Production management method and system based on cloud edge collaborative energy large model

Abstract

The invention discloses a production management method and system based on a cloud edge collaborative energy large model, and relates to the technical field of computers. The method comprises the following steps: acquiring use data and historical data of any production machine in a current production field; according to the actual production efficiency of the production machine, the first operation power of the production machine is converted into second operation power; according to the first operation power, the second operation power and historical data, the number of stages needing to be divided between the first operation power and the second operation power is determined; determining third operation power corresponding to each stage according to the stage number, the first operation power and the second operation power; and performing power adjustment on all production machines in the production field according to the third operation power corresponding to each stage through a preset cloud edge collaborative energy large model. By implementing the technical scheme provided by the invention, the effect of reducing the resource loss of a production field is achieved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and specifically to a production management method and system based on a cloud-edge collaborative energy model. Background Art

[0002] In industrial production, the degree to which a machine's operating power matches its actual production efficiency directly impacts production quality and energy efficiency. In actual production, the actual output efficiency of a machine at the same power level fluctuates over time due to factors such as fluctuations in raw material quality, machine wear, and ambient temperature and humidity. This efficiency fluctuation typically manifests as deviations in product quality parameters and fluctuations in output per unit time. This makes it difficult for a machine to consistently meet production requirements at a fixed power level, necessitating adjustments to the operating power level.

[0003] Currently, production machine power adjustment primarily involves independent adjustments on a single machine basis. This method analyzes the historical production data of a single machine to determine a standard efficiency range, setting its upper and lower limits as monitoring thresholds for the control system. The system continuously collects real-time production data from the machine and calculates the current production efficiency. If the calculated efficiency value exceeds the preset range, the control system selects the corresponding power value from a preconfigured power database. This database stores standard power values for different product types and production requirements. The control system then adjusts the machine's power directly to the selected value.

[0004] However, this adjustment method will cause the power adjustment speed of the production machine to be too fast. During the adjustment process, the speed of the mechanical system of the machine will often change rapidly, causing the input speed to not match the output speed of the previous machine, resulting in severe mechanical vibration and friction between rotating bearings, gears and other components, causing a large amount of input electrical energy to be converted into heat energy, thereby causing a large amount of resource loss in the power switching process of the production machine, and further causing a large amount of resource loss at the production site. Summary of the Invention

[0005] This application provides a production management method and system based on a cloud-edge collaborative energy model, which can reduce resource loss at the production site.

[0006] In a first aspect of the present application, a production management method based on a cloud-edge collaborative energy model is provided, specifically comprising: Obtaining usage data and historical data of any production machine in the current production site, wherein the usage data includes a first operating power and actual production efficiency, and the historical data includes historical power data, historical temperature data, and historical vibration data; converting the first operating power of the production machine into a second operating power according to the actual production efficiency of the production machine; determining, according to the first operating power, the second operating power, and the historical data, the number of stages required to be divided between the first operating power and the second operating power; determining a third operating power corresponding to each stage according to the number of stages, the first operating power, and the second operating power; Through the preset cloud-edge collaborative energy model, the power of all production machines at the production site is adjusted according to the third operating power corresponding to each stage.

[0007] By adopting the above technical solution, the first operating power and actual production efficiency of each production machine are obtained respectively, and its actual production efficiency is analyzed to determine the second operating power that needs to be converted. The appropriate number of adjustment stages is calculated based on historical data, and then the third operating power corresponding to each stage is determined. Then, all production machines at the production site are adjusted in stages according to the corresponding third operating power through the cloud-edge collaborative energy model. In this way, the operating power of each production machine at the production site is calculated for each stage, and the cloud-edge collaborative energy model is used to make collaborative adjustments according to the calculated operating power, thereby reducing resource loss at the production site.

[0008] Optionally, converting the first operating power of the production machine into the second operating power according to the actual production efficiency of the production machine includes: Obtaining a target production efficiency of the production machine, and calculating an efficiency ratio between the actual production efficiency of the production machine and the target production efficiency; Determining a power adjustment coefficient corresponding to the production machine according to a preset mapping relationship between the efficiency ratio and the power adjustment coefficient; The first operating power is multiplied by a corresponding power adjustment coefficient to obtain the second operating power.

