An energy-saving control method and device for a complete set of equipment for recycling lithium battery resources.

Abstract

An energy-saving control method and apparatus for a complete set of equipment for recycling lithium battery resources. In this method, operating parameters and material characteristic information of a first set of equipment are collected, as well as operating parameters and material conveying information of a second set of equipment. Based on the material characteristic information, the dynamic operating intensity is determined; based on the material conveying information, the dynamic operating power is determined; according to the dynamic operating intensity, target operating parameters are queried; based on the parameter difference between the target operating parameters and the operating parameters of the first set of equipment, a first control command is calculated and generated; based on the power difference between the dynamic operating power and the operating parameters of the second set of equipment, a second control command is calculated and generated; based on the operating parameters of the second set of equipment and the material conveying information, a linkage start command and a linkage stop command are determined, and a system collaborative scheduling command is generated; based on the system collaborative scheduling command, a final execution command sequence is generated and sent to the complete set of equipment. Implementing this technical solution enables real-time perception and intelligent control of the operating status of the complete set of equipment.

Description

Technical Field

[0001] This application relates to the technical field of intelligent control of lithium battery recycling equipment, specifically to an energy-saving control method and device for a complete set of equipment for recycling lithium battery resources. Background Technology

[0002] In existing lithium battery recycling production lines, the complete sets of equipment typically employ a relatively rudimentary independent control mode. For core processing equipment such as crushers and kneaders, their operating parameters (such as speed and torque) are often kept constant throughout the entire production batch, operating at "full load and constant speed" regardless of the state of the material being processed. Similarly, for auxiliary conveying equipment such as pneumatic conveyors and conveyor belts, their operating power is usually set to a fixed value to ensure normal operation even at maximum material conveying capacity. At the system level, the start-up and shutdown operations of each equipment unit largely rely on manual observation and intervention, lacking an automated linkage mechanism.

[0003] The aforementioned existing technical solutions suffer from a fundamental technical problem: the complete set of equipment lacks a system-level collaborative control logic that can uniformly link the operating status of each equipment unit with real-time material handling requirements. This lack of control logic leads to a severe disconnect between equipment operating energy consumption and actual load. Specifically, the operating intensity of individual equipment cannot adaptively adjust according to changes in material characteristics. Furthermore, upstream and downstream equipment cannot be started and stopped in an orderly manner according to the material conveying rhythm. This results in equipment operating in unnecessary idle or inefficient states for extended periods, causing systemic energy waste and significantly increasing the processing cost per unit product. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides an energy-saving control method and apparatus for a complete set of equipment for recycling lithium battery resources.

[0005] The first aspect of this application provides an energy-saving control method for a complete set of equipment for recycling lithium battery resources: The system collects in real time the operating parameters and material characteristics of the first type of equipment in the complete set of equipment, and collects the operating parameters and material conveying information of the second type of equipment in the complete set of equipment. The second type of equipment is used to convey materials to the first type of equipment. Based on the material characteristic information, the dynamic operating intensity of the first type of equipment is determined through a preset material characteristic and operating parameter association model. Based on the material conveying information, the dynamic operating power of the second type of equipment is determined through a preset material conveying and energy consumption correlation model; The target operating parameters are obtained by querying the preset intensity parameter correspondence based on the dynamic operating intensity, and a first control command is generated based on the parameter difference between the target operating parameters and the first device operating parameters. Based on the power difference between the dynamic operating power and the operating parameters of the second device, a second control command is calculated and generated. Based on the operating parameters of the second equipment and the material conveying information, a linkage start command and a linkage stop command for controlling the first type of equipment are determined, and a system coordinated scheduling command is generated based on the linkage start command and the linkage stop command. Based on the system coordinated scheduling instruction, the first control instruction and the second control instruction are processed collaboratively to generate a final execution instruction sequence, and the final execution instruction sequence is sent to the complete set of equipment.

[0006] By adopting the above technical solutions, real-time perception and intelligent control of the operating status of the complete set of equipment are achieved: The operating intensity of the first type of equipment is dynamically adjusted based on material characteristic information, enabling it to adapt to changes in material state and avoiding the energy consumption and load mismatch problem caused by constant-speed operation in the past. Simultaneously, the operating power of the second type of equipment is dynamically adjusted according to material conveying information, ensuring that the conveying equipment operates efficiently only under necessary loads, eliminating energy waste from idling or inefficient operation. Furthermore, the coordinated start-up and shutdown of upstream and downstream equipment is achieved through system-wide collaborative scheduling commands, ensuring close coordination between equipment operating rhythm and material processing flow, reducing equipment idleness or over-operation caused by untimely manual intervention. Ultimately, these measures ensure that equipment operating energy consumption precisely corresponds to actual processing needs, significantly reducing overall system energy consumption and unit product processing costs.

[0007] Optionally, determining the dynamic operating intensity of the first type of equipment based on the material characteristic information and through a preset material characteristic and operating parameter correlation model includes: The material characteristic information is parsed into material hardness data, material moisture content data, and material bulk density data; The material hardness data is multiplied by a preset first weighting coefficient to obtain a first strength component, the material accumulation data is multiplied by a preset second weighting coefficient to obtain a second strength component, and the first strength component and the second strength component are summed to obtain a basic strength value. Based on the material moisture content data, the strength correction coefficient is queried and determined in the preset moisture content correction coefficient mapping relationship; The dynamic operating strength is obtained by multiplying the basic strength value by the strength correction coefficient.

[0008] By adopting the above technical solution, refined and adaptive adjustment of the operating intensity of the first type of equipment is achieved. By analyzing material characteristics into multi-dimensional data such as hardness, bulk density, and moisture content, and assigning weighted calculations to each, the basic intensity value can comprehensively reflect the core load requirements of material processing, overcoming the limitations of single-parameter judgment. Furthermore, an intensity correction coefficient based on moisture content is introduced to effectively address equipment load changes that may be caused by damp materials, preventing equipment overload or abnormal energy consumption due to sudden changes in material state. Finally, this method, through a data-driven calculation model, transforms the originally constant operating intensity into parameters that are dynamically optimized according to material characteristics, ensuring that the equipment always operates at the optimal intensity matching the actual material state, significantly reducing ineffective energy consumption while maintaining processing efficiency.

[0009] Optionally, determining the dynamic operating power of the second type of equipment based on the material conveying information and through a preset material conveying and energy consumption correlation model includes: Based on the material conveying information, determine the real-time material flow rate data; Based on the real-time material flow rate data, the predicted target power of the second type of equipment is predicted through the material conveying and energy consumption correlation model. The baseline energy consumption value corresponding to the real-time material flow rate data is retrieved from the preset energy consumption strategy library; The dynamic operating power is calculated based on the predicted target power and the baseline energy consumption value.

[0010] By adopting the above technical solution, precise and on-demand control of the operating power of the second type of equipment is achieved: the target power required by the equipment is dynamically predicted by real-time material flow rate data, enabling the equipment power output to closely follow changes in the actual conveying load, fundamentally changing the previous extensive mode of fixed high-power operation to cope with maximum flow; further, calibration calculations are performed in conjunction with benchmark energy consumption values ​​to ensure that the determined dynamic operating power meets both the current conveying needs and the system's preset optimal energy efficiency principle. Ultimately, this method enables the energy consumption of the conveying equipment to match the actual material handling workload in real time, effectively eliminating energy waste during low flow or no material load periods, and significantly improving the energy utilization efficiency of the conveying process.

[0011] Optionally, calculating the dynamic operating power based on the predicted target power and the baseline energy consumption value includes: Calculate the power deviation between the predicted target power and the baseline energy consumption value, and multiply the power deviation by a preset proportional gain parameter to obtain the proportional component; The power deviation is integrated to obtain a historical integral value, and the historical integral value is multiplied by a preset integral gain parameter to obtain the integral component; The power deviation is differentiated to obtain the rate of change, and the rate of change is multiplied by a preset differential gain parameter to obtain the differential component; The power correction amount is obtained by summing the proportional component, the integral component, and the differential component. The dynamic operating power is obtained by summing the power correction amount with the predicted target power.

