A cement grinding comprehensive energy consumption dynamic evaluation method based on multi-source data analysis

Abstract

The application discloses a cement grinding comprehensive energy consumption dynamic evaluation method based on multi-source data analysis, and belongs to the technical field of industrial production energy consumption management. The method comprises the following steps: collecting multi-source operation data of a cement grinding system, performing a pretreatment operation on the multi-source operation data, and generating standardized energy consumption characteristic data; based on a cement grinding energy consumption conduction mechanism and the standardized energy consumption characteristic data, a cement grinding comprehensive energy consumption coupling evaluation model is constructed; the standardized energy consumption characteristic data is input into the cement grinding comprehensive energy consumption coupling evaluation model in real time, dynamic calculation of comprehensive energy consumption is performed, an energy consumption evaluation result is generated, the energy consumption evaluation result is matched with a preset energy efficiency threshold, and a short board positioning result is generated. The application can significantly improve the precision of cement grinding energy consumption evaluation, greatly shorten the response time of energy consumption abnormalities, effectively help cement enterprises reduce unit grinding power consumption, and has significant economic and social benefits.

Description

Technical Field

[0001] This invention relates to the field of industrial production energy consumption management technology, specifically a dynamic evaluation method for the comprehensive energy consumption of cement grinding based on multi-source data analysis. Background Technology

[0002] As a basic raw material industry of the national economy, the cement industry is a typical high-energy-consuming industry. Under the background of the continuous promotion of the "dual carbon" strategy, the industry is facing increasingly strict energy consumption control and green production requirements. As the core link with the highest proportion of electricity consumption in cement production, the energy efficiency control level of the grinding section directly affects the overall energy-saving effect of cement enterprises and the low-carbon transformation process of the industry. At present, the cement production field generally adopts the combined grinding process of roller press and ball mill, and large-scale and high-yield have become the main development direction of grinding systems.

[0003] Existing energy consumption assessment and control in cement grinding adopts a static and singular implementation model, relying solely on scattered parameters for post-event energy consumption accounting. It has not formed a dynamic assessment mechanism that integrates multi-source data, making it impossible to achieve real-time monitoring of energy consumption in the grinding system and accurate identification of high-energy-consumption bottlenecks. As a result, energy consumption control in cement grinding lacks precision and timeliness, making it difficult to meet the high-efficiency and energy-saving control requirements of modern cement production. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic evaluation method for the comprehensive energy consumption of cement grinding based on multi-source data analysis, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for dynamic evaluation of comprehensive energy consumption in cement grinding based on multi-source data analysis, comprising the following steps:

[0006] Step 100: Collect multi-source operating data of the cement grinding system, perform preprocessing operations on the multi-source operating data, and generate standardized energy consumption characteristic data;

[0007] Step 200: Based on the energy consumption transmission mechanism of cement grinding and the standardized energy consumption characteristic data, construct a coupled evaluation model for comprehensive energy consumption of cement grinding;

[0008] Step 300: Input the standardized energy consumption characteristic data into the cement grinding comprehensive energy consumption coupling evaluation model in real time, perform comprehensive energy consumption dynamic calculation, and generate energy consumption evaluation results;

[0009] Step 400: Match the energy consumption assessment results with the preset energy efficiency threshold to generate the bottleneck location results;

[0010] Step 500: Generate an energy consumption optimization and control strategy based on the shortcoming location result, and output the energy consumption optimization and control strategy.

[0011] Preferably, step 100 includes:

[0012] Step 110: Collect equipment operation data, process control data, power consumption data, material characteristic data and working environment data of the cement grinding system to form the multi-source operation data;

[0013] Step 120: Perform outlier removal, data noise reduction, data normalization and spatiotemporal alignment processing on the multi-source operating data in sequence to generate the standardized energy consumption characteristic data.

[0014] Preferably, the outlier removal in step 120 is used to remove abnormal data from the multi-source operating data; the data noise reduction is used to reduce signal interference in the multi-source operating data; the data normalization is used to unify the dimensions of the multi-source operating data; and the spatiotemporal alignment is used to match the time dimension and spatial location of the multi-source operating data.