[0009] By adopting this technical solution, a specific efficiency ratio is first calculated based on the actual efficiency and the target efficiency. Then, a corresponding power adjustment coefficient is derived from the efficiency ratio according to a preset mapping relationship. Finally, the target power value is determined by multiplying the current power by the adjustment coefficient. The efficiency ratio reflects the degree of deviation in the production status, and the mapping relationship converts the deviation into a specific adjustment range. Ultimately, a reasonable target power is obtained, allowing for more precise control of the intensity of power adjustments and effectively avoiding the problem of over- or under-adjustment.

[0010] Optionally, the determining, according to the first operating power, the second operating power, and the historical data, the number of stages to be divided between the first operating power and the second operating power includes: Obtaining a power adjustment range of the production machine according to the first operating power and the second operating power of the production machine; Calculating a single safety adjustment threshold of the production machine based on the historical data; Based on the power adjustment amplitude and the corresponding single safety adjustment threshold, the number of stages that need to be divided between the first operating power and the second operating power is obtained.

[0011] By adopting this technical solution, the total power adjustment range required is first calculated. The safety threshold for each adjustment is then determined based on historical data. Finally, the number of required adjustment stages is determined based on the relationship between the total adjustment range and the safety threshold. This approach uses the power adjustment range to reflect the overall adjustment amount, and combines historical data to determine the safety threshold to limit the range of a single adjustment. Ultimately, the optimal number of stages is determined based on the combined relationship between the two. This allows for a more scientific division of power adjustment stages, effectively ensuring the safety and controllability of each adjustment, and minimizing resource loss by ensuring the number of stages.

[0012] Optionally, determining the third operating power corresponding to each stage according to the number of stages, the first operating power, and the second operating power includes: detecting operating data of the production machine in a first stage, determining a first adjustment coefficient based on the operating data, subtracting the second operating power from the first operating power and dividing the result by the number of stages to obtain a first adjustment reference value, multiplying the first adjustment reference value by the first adjustment coefficient to obtain a first product, and adding the first operating power to the first product to obtain a third operating power for the first stage, where the first stage is the first of the multiple stages; The next stage is taken as the first stage, and the operating data of the production machine in the first stage is detected, a first adjustment coefficient is determined according to the operating data, the second operating power is subtracted from the first operating power and the resultant is divided by the number of stages to obtain a first adjustment reference value, the first adjustment reference value is multiplied by the first adjustment coefficient to obtain a first product, and the first operating power is added to the first product to obtain the third operating power of the first stage, until the third operating power of the last stage is calculated and the third operating power corresponding to each stage is obtained.

[0013] By adopting the above technical solution, the actual operating data of the current stage is detected to obtain the first adjustment coefficient. Then, the first adjustment reference value is calculated and multiplied by the first adjustment coefficient to obtain the actual adjustment amount. Finally, the target power of each stage is gradually determined through an iterative method. This method uses real-time operating data to reflect the current adjustment effect, and then combines the adjustment reference value and adjustment coefficient to calculate the optimal adjustment range. Ultimately, through cyclic iteration, precise control of the entire process is achieved. It can more flexibly respond to the actual adjustment needs of each stage, effectively improving the accuracy and adaptability of power adjustment.

[0014] Optionally, the operating data of the production machines includes temperature data, vibration data, pressure data, and output data. The detecting the operating data of each of the production machines in the first phase and determining the first adjustment coefficient based on the operating data include: Calculate a first adjustment coefficient according to a first formula; The first formula is: in, is the first adjustment coefficient; is the preset basic coefficient; The preset weights for the corresponding indicators of temperature data, vibration data, pressure data, and output data in the operating data; The scores of the corresponding indicators of temperature data, vibration data, pressure data, and output data in the operation data; is the safety factor; n is the number of evaluation indicators; Among them, the temperature data corresponds to the temperature deviation rate score of the indicator , is the temperature data, The standard operating temperature is the preset optimal operating temperature of the production machine; the vibration data corresponds to the vibration intensity ratio score of the indicator , is the vibration data, The maximum vibration intensity allowed for the production machine; the pressure data corresponds to the pressure stability score of the indicator , is the pressure data, The preset standard working pressure of the production machine; the output data corresponds to the output achievement rate score of the indicator , For production data, The target output of the preset production machine; safety factor , is the safety pressure threshold, the maximum pressure allowed by the equipment, The safety temperature threshold is the maximum safe temperature allowed by the device.