[0012] By adopting the above technical solution, precise, stable, and adaptive closed-loop control of dynamic operating power is achieved. By introducing a PID control algorithm, the system can dynamically respond in multiple dimensions to the power deviation between the predicted target power and the baseline energy consumption value. The proportional component ensures rapid response and immediate correction to the power deviation; the integral component effectively eliminates the system's steady-state error by accumulating historical deviations, avoiding long-term deviations in power regulation; and the derivative component suppresses excessive power regulation and system oscillations by predicting the trend of deviation changes. This composite control strategy ensures that the final calculated dynamic operating power not only quickly tracks the load demand caused by changes in material flow rate but also maintains the stability of the output power, effectively avoiding equipment shocks and additional energy consumption caused by frequent or drastic power adjustments. Thus, while meeting conveying requirements, it achieves smooth optimization of the operating power of the second type of equipment and further improves energy efficiency.

[0013] Optionally, the step of querying the target operating parameters from a preset intensity parameter correspondence based on the dynamic operating intensity, and calculating and generating a first control command based on the parameter difference between the target operating parameters and the first device operating parameters includes: From the strength parameter correspondence, the target crushing speed and target kneading torque are obtained by querying the dynamic operating intensity. Analyze the operating parameters of the first device to obtain the current crushing speed and current kneading torque; Calculate the speed difference between the target crushing speed and the current crushing speed, and the torque difference between the target kneading torque and the current kneading torque, respectively. Based on the preset maximum parameter change rate, the speed difference and the torque difference are subjected to gradient processing to generate a step-by-step adjustment command sequence; The step-by-step adjustment instruction sequence is encapsulated to obtain the first control instruction.

[0014] By adopting the above technical solution, the stability and precision of the adjustment process of the operating parameters of the first type of equipment are achieved: by mapping the dynamic operating intensity to specific target crushing speed and kneading torque, the control commands are precisely matched with the material processing requirements; after calculating the real-time parameter difference, gradient processing is performed based on the maximum parameter change rate to generate a step-by-step adjustment command sequence, which effectively avoids abrupt changes in the equipment operating parameters. This prevents mechanical shock and equipment damage caused by sudden and large changes in speed or torque, and ensures the smoothness of the equipment's operating condition transition. This allows the core processing equipment to adapt to changes in material characteristics while maintaining the stability of its working state. Thus, while ensuring processing efficiency, the equipment's operating efficiency is further optimized and its service life is extended.

[0015] Optionally, the step of determining the linkage start-up command and linkage stop command for controlling the first type of equipment based on the operating parameters of the second equipment and the material conveying information, and generating the system coordinated scheduling command based on the linkage start-up command and the linkage stop command, includes: Based on the operating parameters of the second equipment and the material conveying information, determine whether the second type of equipment has been started and has begun conveying materials; If the second type of equipment has been started and has begun conveying materials, then the linkage start command to start the first type of equipment is generated according to the preset material conveying time delay. Monitor the material conveying status of the second type of equipment. When the material conveying status is stopped, generate the linkage shutdown command to stop the first type of equipment. The linkage shutdown command is configured with a shutdown delay time to ensure that the first type of equipment has processed all the received materials. The coordinated start command and the coordinated stop command are encapsulated to form the system coordinated scheduling command.

[0016] By adopting the above technical solution, precise timing-based coordinated control between upstream and downstream equipment is achieved: By monitoring the start / stop status and material conveying status of the second type of equipment in real time, the system can accurately perceive the start and end points of material conveying; the linkage start command generated based on the material transmission time delay ensures that the first type of equipment starts in time before the material arrives, avoiding energy consumption from equipment idling and ensuring the continuity of the production process; while the linkage stop command configured with a stop delay time ensures that the first type of equipment can complete all processing work on the received materials, effectively preventing material residue or blockage within the equipment. Ultimately, this coordinated mechanism enables the entire set of equipment to operate as a whole, eliminating problems such as equipment waiting, idling, or incomplete material processing caused by untimely manual operation or misjudgment. While ensuring production quality, it minimizes the system's ineffective operating time, achieving significant energy-saving effects.

[0017] Optionally, the method further includes: Before issuing the first control command, determine whether the first type of device is in a ready-to-start state; If the first type of device is in a standby state, the first control command is stopped from being issued, and a startup command sequence is generated based on a preset hierarchical soft-start strategy. The startup command sequence contains multiple power step commands. The power step instructions are issued sequentially. When the operating parameters of the first device reach the preset operating threshold, the first control instruction is issued.

[0018] By adopting the above technical solution, smooth control and energy consumption optimization of the first type of equipment startup process are achieved: by judging the equipment status and activating a graded soft-start strategy when ready to start, a gradual power step command is used instead of a one-time full-power start, which effectively suppresses the instantaneous current surge generated during equipment startup and significantly reduces the peak power consumption during the startup phase; at the same time, this smooth power ramp-up process reduces the instantaneous stress impact on the mechanical transmission system, which helps to extend the service life of the equipment. Once the equipment operating parameters reach a stable threshold, normal operation control is switched to ensure a smooth transition from standstill to operation, achieving energy saving while also improving the safety and reliability of equipment startup.

[0019] A second aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any of the foregoing.

[0020] A third aspect of this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described in any of the preceding descriptions.

[0021] A fourth aspect of this application provides a computer program product that, when run on an electronic device, causes the electronic device to perform the method as described in any of the preceding claims.

[0022] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: By sensing material characteristics and conveying status in real time, dynamic optimization of the operating intensity of core processing equipment and precise matching of conveying equipment power are achieved. The use of PID control algorithm and gradient adjustment strategy ensures the stability and accuracy of equipment parameter adjustment. Through intelligent linkage control of upstream and downstream equipment, equipment idling and waiting time are eliminated. Combined with a graded soft start mechanism, the peak energy consumption and mechanical shock during equipment start-up and shutdown are further reduced. Finally, a complete energy-saving system from single-machine adaptive adjustment to system collaborative control is constructed, which significantly reduces the overall energy consumption and operating cost of the complete set of equipment while ensuring processing efficiency and equipment life. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the system architecture of an energy-saving control method for a complete set of equipment for recycling lithium battery resources according to this application; Figure 2 This is a schematic flowchart of an energy-saving control method for a complete set of equipment for recycling lithium battery resources disclosed in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application.

[0024] Explanation of reference numerals in the attached figures: 100, System architecture; 101, First terminal device; 102, Second terminal device; 103, Third terminal device; 104, Network; 105, Server; 301, Processor; 302, Communication bus; 303, User interface; 304, Network interface; 305, Memory. Detailed Implementation

[0025] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0026] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0027] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0028] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0029] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as model training applications, video recognition applications, web browser applications, social platform software, etc.

[0030] Terminal devices 101, 102, and 103 can be either hardware or software. When terminal devices 101, 102, and 103 are hardware, they can be various electronic devices with displays, including but not limited to smartphones, tablets, e-book readers, MP3 (Moving Picture Experts Group Audio Layer III) players, MP3 (Moving Picture Experts Group Audio Layer IV) players, laptops, and desktop computers, etc. When terminal devices 101, 102, and 103 are software, they can be installed in the aforementioned electronic devices. They can be implemented as multiple software programs or software modules (e.g., multiple software programs or software modules used to provide distributed services) or as a single software program or software module. No specific limitations are imposed here.