[0015] Preferably, step 200 includes:

[0016] Step 210: Based on the interaction between materials, grinding media and airflow inside the cement grinding system, determine the energy consumption transmission mechanism of cement grinding;

[0017] Step 220: Combining the energy consumption transmission mechanism of cement grinding with the standardized energy consumption characteristic data, establish a quantitative mapping relationship between the standardized energy consumption characteristic data and the comprehensive energy consumption, and form the coupled evaluation model of comprehensive energy consumption of cement grinding.

[0018] Preferably, the quantification mapping relationship is used to directly determine the corresponding comprehensive energy consumption value of the cement grinding system based on the standardized energy consumption characteristic data.

[0019] Preferably, step 300 includes:

[0020] Step 310: Input the standardized energy consumption characteristic data into the cement grinding comprehensive energy consumption coupling evaluation model in real time to calculate the comprehensive energy consumption value and the section energy consumption ratio data.

[0021] Step 320: Based on the comprehensive energy consumption value and the energy consumption ratio data of the work section, determine the system energy efficiency matching degree and integrate them to form the energy consumption assessment result.

[0022] Preferably, the system energy efficiency matching degree is determined by the comprehensive energy consumption value and the energy consumption ratio data of the work section, and integrated to form the energy consumption assessment result.

[0023] Preferably, the matching analysis in step 400 involves comparing the energy consumption assessment result with the preset energy efficiency threshold, locating the energy consumption anomaly object based on the comparison result, and generating the bottleneck location result.

[0024] Preferably, step 500 includes:

[0025] Step 510: With the optimization objectives of minimizing overall energy consumption, maximizing production output, and stabilizing product quality, determine the optimal combination of operating parameters based on the aforementioned bottleneck identification results;

[0026] Step 520: Generate and output the energy consumption optimization and control strategy based on the optimal combination of operating parameters.

[0027] Preferably, the optimal combination of operating parameters is a set of operating parameters that achieve the optimization objective, and the energy consumption optimization and control strategy is directly generated based on the optimal combination of operating parameters.

[0028] Compared with the prior art, the beneficial effects of the present invention are:

[0029] This invention collects multi-source operating data from cement grinding systems, constructs a coupled evaluation model based on process mechanisms, realizes real-time dynamic evaluation and precise fault location of comprehensive energy consumption in cement grinding, and generates automatically executable optimization and control strategies. This invention can significantly improve the accuracy of cement grinding energy consumption evaluation, greatly shorten the response time of energy consumption anomalies, and effectively help cement enterprises reduce unit grinding power consumption, thus having significant economic and social benefits. Attached Figure Description

[0030] Figure 1 The main flowchart of a dynamic evaluation method for comprehensive energy consumption of cement grinding based on multi-source data analysis is provided in an embodiment of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Please see Figure 1 This invention provides a method for dynamic evaluation of the comprehensive energy consumption of cement grinding based on multi-source data analysis, comprising:

[0033] Step 100: Collect multi-source operating data of the cement grinding system, perform preprocessing operations on the multi-source operating data, and generate standardized energy consumption characteristic data.

[0034] Specifically, step 100 includes:

[0035] Step 110: Collect equipment operation data, process control data, power consumption data, material characteristic data and working environment data of the cement grinding system to form the multi-source operation data;

[0036] Step 120: Perform outlier removal, data noise reduction, data normalization and spatiotemporal alignment processing on the multi-source operating data in sequence to generate the standardized energy consumption characteristic data.

[0037] Step 100 involves the acquisition of multi-source operational data and the generation of standardized energy consumption characteristic data. The multi-source operational data is a collection of energy consumption correlation data for the entire process of the cement grinding system, which serves as the data foundation for dynamic assessment of comprehensive energy consumption. The standardized energy consumption characteristic data is used to eliminate data interference, unify data dimensions, and ensure the consistency and accuracy of subsequent model inputs.

[0038] In this embodiment, data is simultaneously collected using the existing DCS control system, power quality monitoring terminal, online laser particle size analyzer, and temperature and vibration sensors of the cement plant, with the collection frequency uniformly set to 1Hz. The collected equipment operation data, process control data, power consumption data, material characteristic data, and operating environment data are initially spliced ​​together according to the same timestamp to form complete multi-source operation data. This data acquisition method can comprehensively cover all key factors affecting energy consumption in the cement grinding process, avoiding the inaccurate reflection of energy consumption change patterns in the evaluation model due to the omission of a key influencing factor (such as mill temperature or equipment vibration), thereby preventing the distortion of evaluation results.