[0015] By employing this technical solution, multi-dimensional data, including temperature, vibration, pressure, and output, is collected during machine operation. This data is then converted into standardized scoring metrics using a series of mathematical formulas. Finally, a comprehensive adjustment factor is calculated based on a weighted average and safety factor. This multi-level data processing mechanism uses the temperature deviation rate to reflect temperature stability, the vibration intensity ratio to reflect mechanical status, pressure stability to characterize system smoothness, and the output achievement rate to measure production effectiveness. Safety factors are also introduced to monitor key parameters, ultimately resulting in a comprehensive and reasonable adjustment reference value. This allows for a more accurate assessment of equipment operating status and effectively guides the execution of power adjustments.

[0016] Optionally, the power adjustment of all production machines at the production site according to the third operating power corresponding to each stage through the preset cloud-edge collaborative energy model includes: Detecting the real-time power values of all production machines in the current stage; determining whether the real-time power value reaches the third operating power corresponding to the current stage; if not, calculating a power difference between the real-time power value and the third operating power corresponding to the current stage; Through the cloud-edge collaborative energy model, all the production machines are controlled to adjust their power according to the power difference to obtain the adjusted real-time power value; It is determined whether the adjusted real-time power value reaches the third operating power of the current stage. If so, the power adjustment of the next stage is performed until the power adjustment of all stages is completed.

[0017] By adopting this technical solution, the current power status of production machines is first monitored in real time. The specific difference is then calculated by comparing it with the target power. A cloud-edge collaborative energy model is then used to perform precise power adjustments. Finally, iteration ensures that each stage achieves the desired goal. This multi-level closed-loop control mechanism uses real-time monitoring to reflect the current status, difference calculation to determine the adjustment amount, cloud-edge collaboration to ensure execution, and iteration to achieve full monitoring. Ultimately, this forms a complete power adjustment execution system that can more accurately complete power adjustment tasks for each machine at each stage, effectively improving the controllability and reliability of the entire adjustment process.

[0018] Optionally, after adjusting the power of all production machines at the production site according to the third operating power corresponding to each stage, the method further includes: Record the adjustment time, output changes and energy consumption data of all production machines during the power adjustment process at each stage; Calculate the adjustment success rate, production capacity improvement rate and energy saving rate of all production machines at each stage based on the adjustment time, output change and energy consumption data; Scoring the adjustment effects of all the production machines according to the adjustment success rate, capacity improvement rate, and energy saving rate to obtain a scoring result; The scoring result, the adjustment duration, the output change and the energy consumption data are stored in the historical data for subsequent power adjustment optimization.

[0019] By adopting the above technical solution, key performance indicators during the power adjustment process are first comprehensively recorded. Then, based on this raw data, effect evaluation indicators across multiple dimensions are calculated. The adjustment effect is quantified through a comprehensive score, and the evaluation results are stored in a historical database for continuous optimization. This multi-level effect evaluation mechanism reflects response speed through adjustment duration, production effectiveness through changes in output, energy efficiency through energy consumption data, and multi-dimensional evaluation through indicators such as success rate, improvement rate, and energy saving rate. Ultimately, a complete effect evaluation and optimization feedback system is formed, which can more comprehensively evaluate the adjustment effect and effectively support the continuous optimization of the adjustment strategy.

[0020] In a second aspect of the present application, a production management system based on a cloud-edge collaborative energy model is provided, specifically comprising: A data acquisition module is used to acquire usage data and historical data of any production machine in the current production site, wherein the usage data includes a first operating power and actual production efficiency, and the historical data includes historical power data, historical temperature data, and historical vibration data; a power matching module, configured to convert the first operating power of the production machine into a second operating power according to an actual production efficiency of the production machine; a power division module, configured to determine the number of stages required to be divided between the first operating power and the second operating power according to the first operating power, the second operating power, and the historical data; The power division module is further configured to determine a third operating power corresponding to each stage according to the number of stages, the first operating power, and the second operating power; The power adjustment module is used to adjust the power of all production machines in the production site according to the third operating power corresponding to each stage through the preset cloud-edge collaborative energy model.