[0031] This embodiment discloses an energy-saving control method for a complete set of equipment for recycling lithium battery resources. Figure 2 This is a schematic flowchart of an energy-saving control method for a complete set of equipment for recycling lithium battery resources disclosed in an embodiment of this application. Figure 2 As shown, the method includes the following steps: S201. Real-time acquisition of the first equipment operating parameters and material characteristic information of the first type of equipment in the complete set of equipment, and acquisition of the second equipment operating parameters and material conveying information of the second type of equipment in the complete set of equipment, wherein the second type of equipment is used to convey materials to the first type of equipment; In a preferred embodiment, the system monitors operating parameters such as motor power, speed, and torque in real time by deploying high-precision current and voltage sensors and speed encoders on key mechanical components (such as motors) of the first type of equipment (e.g., high-power crushers or kneaders). Simultaneously, multi-dimensional material characteristic sensors are integrated into the feed inlet or processing area of ​​the first type of equipment. For example, microwave or near-infrared sensors are used to accurately measure the moisture content of materials (e.g., pulverized material from mixed electrode sheets), vibration analysis sensors or impact force sensors are used to indirectly assess the hardness or toughness of materials, and laser rangefinders or ultrasonic sensors are deployed to monitor material accumulation in real time. For the second type of equipment (e.g., fans, conveyor belts, or other pretreatment equipment), operating parameters are obtained by acquiring the output frequency, power factor, or motor speed of the inverter or driver; material conveying information is measured in real time by precise weighing sensors (e.g., belt scales) or differential pressure flow meters in pipelines to measure material flow rate or concentration. All this sensor data is uploaded to the central control unit in real time via industrial Ethernet (e.g., Profibus, EtherCAT, or Modbus TCP / IP) with millisecond-level response speeds. As an alternative implementation, data acquisition can make greater use of intelligent visual recognition technology and the device's built-in controller interface. For example, the system can be equipped with a high-resolution industrial camera above the feed inlet of the first type of equipment. Combining image processing and deep learning algorithms, it can analyze the particle size distribution, color characteristics, and morphological changes of the material in real time (as proxy indicators of material hardness and mixing uniformity) and calculate its bulk volume. The operating parameters of the first type of equipment can be directly subscribed to and read through the communication port of its built-in PLC (Programmable Logic Controller) or intelligent driver. For the second type of conveying equipment, its material conveying information can be estimated by dynamically analyzing the material image on the conveyor belt surface to estimate the instantaneous coverage or flow rate of the material, and then calculate the material flow rate; the equipment operating parameters are uploaded to the control unit through the equipment control system. In yet another implementation, to achieve cost-effectiveness, data acquisition can rely on a more indirect and simpler sensing method. For example, by installing simple power transmitters and voltage transformers in the power circuit of the first type of equipment, and combining this with a pre-established equipment energy consumption-load empirical model, the current load and material characteristics can be indirectly inferred. The material moisture content can be estimated periodically based on batch data from upstream processing stages or environmental humidity sensors, rather than measured in real time. The material accumulation amount can be determined simply by installing reflective photoelectric sensors or point level switches to determine the presence / absence of material or high / low liquid levels. For the second type of conveying equipment, its operating parameters can be obtained by reading the digital display on its main control panel or by collecting its start / stop status and working mode signals through relays. Material conveying information mainly relies on the production reports of upstream equipment or is roughly estimated based on the equipment's rated processing capacity.Regardless of the configuration, all collected data will be aggregated to the central control unit via wired (e.g., RS485) or wireless (e.g., LoRa, ZigBee) networks, providing timely and effective data support for subsequent energy-saving control strategies.

[0032] Those skilled in the art will understand that the first type of equipment is not limited to crushers or kneaders, but can refer to other core equipment for processing materials, and the second type of equipment is not limited to fans or conveyor belts, but can refer to other auxiliary equipment used for material conveying.

[0033] S202. Based on the material characteristic information, the dynamic operating intensity of the first type of equipment is determined through a preset material characteristic and operating parameter association model. In a preferred embodiment, the system receives and parses material characteristic information from the material detection module, decomposing it into material hardness data, material moisture content data, and material bulk density data. The system then multiplies the material hardness data by a preset first weighting coefficient (e.g., 0.6) to obtain a first strength component; multiplies the material bulk density data by a preset second weighting coefficient (e.g., 0.4) to obtain a second strength component; and sums these two components to obtain a basic strength value reflecting the main load of the material. Next, based on the material moisture content data, the system queries and determines a strength correction coefficient from a preset moisture content correction coefficient mapping relationship (e.g., the higher the moisture content, the larger the correction coefficient). Finally, the basic strength value is multiplied by the strength correction coefficient to obtain the final dynamic operating strength. These weighting coefficients and mapping relationships are set using historical experimental data and expert experience, and are fine-tuned during system operation to optimize performance. As an alternative configuration, the central control unit can determine the dynamic operating strength using a machine learning (ML)-based association model. The ML model (e.g., a trained multilayer perceptron or random forest regressor) is trained offline using a large amount of historical operational data. This data includes characteristics such as hardness, moisture content, and bulk density of different batches of materials, as well as corresponding optimal equipment operating parameters and energy consumption indicators. During real-time operation, the collected raw or parsed material characteristic data (such as hardness, moisture content, and bulk density) are directly used as input to the ML model. After calculation, the model outputs a precise dynamic operating intensity value that optimally balances processing efficiency and energy consumption.

[0034] Those skilled in the art will understand that the association model can be constructed based on empirical formulas or multidimensional lookup tables, or it can be obtained by collecting a large amount of historical data and training it using machine learning algorithms (e.g., neural networks, random forest regression, or support vector regression).

[0035] Optionally, determining the dynamic operating intensity of the first type of equipment based on the material characteristic information and through a preset material characteristic and operating parameter association model includes: parsing the material characteristic information into material hardness data, material moisture content data, and material accumulation data; multiplying the material hardness data by a preset first weighting coefficient to obtain a first intensity component, multiplying the material accumulation data by a preset second weighting coefficient to obtain a second intensity component, and summing the first intensity component and the second intensity component to obtain a base intensity value; querying and determining an intensity correction coefficient in a preset moisture content correction coefficient mapping relationship according to the material moisture content data; and multiplying the base intensity value by the intensity correction coefficient to obtain the dynamic operating intensity.

[0036] Specifically, the collected raw material characteristic information is transformed into quantifiable, standardized data points. For example, if the reflectance spectrum data of the material is obtained through a near-infrared spectral sensor, the analysis unit will use spectral analysis algorithms (such as partial least squares regression or principal component analysis combined with a calibration model) to extract the precise percentage of material moisture content from the raw spectral data. For material hardness, image recognition technology can be used to analyze the texture and particle size distribution of the material surface, or acoustic sensors can be used to analyze the impact sound wave characteristics when the material passes through. Combined with a preset model (such as a hardness prediction model based on image features), it can be quantified into a 0-10 level or a relative hardness index. Material accumulation data can come from weighing sensors on the material conveyor belt (which measure the mass of the material passing through in real time) or lidar / ultrasonic sensors (which measure the height of the material pile and calculate the volume based on the known width and length). These sensor data will be filtered and calibrated to directly output specific mass (tons / hour) or volume (cubic meters) data. These analyzed data (hardness value, percentage of moisture content, accumulation mass / volume) are the basic inputs for the subsequent calculation of the dynamic operating intensity of the equipment in this invention, ensuring that the material characteristics can be accurately and objectively reflected.

[0037] Furthermore, the central control unit or a dedicated strength calculation module calculates the basic strength requirements for equipment operation based on the received material hardness data and material accumulation data. Specifically, the material hardness data (e.g., normalized 0-1) is multiplied by a predetermined first weighting coefficient (W1, e.g., 0.65, reflecting the dominant influence of hardness on equipment wear and energy consumption) to obtain the first strength component (Hardness_Data). Simultaneously, the material accumulation data (e.g., normalized 0-1 or actual tons / hour) is multiplied by a predetermined second weighting coefficient (W2, e.g., 0.35, reflecting the load demand on equipment capacity) to obtain the second strength component (Pile_Volume_Data). These two components (e.g., Hardness_Data×W1 and Pile_Volume_Data×W2) are then linearly summed to form a preliminary basic strength value. This basic strength value represents the minimum operating energy or work intensity required by the equipment to handle the current material hardness and load, without considering other correction factors. These weighting factors are typically calibrated and optimized through experimental data analysis, simulation, or expert system experience in production environments to ensure that they accurately reflect the relative importance of hardness and build-up in specific equipment types and material handling processes.