[0039] Furthermore, outlier removal, data noise reduction, data normalization, and spatiotemporal alignment are sequentially performed on the multi-source operational data. Through the above progressive preprocessing operations, invalid information in the original data is removed, signal fluctuation interference is reduced, the numerical ranges of different types of data are unified, and precise matching of time and spatial dimensions is achieved, ultimately generating the standardized energy consumption characteristic data that can be directly input into the evaluation model.

[0040] Optionally, in the data acquisition stage, an edge computing gateway can be used instead of a centralized acquisition unit. An edge gateway can be deployed on the mill control cabinet side to achieve localized acquisition and preliminary filtering of field data, uploading only valid data to the server. In the preprocessing stage, a sliding window filter can be used instead of conventional filtering. The window size can be adjusted within the range of 5-20 sampling points according to fluctuations in field data, adapting to the real-time processing requirements of continuous streaming data.

[0041] In step 120, outlier removal is used to remove abnormal data from the multi-source operational data; data denoising is used to reduce signal interference in the multi-source operational data; data normalization is used to unify the dimensions of the multi-source operational data; and spatiotemporal alignment is used to match the time dimension and spatial location of the multi-source operational data.

[0042] Furthermore, the outlier removal process is used to remove abnormal data from the multi-source operational data that deviates from the normal range due to equipment failure, sudden changes in operating conditions, or packet loss during transmission. This embodiment uses the 3σ criterion for outlier identification, calculating the mean and standard deviation of each parameter over a continuous 30-minute period. Data exceeding the mean ± 3 times the standard deviation is identified as outlier and deleted. This method is a conventional approach to industrial data processing in this field. The improvement of this invention lies in its application to the preprocessing of multi-source operational data for cement grinding, making it compatible with the subsequent coupled evaluation model.

[0043] The data denoising is used to reduce signal noise caused by electromagnetic interference and environmental vibration in the multi-source operational data. This embodiment employs a wavelet denoising algorithm, selecting the db4 wavelet basis for three-level decomposition and reconstruction, preserving the true trend of data variation. This algorithm is a conventional signal processing method in the field. The improvement of this invention lies in selecting a suitable wavelet basis and number of decomposition levels for the low-frequency fluctuation characteristics of cement grinding data.

[0044] The data normalization is used to unify the dimensions and numerical ranges of the multi-source operational data. This embodiment employs the min-max normalization method, mapping all parameters to the [0,1] interval to eliminate calculation biases caused by differences in the dimensions of different parameters. This method is a conventional data processing technique in the field; the improvement of this invention lies in its application to the unified processing of multi-source heterogeneous cement grinding data.

[0045] The spatiotemporal alignment is used to match and integrate the multi-source operating data according to a unified timestamp and equipment installation location, ensuring that data from different sources can correspond to the same operating time and the same equipment location, thereby achieving spatiotemporal data coordination.

[0046] Through the above four processes, the multi-source operating data can be transformed into standard data with unified rules and clear characteristics, providing reliable data support for subsequent model construction and dynamic calculation, and effectively improving the accuracy and stability of subsequent energy consumption calculation.

[0047] Step 200: Based on the energy consumption transmission mechanism of cement grinding and the standardized energy consumption characteristic data, construct a coupled evaluation model for comprehensive energy consumption of cement grinding.

[0048] Specifically, step 200 includes:

[0049] Step 210: Based on the interaction between materials, grinding media and airflow inside the cement grinding system, determine the energy consumption transmission mechanism of cement grinding;

[0050] Step 220: Combining the energy consumption transmission mechanism of cement grinding with the standardized energy consumption characteristic data, establish a quantitative mapping relationship between the standardized energy consumption characteristic data and the comprehensive energy consumption, and form the coupled evaluation model of comprehensive energy consumption of cement grinding.

[0051] In this embodiment, step 200 is used to construct a coupled evaluation model for the comprehensive energy consumption of cement grinding, thereby realizing the correlation between multi-source data and energy consumption quantification.

[0052] The energy transmission mechanism in cement grinding is determined based on the interaction between materials, grinding media, and airflow within the cement grinding system. It fully reflects the energy transfer, loss, and transformation patterns of materials during the grinding process: after being compressed into a cake by a roller press, the material enters the ball mill, where it is ground finely under the impact and shearing action of the grinding media. The airflow carries the fine powder into the classifier system. Energy is gradually lost during material crushing, media movement, and airflow transport. This mechanism is a well-known foundation in grinding kinetics and provides a clear basis for understanding the energy transmission path.