[0021] By adopting the above technical solution, the first operating power and actual production efficiency of each production machine are obtained respectively, and its actual production efficiency is analyzed to determine the second operating power that needs to be converted. The appropriate number of adjustment stages is calculated based on historical data, and then the third operating power corresponding to each stage is determined. Then, all production machines at the production site are adjusted in stages according to the corresponding third operating power through the cloud-edge collaborative energy model. In this way, the operating power of each production machine at the production site is calculated for each stage, and the cloud-edge collaborative energy model is used to make collaborative adjustments according to the calculated operating power, thereby reducing resource loss at the production site.

[0022] In the third aspect of the present application, an electronic device is provided, including 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, and the processor is used to execute the instructions stored in the memory so that the electronic device performs any of the methods described above.

[0023] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions. When the instructions are executed, any one of the methods described above is executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a schematic diagram of the architecture of a production management system based on a cloud-edge collaborative energy model provided in an embodiment of the present application; Figure 2 This is a flowchart of a production management method based on a cloud-edge collaborative energy model provided by an embodiment of the present application; Figure 3 yes Figure 2 A schematic flow chart of a sub-step of step S102; Figure 4 yes Figure 2 A schematic flow chart of a sub-step of step S103; Figure 5 yes Figure 2 A schematic flow chart of a sub-step of step S104; Figure 6 yes Figure 2 A schematic flow chart of a sub-step of step S105; Figure 7 This is a schematic diagram of the adjustment data integration process provided by an embodiment of the present application; Figure 8 This is a structural diagram of a production management system based on a cloud-edge collaborative energy model provided by an embodiment of the present application; Figure 9 This is a structural diagram of an electronic device disclosed in an embodiment of the present application.

[0025] Explanation of the accompanying drawings: 11. Data acquisition module; 12. Power matching module; 13. Power division module; 14. Power adjustment module; 901. Processor; 902. Communication bus; 903. User interface; 904. Network interface; 905. Memory. DETAILED DESCRIPTION

[0026] In order 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 in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0027] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0028] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0029] Figure 1 An exemplary system architecture 010 of a production management system based on a cloud-edge collaborative energy big model is shown.

[0030] like Figure 1 As shown, system architecture 010 may include production machine 011, network 012, and electronic device 013. Network 012 is used to provide a medium for a communication link between production machine 011 and electronic device 013. Network 012 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0031] Workers can use electronic devices 013 to analyze production machines 011 via network 012, enabling remote monitoring and power adjustments of production machines at the production site while minimizing resource consumption. Production machines 011 can be installed with various monitoring applications, such as power monitoring, temperature monitoring, and vibration monitoring.

[0032] The production machine 011 is hardware and can be various types of equipment with production functions, including but not limited to processing equipment, assembly equipment, packaging equipment, and testing equipment.

[0033] The electronic device 013 may be a server that provides various management services, such as a management server that adjusts the power of the production machine 011. The management server may analyze and process the received monitoring data and execute the processing results (power adjustment instructions).

[0034] It should be noted that a server can be either hardware or software. When a server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When a server is software, it can be implemented as multiple software programs or software modules (for example, multiple software programs or software modules used to provide distributed services), or as a single software program or software module. This is not specifically limited here.

[0035] It should be understood that Figure 1 The number of production machines 011, networks 012, and electronic devices 013 in the above diagram is merely illustrative. Depending on implementation requirements, any number of production machines 011, networks 012, and electronic devices 013 may be used. In particular, if target data does not need to be acquired remotely, the above system architecture may not include network 012, but may only include production machines 011 or electronic devices 013.

[0036] Taking the electronic equipment side as an example, the production management method based on the cloud-edge collaborative energy model provided in this application is explained below.

[0037] This application provides a production management method based on a cloud-edge collaborative energy model. Figure 2 , Figure 2 : This is a flowchart of a production management method based on a cloud-edge collaborative energy model provided by an embodiment of the present application, including steps S101 to S105. The above steps are as follows: S101: Acquire usage data and historical data of any production machine in the current production site, where the usage data includes a first operating power and actual production efficiency, and the historical data includes historical power data, historical temperature data, and historical vibration data.