[0038] Furthermore, the system will look up or calculate the corresponding correction coefficient in a preset moisture content correction coefficient mapping relationship based on the parsed real-time material moisture content data (e.g., 0-30%). This mapping relationship can exist in several forms: the first approach is a lookup table based on discrete intervals, for example: when the moisture content is below 5%, the correction coefficient is 1.05 (the material is too dry, which may generate dust or increase wear, requiring a slight increase in strength); when the moisture content is between 5% and 15%, the correction coefficient is 1.0 (the optimal processing range); when the moisture content is between 15% and 25%, the correction coefficient is 0.95 (the material is slightly wet, which may be more easily broken but also more prone to adhesion, requiring a slight reduction in strength or maintaining the original strength); when the moisture content exceeds 25%, the correction coefficient is 0.85 (the material is too wet, which may clump together or form slurry, requiring a significant reduction in strength to avoid clogging or slippage, and may also reduce the impact on the equipment). The second approach employs a continuous mathematical function, such as a piecewise linear function or a polynomial function, which takes moisture content as input and outputs a continuous correction coefficient value. This function curve can be derived by fitting a large amount of experimental data or constructed based on an empirical model. This mapping relationship fully considers the different impacts of "too dry" or "too wet" materials on equipment operation (such as crushing effect, screening efficiency, wall scaling tendency, and energy consumption), ensuring the accuracy and robustness of the correction.

[0039] Furthermore, the obtained basic strength value is multiplied by the moisture content correction coefficient. The final result is the dynamic operating strength required by the first type of equipment under the current material conditions. This dynamic operating strength is a comprehensive core parameter that can be directly used for equipment control, representing the optimal operating state of the equipment. This strength value can be, for example, specific adjustable parameters such as the equipment's instantaneous rotational speed (RPM), motor power setpoint (KW), torque output percentage, crusher jaw clearance, or kneader blade speed. Through this multiplication operation, the basic strength requirement caused by hardness and bulk density is refined by moisture content correction, forming a real-time strength command that can accurately match the current material characteristics. This enables intelligent adaptive operation of the first type of equipment, effectively avoiding overload or idling, maximizing energy savings, and optimizing processing results.

[0040] S203. Based on the material conveying information, determine the dynamic operating power of the second type of equipment through a preset material conveying and energy consumption correlation model; Specifically, based on real-time acquired material conveying information, the system intelligently determines the dynamic operating power of the second type of equipment (such as conveyor belts, feeders, and elevators) through a preset material conveying and energy consumption correlation model. First, the material conveying information mainly includes material presence / absence information (B) and material conveying load information (L). Material presence / absence information (B) can be detected in real-time by optical sensors (such as infrared beam sensors or photoelectric switches) or miniature weighing sensors installed on the conveying path; for example, B=1 when the sensor detects material and B=0 when there is no material. Material conveying load information (L) is obtained indirectly by measuring the instantaneous flow rate of the material (e.g., a load percentage normalized to [0, 1]) through a dynamic weighing system (such as a belt scale) or by monitoring the electrical parameters of the drive motor (e.g., through current, voltage, or power factor). Subsequently, these collected B and L data are input into the preset material conveying and energy consumption correlation model. This model can be implemented in several ways: The first approach is an empirical model based on piecewise functions. For example, when B=0, the equipment operates at a preset minimum sustaining power P_idle; when B=1, the power P_dyn=P_idle+(P_max-P_idle)×L is calculated linearly or nonlinearly based on the normalized load L. k(Where P_max is the maximum power, k is an empirical coefficient, and P_dyn is the dynamic operating power). The second approach is based on a pre-calibrated lookup table, where the system directly queries the corresponding optimal operating power or speed setpoint based on the combination of B and L. The third approach utilizes a machine learning model, training a predictive model using historical operating data (including B, L, and actual measured energy consumption). This model comprehensively considers various factors to output the optimal power setpoint. Ultimately, the dynamic operating power (P_dyn) output by this model is directly used as an instruction for the second type of equipment drive system (such as a frequency converter) to adjust the equipment's operating speed or motor output power in real time, thereby avoiding unnecessary energy consumption under no-load or light-load conditions and achieving economical and efficient equipment operation.

[0041] Optionally, determining the dynamic operating power of the second type of equipment based on the material conveying information and through a preset material conveying and energy consumption correlation model includes: determining real-time material flow rate data based on the material conveying information; predicting the target power of the second type of equipment based on the real-time material flow rate data and through the material conveying and energy consumption correlation model; retrieving a baseline energy consumption value corresponding to the real-time material flow rate data from a preset energy consumption strategy library; and calculating the dynamic operating power based on the predicted target power and the baseline energy consumption value.

[0042] Specifically, the system first converts the raw material conveying information collected by sensors into accurate real-time material flow rate data. This includes extracting and quantifying the actual amount of material conveyed per unit time from material presence / absence information (B) and material conveying load information (L). For example, if L is the instantaneous material flow rate (e.g., tons / hour) directly measured by a belt scale, it can be directly used as the real-time material flow rate data. If L is a motor load percentage, it needs to be converted in conjunction with the rated maximum conveying flow rate of the second type of equipment; for example, real-time material flow rate = rated maximum flow × L. The goal of this step is to obtain a clear, numerical material flow rate for subsequent energy consumption calculations and power prediction.

[0043] Furthermore, after obtaining real-time material flow rate data, the system uses this data as input into a pre-established material conveying and energy consumption correlation model to predict the theoretical target power that the second type of equipment should consume at that flow rate. This model can be a mathematical model built based on the equipment's physical characteristics and energy conversion efficiency, or a predictive model trained using historical operating data (such as a regression model). For example, the model might consider the frictional resistance of the material, changes in gravitational potential energy, and the mechanical losses of the equipment itself at different flow rates, thus deriving an ideal power requirement at that flow rate. This predicted target power represents the theoretical power value that the equipment should achieve with high efficiency under the current conveying task, but does not yet consider other optimization strategies.

[0044] Furthermore, to further optimize equipment operation, the system queries a pre-defined energy consumption strategy library. This strategy library is typically a structured database or lookup table that stores baseline energy consumption values ​​for the second type of equipment under different material flow rates, based on historical best practices, industry standards, or optimized operating modes. This baseline energy consumption value can be the energy consumption per unit of material (e.g., kWh / ton) or the recommended baseline operating power at that flow rate. The strategy library can be established based on long-term operational data analysis, expert experience, optimization curves provided by equipment manufacturers, or simulation results. Using real-time material flow rate data as an index, the system can quickly retrieve the reference energy consumption performance indicators that best meet energy-saving requirements under the current operating conditions.

[0045] Furthermore, the system combines the predicted target power obtained from the preceding steps with the baseline energy consumption value retrieved from the energy consumption strategy library to calculate the final dynamic operating power. This calculation process aims to balance task requirements with energy-saving goals. For example, the system can compare the predicted target power with the baseline power calculated based on the baseline energy consumption value and select the lower of the two as the dynamic operating power, or perform a weighted average based on certain weights. Alternatively, if the predicted target power is significantly higher than the power indicated by the baseline energy consumption value, the system may adjust the dynamic operating power to be closer to the baseline energy consumption value, thereby forcing the equipment to operate in a more energy-efficient direction. In this way, it ensures that the second type of equipment meets real-time material conveying requirements while approaching or reaching the optimal level of energy efficiency as much as possible.

[0046] Optionally, the dynamic operating power is calculated based on the predicted target power and the baseline energy consumption value, including: calculating the power deviation between the predicted target power and the baseline energy consumption value; multiplying the power deviation by a preset proportional gain parameter to obtain a proportional component; performing an integral operation on the power deviation to obtain a historical integral value; multiplying the historical integral value by a preset integral gain parameter to obtain an integral component; performing a differential operation on the power deviation to obtain a rate of change; multiplying the rate of change by a preset differential gain parameter to obtain a differential component; summing the proportional component, the integral component, and the differential component to obtain a power correction amount; and summing the power correction amount with the predicted target power to obtain the dynamic operating power.

