment.

[0151] The processor 702 may include one or more processing cores. The processor 702 calls the managed data stored in the memory 701 by running or executing the instructions, programs, code sets or instruction sets stored in the memory 701, performs various functions of the present application and processes managed data. The processor 702 may be at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller and a microprocessor. It is understandable that for different devices, the electronic device used to implement the above-mentioned processor 702 function can also be other, and the embodiments of the present application are not specifically limited.

[0152] The communication bus 703 may include a path for transmitting information between the above components. The communication bus 703 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The communication bus 703 may be divided into an address bus, a managed data bus, a control bus, etc. For ease of representation, Figure 3 Only one double arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0153] An embodiment of the present application provides a computer storage medium storing a computer program that can be loaded by a processor and execute the smart energy comprehensive control method provided in the above embodiment.

[0154] In this embodiment, a computer storage medium may be a tangible device that holds and stores instructions used by an instruction execution device. The computer storage medium may be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer storage medium may be a portable computer disk, a hard disk, a USB flash drive, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a rostrum random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, an optical disc, a magnetic disk, a mechanical encoding device, or any combination thereof.

[0155] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, method, article, or apparatus.

Claims

1. A smart energy comprehensive control method, characterized in that: include: Acquiring production data and establishing a digital twin model based on the production data; Acquire total energy consumption data and effective energy consumption data based on the digital twin model; Calculating current energy utilization efficiency based on the total energy consumption data and the effective energy consumption data; Determining whether the current energy utilization efficiency meets a first preset threshold; If the current energy utilization efficiency does not meet the first preset threshold, obtaining an adjustment strategy based on the current energy utilization efficiency; Also includes: Get all historical energy utilization rates; Performing trend forecasting based on the historical energy utilization rate to obtain a forecast result; Modifying the first preset threshold based on the prediction result to obtain a modified value; assigning the correction value to the first preset threshold; The performing trend forecasting based on the historical energy utilization rate to obtain a forecast result includes: The calculation formula of the prediction result is: ; Among them, k is a time point, that is, each historical energy utilization rate corresponds to a time point; For the Prediction results at each time point; The original data sequence The first value of is the value of the first historical energy utilization rate; a and b are known numbers; Also includes: Construct the original data series from historical energy utilization ; For the original data series Accumulate to get a new sequence ; Sequence-based Constructing a sequence generating values ​​close to the mean ,sequence ; Construct data matrix B and vector Y; Based on the calculation formula: , calculate the parameter vector U; The calculation formula of parameter vector U is: ; Based on the parameter vector U, the known numbers a and b can be extracted; in, represents the kth historical energy utilization rate; Represents the cumulative value of the historical energy utilization rate from the first to the kth; the data matrix B is , n is the length of the original data sequence, that is, n is the number of historical energy utilization rates, and the first column of the data matrix B is , the second column of the data matrix B is all 1; the vector Y is dimensional vector, vector Y contains the original data sequence The values ​​from the second element to the last element of ; is the transposed matrix of B, for The inverse matrix of Before the first preset threshold is corrected based on the prediction result to obtain a corrected value, the method further includes: Get activity logs; determining whether the activity log includes improvement data; If the activity log includes improvement data, obtaining a corresponding second evaluation value based on the improvement data; Obtaining a first evaluation value before improvement based on the activity log; calculating a change rate based on the first evaluation value and the second evaluation value; Determining whether the change rate is greater than a second preset threshold; If the rate of change is greater than the second preset threshold, the step of correcting the first preset threshold based on the prediction result to obtain a corrected value is not performed; If the rate of change is not greater than the second preset threshold, the step of correcting the first preset threshold based on the prediction result to obtain a corrected value is performed.

2. A smart energy comprehensive control method according to claim 1, characterized in that: The adjustment strategy includes: Determining whether there is an abnormal energy consumption location based on the digital twin model; If the energy consumption abnormal location exists, obtaining corresponding current product data based on the energy consumption abnormal location; Acquiring corresponding historical product data based on the abnormal energy consumption location; Determining whether the current product data is abnormal based on the historical product data; If the current product data has no abnormality, obtaining the corresponding first production equipment based on the abnormal energy consumption location; outputting a maintenance signal and a production reduction signal based on the first production equipment; If the current product data is abnormal, a stop operation signal is output based on the first production equipment.