[0053] Furthermore, combining the energy consumption transmission mechanism of cement grinding with the standardized energy consumption characteristic data, a quantitative mapping relationship between the standardized energy consumption characteristic data and the comprehensive energy consumption is established. This mapping relationship is constructed by combining a BP neural network with mechanistic constraints, using the standardized energy consumption characteristic data as input layer nodes and the comprehensive energy consumption as output layer nodes, with two hidden layers, each containing 16-64 neurons (adjustable according to the amount of data). Simultaneously, the basic formula of grinding kinetics is added as a constraint condition to the loss function to avoid calculation results that violate the process mechanism. Finally, a coupled evaluation model for the comprehensive energy consumption of cement grinding, capable of real-time calculation and dynamic output, is formed, solving the technical shortcomings of traditional evaluation models that only consider a single factor (such as output) and cannot simultaneously consider the influence of the interaction of multiple factors such as materials, grinding media, and airflow on energy consumption.

[0054] The quantitative mapping relationship is used to directly determine the corresponding comprehensive energy consumption value of the cement grinding system based on the standardized energy consumption characteristic data.

[0055] Understandably, the function of the quantification mapping relationship is to directly convert the input standardized energy consumption characteristic data into the corresponding comprehensive energy consumption value of the cement grinding system without the need for manual calculation, thereby automating and dynamizing the evaluation process.

[0056] This quantitative mapping relationship is not a simple data fitting relationship, but a constrained mapping relationship constructed by combining the cement grinding process mechanism and multi-source data characteristics. The constraints include: the grinding media filling rate is positively correlated with energy consumption, the system ventilation volume is a quadratic function of energy consumption, and the particle size of the material entering the mill is positively correlated with energy consumption, all of which are well-known process laws in the field.

[0057] This mapping relationship can be adapted to energy consumption calculation scenarios under different operating conditions and equipment parameters, ensuring the accuracy and universality of the evaluation results. In this embodiment, under stable operating conditions, this quantitative mapping relationship has high fitting accuracy, and the model output results match the actual operating conditions well, meeting the evaluation accuracy requirements of industrial sites.

[0058] Step 300: Input the standardized energy consumption characteristic data into the cement grinding comprehensive energy consumption coupling evaluation model in real time, perform comprehensive energy consumption dynamic calculation, and generate energy consumption evaluation results.

[0059] Specifically, step 300 includes:

[0060] Step 310: Input the standardized energy consumption characteristic data into the cement grinding comprehensive energy consumption coupling evaluation model in real time to calculate the comprehensive energy consumption value and the section energy consumption ratio data.

[0061] Step 320: Based on the comprehensive energy consumption value and the energy consumption ratio data of the work section, determine the system energy efficiency matching degree and integrate them to form the energy consumption assessment result.

[0062] Step 300 is used to generate integrated energy consumption dynamic calculation and energy consumption assessment results, realizing real-time conversion from data input to result output.

[0063] "Real-time input" refers to continuously inputting the standardized energy consumption characteristic data generated at each sampling moment into the cement grinding integrated energy consumption coupling evaluation model at a frequency of 1Hz, the same as the data acquisition frequency, rather than batch input or timed input. This method ensures that the energy consumption calculation results remain synchronized with the on-site operating conditions, achieving true dynamic evaluation.

[0064] Furthermore, "dynamic calculation" refers to the model updating the comprehensive energy consumption value and the energy consumption ratio of the work section every second based on the standardized energy consumption characteristic data input in real time, rather than performing static statistical calculations on an hourly or daily basis. Dynamic calculation can promptly capture energy consumption changes caused by fluctuations in operating conditions, providing real-time basis for subsequent bottleneck identification and control.

[0065] The standardized energy consumption characteristic data is input in real time into the cement grinding comprehensive energy consumption coupling evaluation model. Through the quantitative mapping relationship within the model, the overall comprehensive energy consumption value of the cement grinding system and the energy consumption ratio of each sub-section are quickly calculated. The comprehensive energy consumption value is the unit product power consumption, expressed in kWh / t. The sub-sections are divided according to the grinding process flow into roller pressing section, ball mill coarse grinding section, ball mill fine grinding section, air classification section, and ventilation section. The energy consumption ratio of each sub-section is the ratio of its power consumption to the total power consumption of the system. The above data can intuitively reflect the overall energy consumption level of the system and the energy contribution of each sub-section.