[0047] Specifically, the system first calculates a key control input—power deviation. This power deviation is defined as the difference between the predicted target power and the baseline energy consumption value (or the baseline power converted from it). This deviation reflects the gap between the current theoretical power demand and the optimization target. When the deviation is positive, it indicates that the predicted power is higher than the energy-saving baseline and a negative correction is needed; conversely, the opposite is also true. Subsequently, the system multiplies this instantaneous power deviation by a preset proportional gain parameter (Kp) to obtain the proportional component. The role of the proportional component is to provide an instantaneous correction force proportional to the current deviation. The larger the deviation, the stronger the correction effect of the proportional component, thereby quickly pulling the operating power of the equipment towards the baseline target and achieving a rapid response to the current power deviation.

[0048] Furthermore, to eliminate potential steady-state errors in the system (i.e., power continuing to deviate from the reference value slightly even after proportional adjustment), this step introduces an integral component. The system accumulates and sums historical power deviations within a time window to obtain historical integral values. Then, this historical integral value is multiplied by a preset integral gain parameter (Ki) to obtain the integral component. The core function of the integral component is to "memorize" and amplify past accumulated deviations. If the equipment power is consistently slightly higher (or lower) than the reference value over a long period, even if a single deviation is small, its integral value will continuously increase over time, thereby generating a continuously strengthening corrective force until the steady-state error is completely eliminated, ensuring that the long-term average energy consumption of the equipment strictly approaches the optimal reference.

[0049] Furthermore, to improve system stability and dynamic response characteristics, and to prevent severe power oscillations or overshoot during adjustment, a differential element is introduced in this step. The system calculates the rate of change of the deviation by comparing the current power deviation with the power deviation at the previous sampling time. This rate of change reflects the trend and speed of deviation change. Subsequently, this rate of change is multiplied by a preset differential gain parameter (Kd) to obtain the differential component. The role of the differential component is to "predict" future deviation trends and apply a damping or suppression effect in advance. For example, when the power is rapidly approaching the reference value, the rate of change of the deviation is large. The differential component will generate a reverse correction force, acting as a brake, thereby slowing down the adjustment speed, effectively suppressing overshoot, and allowing the power to smoothly stabilize near the target value.

[0050] Furthermore, in this step, the system algebraically sums the proportional, integral, and differential components calculated in the previous three steps. This sum is the final power correction. It integrates the rapid response to the current deviation (proportional P), the elimination of historical accumulated deviations (integral I), and the prediction and suppression of future trends (differential D). Through this comprehensive calculation, the resulting power correction not only accurately points in the correction direction but also intelligently controls the magnitude and speed of the correction, achieving a fast, stable, and error-free closed-loop feedback regulation effect.

[0051] Furthermore, the system sums the calculated power correction with the initial predicted target power to obtain the final dynamic operating power output to the second type of equipment actuators (such as frequency converters). This final power value satisfies both the theoretical power requirement based on the real-time material flow rate (reflected by the predicted target power) and incorporates an optimization strategy that converges to energy-saving benchmarks through PID correction. Therefore, this dynamic operating power is a highly optimized equipment operation command that achieves a dynamic balance between meeting production tasks and realizing extreme energy savings, ensuring that the entire system operates both efficiently and economically.

[0052] S204. Obtain the target operating parameters from the preset intensity parameter correspondence relationship according to the dynamic operating intensity, and calculate and generate the first control command based on the parameter difference between the target operating parameters and the first device operating parameters; In a specific embodiment, to execute step S204, the system first uses the previously calculated dynamic operating intensity as an index to query and obtain the target operating parameter from a preset intensity parameter correspondence. This correspondence can be a direct lookup table based on experimental data calibration, which maps different levels of operating intensity to a key target operating parameter, such as a target rotational speed. As a more refined preferred option, this correspondence can also be a multi-dimensional parameter model, capable of simultaneously resolving a single operating intensity into a set of mutually synergistic target operating parameters, such as simultaneously obtaining the target crushing rotational speed and the target kneading torque, to achieve more comprehensive control over the equipment's operating conditions. After obtaining the target parameter, the system immediately parses the corresponding dimension's current actual parameter from the first real-time collected equipment operating parameters and calculates the parameter difference between the target value and the actual value. There are several ways to generate the first control command based on this difference: one is to use a PID controller, take the difference as input, and generate a precise adjustment quantity through proportional, integral, and derivative operations; another is to protect the equipment and achieve a smooth transition, the system performs gradient processing on the parameter difference based on the preset maximum parameter change rate, decomposes it into a smooth adjustment command sequence composed of multiple small steps, and finally encapsulates this PID adjustment quantity or step instruction sequence into a first control command that conforms to the communication protocol and sends it to the actuator of the first type of equipment.

[0053] Optionally, obtaining the target operating parameters from a preset intensity parameter correspondence based on the dynamic operating intensity, and calculating and generating the first control command based on the parameter difference between the target operating parameters and the first equipment operating parameters includes: obtaining the target crushing speed and target kneading torque from the intensity parameter correspondence based on the dynamic operating intensity; parsing the first equipment operating parameters to obtain the current crushing speed and current kneading torque; calculating the speed difference between the target crushing speed and the current crushing speed, and the torque difference between the target kneading torque and the current kneading torque; performing gradient processing on the speed difference and the torque difference according to a preset maximum parameter change rate to generate a step-by-step adjustment command sequence; and encapsulating the step-by-step adjustment command sequence to obtain the first control command.

[0054] Specifically, the system first uses the received dynamic operating intensity as the basis for querying. The intensity parameter correspondence here is a pre-defined, multi-dimensional mapping data structure stored in the system, such as a two-dimensional lookup table or a parameterized function set. This correspondence precisely defines the optimal collaborative working point that the upstream crusher (i.e., the first device) should achieve for each operating intensity range of the downstream equipment. This working point is not determined by a single parameter, but rather by a set of interrelated target parameters. In this embodiment, this set of parameters specifically refers to the target crushing speed and the target kneading torque. For example, when the dynamic operating intensity is 35% (indicating that the downstream conveyor belt is under low to medium load), the system queries this correspondence and may obtain the following combination of target parameters: a target crushing speed of 800 RPM and a target kneading torque of 1200 Nm. The technical purpose of this design is to achieve dual, refined control of the upstream equipment's production efficiency and product quality by simultaneously setting the speed (affecting throughput) and torque (affecting crushing / kneading effect and energy consumption), in order to more comprehensively match downstream needs.

[0055] Furthermore, to form an effective closed-loop control system, after determining the control objective, the system needs to acquire the current actual operating status of the first device in real time. The system receives data packets or status words of the first device's operating parameters from its controller (such as a PLC, frequency converter, or dedicated driver) via a communication interface (such as industrial Ethernet, Modbus, or CAN bus). Next, the system parses this raw data, extracting the specific values ​​corresponding to the control objective. In this embodiment, the parsing operation specifically refers to identifying and separating the signal representing the motor speed (which may come from the frequency feedback of the motor encoder or frequency converter) and the signal representing the motor output torque (which may come from a torque sensor, or more commonly, estimated by the motor model inside the frequency converter based on parameters such as current and voltage). Through parsing, the system obtains the current crushing speed and the current kneading torque, which form the basis for subsequent difference calculations.

[0056] Furthermore, the system performs error assessments on both control dimensions separately. The system executes two independent subtraction operations: first, subtracting the current crushing speed from the target crushing speed to obtain the speed difference (Δω); second, subtracting the current crushing torque from the target crushing torque to obtain the torque difference (ΔT). These two differences—speed difference and torque difference—are the core signals for drive control adjustment. They not only contain the direction of adjustment (positive values ​​indicate an increase, negative values ​​indicate a decrease) but also reflect the magnitude of deviation from the target. This separate calculation method ensures that the control of the two physical quantities, speed and torque, is decoupled and parallel, providing clear and independent inputs for the next step of multivariate coordinated adjustment.