[0038] In an embodiment of the present application, the production site refers to a specific place where industrial production activities are carried out, which refers to a factory workshop or production workshop containing multiple production machines. The production machines in the production site refer to various types of equipment used for industrial production, including processing equipment, assembly equipment, etc. When the production machines are running, usage data will be generated. The usage data represents the real-time monitoring data of the production machine during its current operation, including the first operating power and the actual production efficiency. The first operating power refers to the power value when the initial data is obtained when the production machine is currently running, and the actual production efficiency is used to represent the actual output effect of the production machine under the current power. Similarly, each production machine will generate historical data and be recorded during operation. The historical data represents the data records collected during the previous operation of the production machine, which includes historical power data, historical temperature data, and historical vibration data. The historical power data refers to the recorded power values of the production machine at different time points, and the corresponding historical temperature data is used to represent the recorded temperature changes of each component of the production machine during operation. Similarly, the historical vibration data refers to the recorded mechanical vibration intensity data of the production machine during operation.

[0039] Specifically, the electronic device first selects a representative production machine from the production site as a sample, and collects its first operating power and actual production efficiency in real time through various sensors installed on the machine. At the same time, historical data such as historical power records, historical temperature records of each component, and historical vibration records recorded during the previous operation of the machine are retrieved from the database.

[0040] S102: Converting the first operating power of the production machine into the second operating power according to the actual production efficiency of the production machine.

[0041] In the embodiment of the present application, the second operating power is used to represent the target adjustment power calculated according to the production efficiency.

[0042] Specifically, the electronic device first compares the actual production efficiency with the preset target production efficiency to determine whether the actual production efficiency is within a reasonable range. If the efficiency is too low or too high, the first operating power needs to be adjusted accordingly. By calculating the efficiency ratio and combining it with the preset adjustment rules, the specific power adjustment coefficient is determined, ultimately determining the target power value to which the production machine needs to be adjusted, namely the second operating power.

[0043] Please refer to Figure 3 , Figure 3 This is a schematic diagram of a sub-step flow chart of step S102 provided in an embodiment of the present application. Based on the above embodiment, as an optional embodiment, S102: converting the first operating power of the production machine into the second operating power according to the actual production efficiency of the production machine can specifically include the following steps: S1021: Obtain the target production efficiency of the production machine, and calculate the efficiency ratio of the actual production efficiency of the production machine to the target production efficiency.

[0044] In an embodiment of the present application, the target production efficiency represents the ideal output level that the production machine should achieve under normal operating conditions, and refers to a standard efficiency value pre-set according to equipment specifications and production plans. In order to obtain the second operating power, it is necessary to calculate the efficiency ratio of the actual efficiency to the target efficiency. The efficiency ratio represents the numerical proportional relationship between the actual efficiency and the target efficiency, and is used to quantify the degree of deviation from the current efficiency level.

[0045] Specifically, the electronic equipment first retrieves the standard efficiency parameters for the production machine model from the production plan database, including indicators such as theoretical maximum production capacity and standard pass rate, as the target efficiency value. It then converts the real-time production and quality data collected into actual efficiency indicators. By dividing the actual efficiency by the target efficiency, it calculates the efficiency ratio that reflects the current efficiency level.

[0046] S1022: Determine a power adjustment coefficient corresponding to the production machine according to a preset mapping relationship between the efficiency ratio and the power adjustment coefficient.

[0047] In the embodiment of the present application, the power adjustment coefficient refers to a multiplication factor used to adjust the first operating power.

[0048] Specifically, the electronic device first reads pre-configured mapping data, which can be a discrete numerical table or a continuous function curve. It then substitutes the calculated efficiency ratio into the mapping, taking into account the device's operating safety margins and the smoothness of the adjustment, and calculates the appropriate power adjustment factor through interpolation or function calculation.

[0049] S1023: Multiply the first operating power by the corresponding power adjustment coefficient to obtain a second operating power.

[0050] In the embodiment of the present application, the second operating power represents the target power value to which the production machine needs to be adjusted, and is used to indicate the specific numerical value of the power adjustment; obtaining refers to obtaining the final result value through calculation.

[0051] Specifically, the electronic device first checks whether the current first operating power value is within the device's allowable power range, then multiplies this power value by the previously determined power adjustment factor. During this calculation, the system considers the power adjustment step limit to ensure that the calculated second operating power meets the efficiency improvement requirements while remaining within the device's safe operating range.

[0052] S103: Determine the number of stages that need to be divided between the first operating power and the second operating power according to the first operating power, the second operating power, and historical data.

[0053] In the embodiment of the present application, the number of stages represents the specific number of steps into which the power adjustment process is divided, and is used to indicate the degree of subdivision of the progressive adjustment.