[0066] Furthermore, the system energy efficiency matching degree is determined based on the comprehensive energy consumption value and the energy consumption ratio data of the work section. This matching degree is used to characterize the degree of coordination and adaptation of the operating parameters of each link in the system. The comprehensive energy consumption value, the energy consumption ratio data of the work section, and the system energy efficiency matching degree are integrated to form a complete energy consumption assessment result, providing a comprehensive assessment basis for subsequent weakness identification.

[0067] The system energy efficiency matching degree is determined by the comprehensive energy consumption value and the energy consumption ratio data of the work section, and integrated to form the energy consumption assessment result.

[0068] Furthermore, the system energy efficiency matching degree is determined by the comprehensive energy consumption value and the energy consumption ratio data of the process section. Its value directly reflects the operating energy efficiency status of the cement grinding system and is a key indicator for judging whether there is an energy consumption anomaly in the system.

[0069] In this embodiment, the overall energy consumption value is compared with the industry's advanced value to obtain the overall system energy efficiency score; the energy consumption ratio data of each work section is compared with the theoretical optimal ratio of each work section to obtain the work section energy efficiency score. The overall system energy efficiency score and the work section energy efficiency score are weighted according to preset weights to obtain the system energy efficiency matching degree. The matching degree ranges from 0 to 100 points, with a higher score indicating a better system energy efficiency matching degree.

[0070] The energy consumption value, the energy consumption ratio of the work section, and the system energy efficiency matching degree are integrated to form the energy consumption assessment result. This ensures that the assessment result includes information on overall energy consumption, work section distribution, and collaborative adaptability, avoiding the problem that a single indicator cannot fully reflect the system's energy efficiency status and improving the comprehensiveness and guidance of the assessment result.

[0071] Step 400: Match the energy consumption assessment results with the preset energy efficiency threshold to generate the bottleneck location results.

[0072] The matching analysis involves comparing the energy consumption assessment results with the preset energy efficiency threshold, locating energy consumption anomalies based on the comparison results, and generating the bottleneck location results.

[0073] In this embodiment, the preset energy efficiency threshold is an energy efficiency judgment benchmark set based on cement grinding industry standards, the company's historical best operating conditions, and equipment design parameters.

[0074] The preset energy efficiency thresholds include a comprehensive energy consumption threshold and a section energy consumption ratio threshold. The comprehensive energy consumption threshold can be set according to the advanced value, access value, or limit value of "Energy Consumption Limits per Unit Product of Cement" GB16780-2021; the section energy consumption ratio threshold is set according to the average ratio of the enterprise's best operating conditions in the past year, and is allowed to fluctuate by 5%-10%. Enterprises can adjust the above thresholds according to their own production realities.

[0075] Furthermore, in this embodiment, "matching analysis" is specifically manifested as an item-by-item comparison operation: each parameter in the energy consumption assessment result (including the comprehensive energy consumption value, the energy consumption ratio of each work section, and the system energy efficiency matching degree) is compared one-to-one with the corresponding preset energy efficiency threshold, rather than a holistic comparison. Item-by-item comparison can accurately locate specific abnormal parameters and avoid overlooking subtle energy consumption shortcomings.

[0076] If the overall energy consumption value exceeds the threshold or the energy consumption ratio of a certain work section exceeds the threshold range, the object is determined to be an energy-consuming object. Energy-consuming objects include work sections with excessive energy consumption and equipment units with abnormal operating parameters. Based on the comparison results, the energy consumption bottleneck is accurately located, generating the bottleneck location result. This achieves a precise transformation from macro-level assessment to micro-level bottleneck location, providing clear optimization targets for subsequent control strategy generation.

[0077] Step 500: Generate an energy consumption optimization and control strategy based on the shortcoming location result, and output the energy consumption optimization and control strategy.

[0078] Specifically, step 500 includes:

[0079] Step 510: With the optimization objectives of minimizing overall energy consumption, maximizing production output, and stabilizing product quality, determine the optimal combination of operating parameters based on the aforementioned bottleneck identification results;

[0080] Step 520: Generate and output the energy consumption optimization and control strategy based on the optimal combination of operating parameters.