[0057] Furthermore, the system is not a trial. Figure 1 Instead of eliminating all speed and torque differences at once, a gradient processing strategy is employed. The system has preset maximum parameter change rates for the crusher's characteristics; for example, the maximum speed change rate might be set to 100 RPM / s, and the maximum torque change rate might be set to 200 Nm / s. The gradient processing process is as follows: the system compares the total difference (Δω and ΔT) calculated in the previous step with the maximum change rate. If the total difference exceeds the maximum allowable change within a control cycle (e.g., 100 milliseconds), the system decomposes this total difference into multiple smaller step adjustments that conform to the change rate limit. In this way, the system generates a step-by-step adjustment command sequence consisting of multiple consecutive, small-amplitude adjustment commands for both speed and torque dimensions. For example, for a speed difference of 500 RPM, the system will decompose it into a sequence of five consecutive +100 RPM adjustment commands. This aims to smooth a large step change into a gradual, ramp-like change, thereby achieving flexible adjustment of the equipment's operating state.

[0058] Furthermore, as the final step in generating control commands, the system encapsulates the step-by-step adjustment command sequences generated in the previous step for speed and torque. Encapsulation involves integrating these discrete, serialized adjustment commands into one or more messages or data structures conforming to the communication protocol format with the first device controller, forming the final first control command. This encapsulated command may be a complex data block containing information such as the starting point of the command sequence, the target value for each step, the time interval between steps, and the final target value. When the controller of the first device receives this encapsulated first control command, it automatically parses and executes the command sequence step by step, driving the motor and other actuators to complete smooth speed and torque adjustments. In this way, this application not only issues a simple target command but also provides a complete adjustment path plan that considers process stability, thereby ensuring that the entire cooperative control process is both efficient and reliable.

[0059] S205. Based on the power difference between the dynamic operating power and the operating parameters of the second device, calculate and generate a second control command; In an optional embodiment, step S205 performs a closed-loop adaptive adjustment on the second device to dynamically match its energy consumption with the actual load. This process first calculates the power difference between the dynamic operating power and the actual operating power of the second device. The calculation of this power difference and the subsequent generation of the second control command can include various non-limiting implementation methods. The first implementation method is proportional control, where the system directly multiplies the power difference by a preset proportional gain coefficient to obtain an adjustment amount that constitutes the core of the second control command; this scheme has a fast response, is simple to implement, and can quickly respond to power deviations. The second implementation method, as a more preferred option, is to use a proportional-integral-derivative (PID) controller. It not only considers the current power difference (proportional element), but also accumulates historical differences to eliminate steady-state errors (integral element), and performs predictive adjustment based on the rate of change of the difference to suppress system oscillations (derivative element). The controller weighted sums of these three calculation results to generate a smoother and more accurate adjustment command, significantly improving system stability and energy efficiency. The third implementation method employs fuzzy logic control. The system internally pre-defines a fuzzy rule base mapping expert experience. Inputs such as power difference and the rate of change of power difference are fuzzified, for example, divided into multiple fuzzy subsets such as negative large, negative small, zero, positive small, and positive large. Then, a fuzzy inference engine matches the rules and performs defuzzification to calculate a specific adjustment command. This approach is particularly suitable for complex systems with strong nonlinearity and time-varying characteristics, enabling flexible and robust intelligent control. Regardless of the approach used, the final generated second control command is sent to the actuator of the second device, such as a frequency converter, to dynamically optimize its actual operating state.

[0060] S206. Based on the operating parameters of the second equipment and the material conveying information, determine the linkage start command and linkage stop command for controlling the first type of equipment, and generate the system collaborative scheduling command based on the linkage start command and the linkage stop command; In a specific embodiment of this application, step S206 aims to establish intelligent start-stop linkage logic between the first type of equipment and the second type of equipment, thereby realizing unmanned or minimally manned automatic scheduling of the entire production line. This step determines the timing of linkage start-up and shutdown based on the operating parameters of the second equipment and material conveying information, and generates the final system collaborative scheduling instruction. Its specific implementation methods may include, but are not limited to, the following schemes. The first implementation scheme is direct linkage control based on thresholds. The system continuously monitors the operating status of downstream equipment (used as operating parameters of the second equipment) and the material level of downstream silos (used as material conveying information). When the system detects that the downstream equipment has started and its operating speed exceeds a preset minimum operating speed, while the material level of the downstream silo is lower than a preset start-up threshold, the system determines to generate a linkage start-up instruction. Conversely, when it detects that the downstream equipment has stopped, or that the material level of the downstream silo is higher than a preset stop-up threshold to prevent overflow, the system determines to generate a linkage stop-up instruction. Finally, these determined instructions are encapsulated into a system collaborative scheduling instruction and sent to the first type of equipment. The second implementation scheme, as a more optimized solution, introduces predictive timing control logic. In this scheme, the system not only monitors the current material level but also calculates the material descent rate based on historical data. By comparing the material descent rate with the operating speed of downstream equipment, the system can predict the lead time when the downstream silo will run out of material. When this lead time is less than or equal to the time required for the first type of equipment to go from startup to stable output plus a safety margin, the system generates a linkage start command in advance to achieve seamless material connection. Similarly, during shutdown, the system calculates the delay time required to clear the material remaining on the conveyor belt and, after the downstream equipment stops, allows the first type of equipment to continue running for that delay time before generating a linkage stop command to ensure the production line is emptied. The third implementation scheme is based on production batch task management and control. In this scheme, material conveying information is expanded to include production work orders containing the total production task or batch number. The generation of system collaborative scheduling commands is bound to a complete production task. The linkage start command is triggered after receiving a new production work order and confirming that the second type of equipment is ready. The linkage stop command is determined and generated when the system determines that the current batch task has been completed by accumulating the material conveying volume fed back by the metering equipment. This approach tightly integrates equipment start-up and shutdown with the overall production plan, enabling a higher level of automated scheduling and production management.

[0061] Optionally, based on the operating parameters of the second equipment and the material conveying information, determining the linkage start command and linkage stop command for controlling the first type of equipment, and generating the system collaborative scheduling command based on the linkage start command and the linkage stop command includes: determining whether the second type of equipment has been started and started conveying materials based on the operating parameters of the second equipment and the material conveying information; if the second type of equipment has been started and started conveying materials, generating the linkage start command to start the first type of equipment according to a preset material conveying time delay; monitoring the material conveying status of the second type of equipment, and when the material conveying status is stopped, generating the linkage stop command to stop the first type of equipment, wherein the linkage stop command is configured with a stop delay time to ensure that the first type of equipment has processed all received materials; and encapsulating the linkage start command and the linkage stop command to form the system collaborative scheduling command.

[0062] In one specific embodiment, the process of determining the start-up timing of the first type of equipment begins with accurately judging whether the second type of equipment has entered an effective material conveying state. This judgment is not simply a matter of detecting whether the equipment is powered on, but rather a composite judgment based on the operating parameters of the second equipment and material conveying information, to avoid triggering the start-up of upstream equipment when the equipment is unloaded or operating abnormally. A basic implementation involves the controller simultaneously monitoring whether the drive motor of the second equipment is running (as an operating parameter of the second equipment) and whether photoelectric or ultrasonic sensors installed on the second equipment (such as a conveyor belt) have detected material (as material conveying information); only when both are true is it determined that the second type of equipment has started conveying material. A more precise implementation involves the controller collecting the real-time operating current or power of the drive motor of the second equipment as its operating parameter and comparing it with a preset no-load current or power threshold; simultaneously, it obtains the real-time material flow rate as material conveying information through a weighing sensor or dynamic metering scale installed under the conveyor belt. Only when the operating current or power is significantly higher than the no-load threshold and the real-time material flow rate is greater than zero does the system confirm that the second type of equipment has entered an effective material conveying state. In other embodiments, the determination can also be combined with the status code fed back from the PLC or controller of the second type of equipment itself, which can directly indicate whether the equipment is in material conveying mode, thereby achieving a more direct and reliable determination.