[0054] Specifically, the electronic device first calculates the difference between the first and second operating power levels and analyzes historical records of successful adjustments with similar amplitudes. It then determines the appropriate number of phases based on power adjustment safety requirements, equipment response characteristics, production continuity needs, and the urgency of the current production task.

[0055] Please refer to Figure 4 , Figure 4 This is a schematic diagram of a sub-step flow chart of step S103 provided in an embodiment of the present application. Based on the above embodiment, as an optional embodiment, S103: determining the number of stages to be divided between the first operating power and the second operating power based on the first operating power, the second operating power, and historical data, can specifically include the following steps: S1031: Obtaining a power adjustment range of the production machine according to the first operating power and the second operating power of the production machine.

[0056] In the embodiment of the present application, the power adjustment amplitude refers to the difference between two power values, indicating the power value that needs to be changed.

[0057] Specifically, the electronic device first compares the values of the first and second operating powers to determine whether the adjustment is an increase or decrease. It then calculates the difference between the two power values and converts this difference into a standardized representation of the adjustment amplitude. The electronic device also converts the adjustment amplitude into a relative proportional value, taking into account the rated power range of the production machine, to more intuitively assess the scale of the adjustment. Furthermore, the electronic device verifies whether the calculated adjustment amplitude is within the device's allowable single adjustment range to determine the power adjustment amplitude for the production machine.

[0058] S1032: Calculate a single safety adjustment threshold of the production machine based on historical data.

[0059] In the embodiment of the present application, the single safety adjustment threshold refers to the maximum power change allowed for each adjustment under the premise of ensuring equipment safety and production stability.

[0060] Specifically, the electronic equipment first analyzes historical power data to identify the device's operating characteristics at different power levels. It then combines historical temperature data to assess the impact of power changes on device temperature and the hysteresis effect of temperature changes. Historical vibration data is also used to analyze the impact of power adjustments on the device's mechanical condition. The system correlates these three types of data to establish a relationship model between power adjustments and device response. Taking into account thermal and mechanical stability constraints, it ultimately determines the threshold that ensures effective adjustments while maintaining safe device operation.

[0061] S1033: Based on the power adjustment range and the corresponding single safety adjustment threshold, obtain the number of stages that need to be divided between the first operating power and the second operating power.

[0062] Specifically, the electronic device first compares the power adjustment amplitude with the single safety adjustment threshold to determine whether a step-by-step adjustment is necessary. If the adjustment amplitude exceeds the safety threshold, the adjustment process needs to be divided into multiple stages. The electronic device divides the total adjustment amplitude by the single safety adjustment threshold and rounds up to the nearest integer to determine the initial number of stages. The electronic device then optimizes this value, taking into account the device's response characteristics and the required adjustment time, to ensure that the adjustment amount in each stage does not exceed the safety threshold while ensuring adjustment efficiency, ultimately determining the final number of stages.

[0063] S104: Determine a third operating power corresponding to each stage according to the number of stages, the first operating power, and the second operating power.

[0064] In the embodiment of the present application, the third operating power represents the transition power value of each stage, and refers to the intermediate transition state between the starting power and the target power.

[0065] Specifically, the electronic device first calculates the overall difference between the first operating power and the second operating power, and then evenly divides this difference according to the number of stages to obtain the basic adjustment increment for each stage. Next, the electronic device will consider the adjustment characteristics of the device in different power ranges and optimize the adjustment increments for each stage to ensure that the adjustment of each stage is both smooth and efficient. At the same time, the electronic device will also verify whether the power value of each stage is within the reasonable operating range of the device, and ensure that the power difference between adjacent stages does not exceed the safety threshold, and finally determine the third operating power of each stage.

[0066] Please refer to Figure 5 , Figure 5This is a sub-step flow diagram of step S104 provided in an embodiment of the present application. Based on the above embodiment, as an optional embodiment, S104: determining the third operating power corresponding to each stage based on the number of stages, the first operating power, and the second operating power may specifically include the following steps: S1041: Detect the operating data of the production machine in the first stage, determine the first adjustment coefficient based on the operating data, subtract the second operating power from the first operating power and divide it by the number of stages to obtain a first adjustment reference value, multiply the first adjustment reference value by the first adjustment coefficient to obtain a first product, and add the first operating power to the first product to obtain the third operating power of the first stage, where the first stage is the first stage of the multiple divided stages.