[0081] Step 500 is the energy consumption optimization and control strategy generation stage, which is used to realize closed-loop control from identifying the weak link to outputting the optimization scheme.

[0082] In this embodiment, the optimization objectives are to minimize overall energy consumption, maximize production output, and stabilize product quality. These objectives align with the actual needs of cement enterprises for synergistic production and energy conservation, taking into account energy efficiency, production capacity, and product quality, thus avoiding production imbalances caused by a single optimization objective.

[0083] Based on the aforementioned bottleneck identification results, a multi-objective genetic algorithm is used to solve for the constraints. For example, the constraints include: roller press pressure range of 8-12 MPa, ball mill speed range of 14-16 r / min, system ventilation range of 30000-40000 m³ / h, and finished cement residue ≤10% on a 45 μm sieve, all of which are typical operating parameters for cement grinding systems. The optimal combination of operating parameters is obtained, representing the best parameter configuration that simultaneously satisfies all optimization objectives.

[0084] Based on the optimal combination of operating parameters, an energy consumption optimization and control strategy that can be directly executed is generated and output, so that the control strategy has a clear basis for execution and is feasible.

[0085] The optimal combination of operating parameters is a set of operating parameters that achieve the optimization objective, and the energy consumption optimization and control strategy is directly generated based on the optimal combination of operating parameters.

[0086] Furthermore, the optimal operating parameter combination is a set of operating parameters for achieving the optimization goal, including the optimal set values ​​of all key parameters such as equipment operation and process control, which can be directly used to guide the adjustment of the operating conditions of the cement grinding system.

[0087] This embodiment transforms the optimal combination of operating parameters into standardized control commands in the Modbus-RTU protocol format, which is commonly used in industrial settings. The commands include parameter addresses, setpoints, and execution times, and are transmitted to the DCS control system via industrial Ethernet. Upon receiving the commands, the DCS control system automatically adjusts the operating parameters of the corresponding equipment without manual intervention, thus achieving automated energy consumption optimization.

[0088] This method can effectively improve the energy efficiency control of cement grinding systems and reduce manual operation costs. In this embodiment, the typical response time for the control command is ≤15s, which can meet the real-time control needs of industrial sites.

[0089] Alternatively, the cement grinding comprehensive energy consumption coupled evaluation model can also be constructed using a random forest algorithm instead of a BP neural network. Specifically, using the standardized energy consumption feature data as input features and comprehensive energy consumption as output label, a random forest model containing 100 decision trees is trained, while incorporating the same process mechanism constraints.

[0090] It should be noted that this alternative embodiment can also achieve the technical effects of the present invention, proving that the scope of protection of the present invention is not limited to a specific algorithm type.

[0091] It should also be noted that this invention applies to all cement grinding systems employing a combined roller press and ball mill grinding process, including but not limited to cement mill systems of different specifications such as Φ3.2×13m, Φ3.8×12m, and Φ4.2×13m. For systems employing other grinding processes (such as vertical mill final grinding), those skilled in the art can, based on the disclosure of this invention, apply the method of this invention to such systems by adjusting the described cement grinding energy consumption transmission mechanism and the preset energy efficiency threshold; this also falls within the protection scope of this invention.

[0092] In summary, this invention, by collecting multi-source operational data from cement grinding systems and constructing a coupled evaluation model based on process mechanisms, achieves real-time dynamic evaluation and precise bottleneck identification of the comprehensive energy consumption of cement grinding, and generates automatically executable optimization and control strategies. This invention can significantly improve the accuracy of cement grinding energy consumption evaluation, greatly shorten the response time to energy consumption anomalies, and effectively help cement enterprises reduce unit grinding power consumption, thus having significant economic and social benefits.

[0093] Furthermore, it should be noted that the combination of the various technical features in this case is not limited to the combination methods described in the claims of this case or the combination methods described in the specific embodiments. All technical features described in this case can be freely combined or combined in any way, unless they contradict each other.

[0094] It should be noted that the above examples are merely specific embodiments of the present invention, and the present invention is obviously not limited to the above embodiments, with many similar variations. All modifications that can be directly derived or conceived by those skilled in the art from the content disclosed in this invention should fall within the protection scope of this invention.