3. A smart energy comprehensive control method according to claim 2, characterized in that: Before outputting a stop-operation signal based on the first production equipment, the method further includes: Acquire a received operation instruction based on the first production device; acquiring, based on the first production equipment, a production status before receiving the operation instruction; Determining whether the operation instruction meets the operation requirements based on the production status; If the operation instruction does not meet the operation requirement, obtaining a first preset delay; within the first preset delay, determining whether the first production device receives a debugging instruction; If the first production device receives the debugging instruction, the step of outputting a stop-operation signal based on the first production device is not performed; If the operation instruction meets the operation requirement, the step of outputting a stop operation signal based on the first production equipment is executed.

4. A smart energy comprehensive control device, characterized in that: include: An acquisition and establishment module, configured to acquire production data and establish a digital twin model based on the production data; A first acquisition module is used to acquire total energy consumption data and effective energy consumption data based on the digital twin model; A first calculation module, configured to calculate current energy utilization efficiency based on the total energy consumption data and the effective energy consumption data; A first judging module, configured to judge whether the current energy utilization efficiency meets a first preset threshold; If the current energy utilization efficiency does not meet the first preset threshold, proceeding to the second acquisition module; The second acquisition module is configured to acquire an adjustment strategy based on the current energy utilization efficiency; The fourth acquisition submodule is used to obtain all historical energy utilization rates; A first prediction submodule is used to perform trend prediction based on historical energy utilization to obtain a prediction result; A first correction submodule, configured to correct the first preset threshold based on the prediction result to obtain a correction value; A first assignment submodule, configured to assign the correction value to a first preset threshold; The first prediction submodule includes: The first calculation submodule is used to calculate the prediction result. The calculation formula of the prediction result is: ; Where k is a time point, that is, each historical energy utilization rate corresponds to a time point; For the Prediction results at each time point; The original data sequence The first value of is the value of the first historical energy utilization rate; a and b are known numbers; The first construction submodule is used to construct the original data sequence from the historical energy utilization rate ; The first accumulation submodule is used to accumulate the original data sequence Accumulate to get a new sequence ; The first generation submodule is used to generate Constructing a sequence generating values ​​close to the mean ,sequence ; The second construction submodule is used to construct the data matrix B and vector Y; The second calculation submodule is used to calculate the parameter vector U. The calculation formula is: After deformation, ; The first extraction submodule is used to extract the known numbers a and b based on the parameter vector U; wherein, represents the kth historical energy utilization rate; Represents the cumulative value of the historical energy utilization rate from the first to the kth; the data matrix B is , n is the length of the original data sequence, that is, n is the number of historical energy utilization rates, and the first column of the data matrix B is , the second column of the data matrix B is all 1; the vector Y is dimensional vector, vector Y contains the original data sequence The values ​​from the second element to the last element of ; is the transposed matrix of B, for The inverse matrix of A fifth acquisition submodule, configured to acquire an activity log before correcting the first preset threshold value based on the prediction result to obtain a corrected value; a fifth judgment submodule, configured to judge whether the activity log includes improvement data; if the activity log includes improvement data, obtaining a corresponding second evaluation value based on the improvement data; a sixth acquisition submodule, configured to acquire a first evaluation value before improvement based on the activity log; a third calculation submodule, configured to calculate a change rate based on the first evaluation value and the second evaluation value; The sixth judgment submodule is used to determine whether the rate of change is greater than a second preset threshold; if the rate of change is greater than the second preset threshold, the step of correcting the first preset threshold based on the prediction result to obtain a corrected value is not executed; if the rate of change is not greater than the second preset threshold, the step of correcting the first preset threshold based on the prediction result to obtain a corrected value is executed.

5. An electronic device, characterized in that: The electronic device comprises a processor coupled to a memory; the processor is configured to execute a computer program stored in the memory, so that the electronic device executes the method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that The method comprises a computer program or an instruction, which, when executed on a computer, causes the computer to execute the method according to any one of claims 1 to 3.

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