[0067] In an embodiment of the present application, there are multiple stages for power adjustment, wherein the first stage represents the starting stage in the multi-stage adjustment process, and is used to indicate the starting link of the adjustment. When the adjustment starts, the electronic device will detect the operating data of the production machine. The operating data represents the current working status information of the production machine, and refers to real-time monitoring parameters such as temperature and vibration. In order to calculate the actual adjustment amount, it is necessary to first obtain the first adjustment reference value. The first adjustment reference value refers to the standard adjustment amount of each stage, which represents the uniform distribution value under ideal conditions. In order to make the adjusted power more in line with reality and to make the actual adjustment amount more realistic, it is necessary to calculate the first adjustment coefficient of the production machine. The first adjustment coefficient represents a correction factor determined according to the operating conditions, which is used to adjust the amplitude of the power change. Finally, the first product can be obtained based on the obtained first adjustment reference value and the coefficient. The first product represents the actual adjustment amount after considering the actual situation, and refers to the result after the reference value is corrected.

[0068] Specifically, the electronic equipment first collects temperature data in real time to assess the heat load level of each component and its changing trends. It also monitors vibration data to analyze the mechanical stability of the equipment. Pressure data is combined to determine the flow state of the internal medium and the pressure tolerance of the equipment. Finally, output data is used to assess the current production efficiency level. The system inputs this data into a comprehensive evaluation model to calculate a first adjustment coefficient reflecting the current operating status. The system then calculates a theoretical uniform adjustment as the first adjustment reference value. This first adjustment reference value is multiplied by the first adjustment coefficient to obtain the actual adjustment value, which is then added to the first operating power to obtain the third operating power for the first stage.

[0069] S1042: The operating data of the production machines include temperature data, vibration data, pressure data, and output data. The operating data of each of the production machines in the first phase is detected, and a first adjustment coefficient is determined based on the operating data.

[0070] In an embodiment of the present application, the operation of the production machine will generate temperature data. The temperature data refers to the thermal load status information of the key components of the production machine, which is used to represent the temperature distribution and changes during the operation process. Vibration will also occur, generating vibration data. The vibration data represents the mechanical stability index of the equipment, which refers to the vibration intensity and frequency characteristics during operation. The corresponding production machine will also generate pressure. The corresponding pressure data is used to represent the pressure state of each pipeline and cavity of the equipment, which refers to the stress condition of the internal medium. Finally, the production machine will produce products. The corresponding output data represents the production efficiency status of the equipment, which is used to reflect the product output per unit time.

[0071] Specifically, the first formula adopts a three-tier architecture design The benchmark coefficient layer sets the basic range of adjustment through β to ensure that the adjustment range is controllable; the scoring layer integrates the evaluation results of multiple dimensions through a weighted average structure, among which the temperature deviation rate , using the relative deviation form to eliminate the dimension effect, the vibration intensity ratio , safety margin and pressure stability are reflected through limit value comparison , using the maximum deviation to capture fluctuations, production achievement rate Use direct ratio to reflect efficiency status; safety correction layer Multiple comparisons ensure that all parameters operate within a safe range. Each score is normalized to the interval [0,1], ensuring comparability of results and facilitating comprehensive evaluation.

[0072] The electronic device first obtains real-time data of various operating parameters, including temperature, vibration, pressure and output data. For temperature data, the relative deviation between the actual value and the standard value is calculated and converted into a temperature deviation rate score; for vibration data, the actual vibration intensity is compared with the maximum allowable value to calculate the vibration intensity ratio score; for pressure data, the maximum deviation is identified and the pressure stability score is calculated; for output data, the actual output is compared with the target value to obtain the output achievement rate score. The electronic device then multiplies these scores with the corresponding weight coefficients and normalizes them, and then multiplies them with the reference coefficient. Finally, the electronic device calculates the degree of deviation of temperature and pressure from the safety threshold, selects the maximum deviation value to generate a safety correction coefficient, and multiplies it with the above result to obtain the final first adjustment coefficient.

[0073] S1043: Take the next stage as the first stage, and execute the operation data of the production machine in the first stage to detect it, determine the first adjustment coefficient according to the operation data, subtract the second operating power from the first operating power and divide it by the number of stages to obtain the first adjustment reference value, multiply the first adjustment reference value by the first adjustment coefficient to obtain the first product, and add the first operating power to the first product to obtain the third operating power of the first stage, until the third operating power of the last stage is calculated and the third operating power corresponding to each stage is obtained.