[0063] Furthermore, after confirming that the second type of equipment has started and begun conveying materials, the system does not immediately start the first type of equipment. Instead, a crucial preset material transfer time delay is introduced to achieve flexible buffering and precise connection between upstream and downstream equipment. This delay aims to ensure that when the first type of equipment begins processing or producing materials, the second type of equipment has sufficient space or capacity to receive these materials, thus preventing material accumulation or production interruption. Several specific schemes exist for setting this material transfer time delay. The first scheme is a fixed time delay, which is pre-calculated and fixed in the system based on the physical distance between the equipment and the material conveying rate of the second type of equipment at its rated speed. This scheme is simple to implement. The second scheme, as a preferred adaptive scheme, involves a dynamically variable delay. The system dynamically calculates an optimal start-up delay based on the real-time operating speed obtained from the operating parameters of the second equipment, combined with the real-time material level information of the downstream material container (such as a silo). For example, when the downstream material level is low and the second equipment's operating speed is high, the system can appropriately shorten the delay time to quickly replenish materials; conversely, it can extend the delay time. The third approach is based on event-triggered delay. After the system determines that the second type of equipment has started, it begins to monitor a specific sensor upstream of it. Only when the sensor is first triggered by the material does it start timing for a very short delay, and then generate a linkage start command. This method can most accurately guarantee the continuity of the material flow.

[0064] Further, the system enters the continuous monitoring and intelligent shutdown management phase. The core of this phase lies in generating a shutdown command to stop the first type of equipment at the appropriate time and in the appropriate manner based on the material feeding status of the second type of equipment. The system determines the material feeding status of the second type of equipment by continuously monitoring the aforementioned material feeding information used to determine startup (such as sensor signals, weighing data, or status codes). When the system detects that the material feeding status has changed from feeding to stopping, for example, if the photoelectric sensor remains unobstructed for a period of time, or the weighing sensor reading remains zero, the shutdown logic is triggered. However, the shutdown command is not executed immediately; instead, a critical shutdown delay time is configured. This delay time is set to ensure that after the second type of equipment stops feeding, the material remaining inside the first type of equipment and in the connecting channel between the first and second type of equipment can be completely processed or transferred, thereby achieving the emptying of the production line and zero material waste. The shutdown delay time can also be set in several ways. For example, it can be set to a fixed value based on experience estimation, or better yet, the system can accurately calculate the shortest time required to process the remaining material based on the rated processing capacity of the first type of equipment and its internal material volume, and use this as the shutdown delay time to achieve the best balance between efficiency and emptying effect.

[0065] Furthermore, the system unifies the linked start and stop commands generated in the aforementioned process into a structured system collaborative scheduling command. This encapsulation process is not merely a simple combination of two independent commands, but more importantly, it transforms them into a standardized data packet or control message containing a clearly defined execution object, action type, trigger condition, and timing parameters. For example, a system collaborative scheduling command can be in JSON format, explicitly defining the target device ID as the address of the first type of device, the command type as linkage control, and containing two sub-commands: a start command triggered by the second type of device running with material, and accompanied by a calculated start delay time parameter; and a stop command triggered by the second type of device stopping material feeding, and accompanied by a configured stop delay time parameter. This encapsulated system collaborative scheduling command is then sent to the central control system or directly to the controller (such as a PLC or dedicated controller) of the first type of device via industrial Ethernet, fieldbus, or wireless communication network, where it is parsed and executed to perform the corresponding start and stop operations, thereby completing the entire closed-loop automated and intelligent collaborative scheduling process.

[0066] S207. Based on the system collaborative scheduling instruction, the first control instruction and the second control instruction are processed collaboratively to generate a final execution instruction sequence, and the final execution instruction sequence is sent to the complete set of equipment.

[0067] In another optional embodiment of this application, the collaborative processing specifically refers to: the system collaborative scheduling instruction, as the highest priority gating signal, determines the start and stop of the first and second types of devices; when the device is in operation, the control system allows the issuance and execution of the first and second control instructions for real-time parameter optimization. This collaborative processing process takes the system collaborative scheduling instruction as the highest behavior criterion, arbitrates, merges, and sequences the dynamically generated first and second control instructions to generate a final directly issueable sequence of execution instructions. Specific implementation methods include, but are not limited to, the following: The first implementation scheme is based on priority and state machine gating logic, which treats the system collaborative scheduling instruction as the highest priority enable signal. The central control system maintains a running state machine for the first type of device, including states such as standby, startup, running, and shutdown. When the linkage start instruction in the system collaborative scheduling instruction arrives, the state machine switches to the running state, at which point the central control system begins to transmit continuously updated first control instructions (such as power setpoints) to the first type of device. Conversely, when the coordinated shutdown command arrives, the state machine switches to the shutdown state, the system immediately issues a shutdown command and blocks subsequent first control commands. This scheme has clear logic and high reliability. The second implementation scheme, a more refined approach, employs command fusion and ramp control. In this scheme, the collaborative processing unit does not simply pass through or block commands. Instead, upon receiving the coordinated start command, it uses the target operating parameters (such as target speed) in the first control command as the endpoint to generate a smooth ramp start command sequence, allowing the equipment power and speed to gradually reach the target values, avoiding impact on the power grid and mechanical structure. Similarly, a ramp shutdown sequence is generated when executing the coordinated shutdown command. During this process, the collaborative processing unit can even temporarily fine-tune the second control command, such as slightly increasing the downstream belt speed before startup to better handle materials, achieving more flexible dynamic coordination between equipment. The third implementation scheme introduces predictive optimization based on a digital twin model. The system pre-establishes a complete simulation model of the equipment, including both the first and second types of equipment. Upon receiving the system's coordinated scheduling command, the coordinated processing unit first inputs the current first and second control commands into the digital twin model for advanced simulation, predicting the system's material flow, energy consumption curves, and equipment load over a future period. If the simulation identifies potential bottlenecks or conflicts, such as exceeding the instantaneous total power limit during startup, the processing unit proactively optimizes and adjusts the command sequence, for example, by staggering startup times or temporarily limiting the peak power of a particular device, ultimately generating a final execution command sequence that has undergone global optimization and conflict verification. This sequence is then encoded into messages conforming to the target device's communication protocol and accurately distributed to the PLCs or frequency converters of each unit in the complete equipment assembly via buses such as Industrial Ethernet, Profinet, or Modbus, thereby completing the entire closed-loop control.

[0068] Optionally, the method further includes: before issuing the first control command, determining whether the first type of device is in a standby state; if the first type of device is in a standby state, stopping the issuance of the first control command, and generating a startup command sequence based on a preset hierarchical soft-start strategy, the startup command sequence containing multiple power step commands; issuing the power step commands sequentially, and issuing the first control command when the operating parameters of the first device reach a preset operating threshold.

[0069] In a particularly optimized embodiment, to protect the first type of equipment and reduce the impact of its startup process on the power grid, this application adds a pre-processing startup status judgment logic before issuing the conventional first control command. Specifically, the control system first needs to determine whether the first type of equipment is in a standby startup state. This state is not a simple shutdown or power-on state, but a specific logical state that is clear, ready but not yet in operation. One specific judgment method is that the central control system maintains a status flag for the equipment. When the upper-level scheduling logic issues a startup intention but has not yet issued any power command, this flag is set to standby startup. As another more reliable solution, the control system can actively query the controller of the first type of equipment itself, such as a programmable logic controller (PLC) or frequency converter, through a fieldbus or dedicated communication link to read its internal status register information. Only when the status combination fed back by the equipment controller is power-on, fault-free, and the operation enable signal is off, does the system comprehensively determine that the equipment is indeed in a standby startup state, thereby providing an accurate triggering premise for the subsequent soft-start procedure.

[0070] Furthermore, once the system confirms that the first type of device is in a standby state, it activates a preset control process. This process first suspends or suspends the upcoming regular first control command, and then generates a dedicated sequence of startup commands based on a preset tiered soft-start strategy. The action of suspending regular commands can be achieved by setting a logic gate or switch on the control command distribution channel. When a standby state is detected, this logic gate temporarily buffers or discards the regular commands, while simultaneously switching the command channel to the output of the soft-start command generation module. Next, this module generates a sequence of startup commands containing multiple power step commands according to the preset tiered soft-start strategy. The implementation of this strategy is flexible and varied. For example, one approach is to embed a static power step array in the system, such as commands containing multiple power percentages like 10%, 25%, 45%, and 70%. Another more adaptive preferred option is to define the strategy as a dynamic calculation mode, for example, specifying that it starts with an initial power of X%, increases by Y% at each level, and the final target power is dynamically extracted from the suspended regular first control command. Based on this, the system calculates the specific value of each power step in real time and combines them into a sequence.