[0095] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A dynamic evaluation method for the comprehensive energy consumption of cement grinding based on multi-source data analysis, characterized in that, Includes the following steps: Step 100: Collect multi-source operating data of the cement grinding system, perform preprocessing operations on the multi-source operating data, and generate standardized energy consumption characteristic data; Step 200: Based on the energy consumption transmission mechanism of cement grinding and the standardized energy consumption characteristic data, construct a coupled evaluation model for comprehensive energy consumption of cement grinding; Step 300: Input the standardized energy consumption characteristic data into the cement grinding comprehensive energy consumption coupling evaluation model in real time, perform comprehensive energy consumption dynamic calculation, and generate energy consumption evaluation results; Step 400: Match the energy consumption assessment results with the preset energy efficiency threshold to generate the bottleneck location results; Step 500: Generate an energy consumption optimization and control strategy based on the shortcoming location result, and output the energy consumption optimization and control strategy.

2. The method for dynamic evaluation of comprehensive energy consumption in cement grinding based on multi-source data analysis according to claim 1, characterized in that, Step 100 includes: Step 110: Collect equipment operation data, process control data, power consumption data, material characteristic data and working environment data of the cement grinding system to form the multi-source operation data; Step 120: Perform outlier removal, data noise reduction, data normalization and spatiotemporal alignment processing on the multi-source operating data in sequence to generate the standardized energy consumption characteristic data.

3. The method for dynamic evaluation of comprehensive energy consumption in cement grinding based on multi-source data analysis according to claim 2, characterized in that, The outlier removal in step 120 is used to remove abnormal data from the multi-source operating data; the data noise reduction is used to reduce signal interference in the multi-source operating data; and the data normalization is used to unify the dimensions of the multi-source operating data. The spatiotemporal alignment is used to match the time dimension and spatial location of the multi-source operational data.

4. The method for dynamic evaluation of comprehensive energy consumption in cement grinding based on multi-source data analysis according to claim 1, characterized in that, Step 200 includes: Step 210: Based on the interaction between materials, grinding media and airflow inside the cement grinding system, determine the energy consumption transmission mechanism of cement grinding; Step 220: Combining the energy consumption transmission mechanism of cement grinding with the standardized energy consumption characteristic data, establish a quantitative mapping relationship between the standardized energy consumption characteristic data and the comprehensive energy consumption, and form the coupled evaluation model of comprehensive energy consumption of cement grinding.

5. The method for dynamic evaluation of comprehensive energy consumption in cement grinding based on multi-source data analysis according to claim 4, characterized in that, The quantitative mapping relationship is used to directly determine the corresponding comprehensive energy consumption value of the cement grinding system based on the standardized energy consumption characteristic data.

6. The method for dynamic evaluation of comprehensive energy consumption in cement grinding based on multi-source data analysis according to claim 1, characterized in that, Step 300 includes: Step 310: Input the standardized energy consumption characteristic data into the cement grinding comprehensive energy consumption coupling evaluation model in real time to calculate the comprehensive energy consumption value and the section energy consumption ratio data. Step 320: Based on the comprehensive energy consumption value and the energy consumption ratio data of the work section, determine the system energy efficiency matching degree and integrate them to form the energy consumption assessment result.

7. The method for dynamic evaluation of comprehensive energy consumption in cement grinding based on multi-source data analysis according to claim 6, characterized in that, The system energy efficiency matching degree is determined by the comprehensive energy consumption value and the energy consumption ratio data of the work section, and integrated to form the energy consumption assessment result.

8. The method for dynamic evaluation of comprehensive energy consumption in cement grinding based on multi-source data analysis according to claim 1, characterized in that, The matching analysis in step 400 involves comparing the energy consumption assessment results with the preset energy efficiency threshold, locating energy consumption anomalies based on the comparison results, and generating the bottleneck location results.

9. The method for dynamic evaluation of comprehensive energy consumption in cement grinding based on multi-source data analysis according to claim 1, characterized in that, Step 500 includes: Step 510: With the optimization objectives of minimizing overall energy consumption, maximizing production output, and stabilizing product quality, determine the optimal combination of operating parameters based on the aforementioned bottleneck identification results; Step 520: Generate and output the energy consumption optimization and control strategy based on the optimal combination of operating parameters.

10. The method for dynamic evaluation of comprehensive energy consumption of cement grinding based on multi-source data analysis according to claim 9, characterized in that, The optimal combination of operating parameters is a set of operating parameters that achieves the optimization objective, and the energy consumption optimization and control strategy is directly generated based on the optimal combination of operating parameters.

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