[0071] Furthermore, after the startup command sequence is generated, the system begins to execute the sequence in an orderly manner, sequentially issuing the power step commands contained therein to the actuators of the first type of equipment, such as frequency converters or soft starters. The command issuance process is not a simple continuous transmission, but follows strict timing control. In one implementation, the system sets a fixed duration for each power step; for example, after stabilizing at the 10% power step for 5 seconds, it switches to the 25% power step and maintains it for a similar period, thus progressing step by step. As a more sophisticated closed-loop control scheme, after issuing a new power step command, the system monitors one or more key operating parameters of the first type of equipment in real time, such as the actual operating current of the drive motor or the spindle speed. The system continuously determines whether these parameters have stabilized at the current power step, i.e., their fluctuation range is less than a preset stability threshold. Only when the parameters are stable will the system continue to issue the next higher-level power step command in the sequence. This adaptive, step-by-step confirmation method can better match the actual load characteristics of the equipment, ensuring a smooth and safe startup at each step.

[0072] Furthermore, the endpoint of this tiered soft-start process is precisely defined to achieve a seamless transition to the conventional production control mode. Throughout the process of issuing power step-by-step commands, the system continuously compares the real-time monitored first equipment operating parameters, such as the equipment's actual output power, spindle speed, or machine vibration frequency, with a preset operating threshold. This operating threshold signifies that the equipment has successfully passed the most energy-intensive and time-consuming startup phase and entered a stable operating range capable of handling production tasks. Its specific value can be preset according to the equipment's design specifications and process requirements; for example, it can be set to 85% of the equipment's rated speed. When the monitored operating parameters first reach or exceed this preset operating threshold, the system determines that the soft-start process is complete. At this moment, the system immediately stops issuing any remaining commands in the startup command sequence, simultaneously releases the suspension or abort of the conventional first control command, and begins to unimpededly issue the dynamically calculated first control command to the first type of equipment, allowing the equipment to smoothly transition from the last step of the soft start to the normal closed-loop control phase responding to production needs.

[0073] This embodiment also discloses an electronic device, as shown in the reference. Figure 3The electronic device may include: at least one processor 301, at least one communication bus 302, a user interface 303, a network interface 304, and at least one memory 305. The communication bus 302 is used to enable communication between these components. The user interface 303 may include a display screen or a camera; optionally, the user interface 303 may also include a standard wired interface or a wireless interface. The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0074] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0075] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. Figure 3 As shown, the memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for an energy-saving control method of a complete set of equipment for recycling lithium battery resources.

[0076] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some service interfaces; indirect couplings or communication connections between apparatuses or units may be electrical or other forms.

[0077] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the disclosure in this specification. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. An energy-saving control method for a complete set of equipment for recycling lithium battery resources, characterized in that, Applied to a server, the method includes: The system collects in real time the operating parameters and material characteristics of the first type of equipment in the complete set of equipment, and collects the operating parameters and material conveying information of the second type of equipment in the complete set of equipment. The second type of equipment is used to convey materials to the first type of equipment. Based on the material characteristic information, the dynamic operating intensity of the first type of equipment is determined through a preset material characteristic and operating parameter association model. Based on the material conveying information, the dynamic operating power of the second type of equipment is determined through a preset material conveying and energy consumption correlation model; The target operating parameters are obtained by querying the preset intensity parameter correspondence based on the dynamic operating intensity, and a first control command is generated based on the parameter difference between the target operating parameters and the first device operating parameters. Based on the power difference between the dynamic operating power and the operating parameters of the second device, a second control command is calculated and generated. Based on the operating parameters of the second equipment and the material conveying information, a linkage start command and a linkage stop command for controlling the first type of equipment are determined, and a system coordinated scheduling command is generated based on the linkage start command and the linkage stop command. Based on the system coordinated scheduling instruction, the first control instruction and the second control instruction are processed collaboratively to generate a final execution instruction sequence, and the final execution instruction sequence is sent to the complete set of equipment.

2. The method according to claim 1, characterized in that, The step of determining the dynamic operating intensity of the first type of equipment based on the material characteristic information and through a preset material characteristic and operating parameter correlation model includes: The material characteristic information is parsed into material hardness data, material moisture content data, and material bulk density data; The material hardness data is multiplied by a preset first weighting coefficient to obtain a first strength component, the material accumulation data is multiplied by a preset second weighting coefficient to obtain a second strength component, and the first strength component and the second strength component are summed to obtain a basic strength value. Based on the material moisture content data, the strength correction coefficient is queried and determined in the preset moisture content correction coefficient mapping relationship; The dynamic operating strength is obtained by multiplying the basic strength value by the strength correction coefficient.

3. The method according to claim 1, characterized in that, The step of determining the dynamic operating power of the second type of equipment based on the material conveying information and through a preset material conveying and energy consumption correlation model includes: Based on the material conveying information, determine the real-time material flow rate data; Based on the real-time material flow rate data, the predicted target power of the second type of equipment is predicted through the material conveying and energy consumption correlation model. The baseline energy consumption value corresponding to the real-time material flow rate data is retrieved from the preset energy consumption strategy library; The dynamic operating power is calculated based on the predicted target power and the baseline energy consumption value.

4. The method according to claim 3, characterized in that, The step of calculating the dynamic operating power based on the predicted target power and the baseline energy consumption value includes: Calculate the power deviation between the predicted target power and the baseline energy consumption value, and multiply the power deviation by a preset proportional gain parameter to obtain the proportional component; The power deviation is integrated to obtain a historical integral value, and the historical integral value is multiplied by a preset integral gain parameter to obtain the integral component; The power deviation is differentiated to obtain the rate of change, and the rate of change is multiplied by a preset differential gain parameter to obtain the differential component; The power correction amount is obtained by summing the proportional component, the integral component, and the differential component. The dynamic operating power is obtained by summing the power correction amount with the predicted target power.

5. The method according to claim 1, characterized in that, The step of obtaining the target operating parameters from a preset intensity parameter correspondence based on the dynamic operating intensity, and calculating and generating a first control command based on the parameter difference between the target operating parameters and the first device operating parameters includes: From the strength parameter correspondence, the target crushing speed and target kneading torque are obtained by querying the dynamic operating intensity. Analyze the operating parameters of the first device to obtain the current crushing speed and current kneading torque; Calculate the speed difference between the target crushing speed and the current crushing speed, and the torque difference between the target kneading torque and the current kneading torque, respectively. Based on the preset maximum parameter change rate, the speed difference and the torque difference are subjected to gradient processing to generate a step-by-step adjustment command sequence; The step-by-step adjustment instruction sequence is encapsulated to obtain the first control instruction.

6. The method according to claim 1, characterized in that, The step of determining the linkage start-up command and linkage stop command for controlling the first type of equipment based on the operating parameters of the second equipment and the material conveying information, and generating the system coordinated scheduling command based on the linkage start-up command and linkage stop command includes: Based on the operating parameters of the second equipment and the material conveying information, determine whether the second type of equipment has been started and has begun conveying materials; If the second type of equipment has been started and has begun conveying materials, then the linkage start command to start the first type of equipment is generated according to the preset material conveying time delay. Monitor the material conveying status of the second type of equipment. When the material conveying status is stopped, generate the linkage shutdown command to stop the first type of equipment. The linkage shutdown command is configured with a shutdown delay time to ensure that the first type of equipment has processed all the received materials. The coordinated start command and the coordinated stop command are encapsulated to form the system coordinated scheduling command.

7. The method according to claim 1, characterized in that, The method further includes: Before issuing the first control command, determine whether the first type of device is in a ready-to-start state; If the first type of device is in a standby state, the first control command is stopped from being issued, and a startup command sequence is generated based on a preset hierarchical soft-start strategy. The startup command sequence contains multiple power step commands. The power step instructions are issued sequentially. When the operating parameters of the first device reach the preset operating threshold, the first control instruction is issued.

8. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions. The user interface and the network interface are both used to communicate with other devices. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on an electronic device, it causes the electronic device to perform the method as described in any one of claims 1-7.

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