[0074] Specifically, the calculation starting point is first set to the next production stage, and the operating data of the machine in this stage is collected in real time, including key parameters such as temperature, pressure, vibration intensity, and output. Then, based on this data, a first adjustment coefficient reflecting the operating status of the equipment is calculated through multi-dimensional evaluation. After that, the total adjustment amount from the first operating power to the second operating power is calculated, and it is evenly distributed to the remaining stages to obtain the basic adjustment amount for each stage. This basic adjustment amount is multiplied by the adjustment coefficient calculated previously to obtain the actual adjustment amount that takes into account the actual operating conditions. Finally, this adjustment amount is superimposed on the current power to obtain the third operating power of this stage. Repeat the above steps until the power calculation of all stages is completed, and finally the third operating power corresponding to each stage is obtained.

[0075] S105: Through the preset cloud-edge collaborative energy model, the power of all production machines at the production site is adjusted according to the third operating power corresponding to each stage.

[0076] In an embodiment of the present application, the cloud-edge collaborative energy model represents an energy management system that integrates a cloud-based deep learning engine and an edge execution unit.

[0077] Specifically, the cloud-edge collaborative energy model first receives the target power values for each phase. Combining historical data and device characteristics, it develops a personalized power adjustment strategy for each machine, tailored to the third operating power corresponding to each phase. Based on the issued strategy, the edge execution unit simultaneously initiates power adjustments for multiple devices. During execution, the edge controller collects device response data in real time and performs preliminary analysis to ensure that the adjustment process meets expectations.

[0078] Please refer to Figure 6 , Figure 6 This is a sub-step flow diagram of step S105 provided in an embodiment of the present application. Based on the above embodiment, as an optional embodiment, S105: Using the preset cloud-edge collaborative energy model, all production machines at the production site are adjusted in power according to the third operating power corresponding to each stage. This step may specifically include the following steps: S1051: Detect the real-time power values of all production machines in the current stage.

[0079] In the embodiment of the present application, the real-time power is a first power value that changes dynamically during the adjustment process.

[0080] Specifically, the electronic equipment first identifies a list of all production machines requiring testing during the current production phase and activates the power detection devices located on each machine. The detection devices continuously collect power data at a preset sampling interval and transmit it in real time via the data acquisition system. The electronic equipment then preprocesses the collected raw data, including filtering, calibration, and outlier processing, to ensure data accuracy.

[0081] S1052: Determine whether the real-time power value reaches a third operating power corresponding to the current stage; if not, calculate a power difference between the real-time power value and the third operating power corresponding to the current stage.

[0082] Specifically, during the power adjustment process, the electronic device continuously collects the changing actual power value and compares this dynamic data with the third operating power value for that stage in real time. Using pre-set judgment logic, the electronic device determines whether the current adjustment progress meets the target requirements. If it determines that the target requirements are not met, the electronic device calculates the difference between the dynamically changing actual power and the third operating power and updates this difference data in real time.

[0083] S1053: Through the cloud-edge collaborative energy model, all production machines are controlled to adjust their power according to the power difference and obtain the adjusted real-time power value.

[0084] Specifically, the cloud-edge collaborative energy model first analyzes the power differentials of all machines and develops customized adjustment plans based on the magnitude of these differentials. For machines with large differentials, a large-step, rapid adjustment strategy is used; for machines with small differentials, a small-step, fine-grained adjustment strategy is used. Upon receiving the adjustment instructions, the edge execution unit initiates the adjustment process sequentially, based on the differential size, ensuring that each machine is precisely adjusted according to its own differential, ultimately achieving the adjusted real-time power values for all production machines.

[0085] S1054: Determine whether the adjusted real-time power value reaches the third operating power of the current stage. If so, execute the power adjustment of the next stage until the power adjustment of all stages is completed.

[0086] Specifically, the electronic device first verifies the adjusted real-time power value to ensure its authenticity and reliability. It then accurately compares the verified power value with the target power for the current stage, using pre-defined judgment rules to determine whether the required power is met. Once the power adjustment for the current stage is confirmed to meet the target, the electronic device automatically triggers the stage switching mechanism, loading the target power value and adjustment parameters for the next stage. During the stage switching process, the electronic device ensures smooth switching to avoid sudden power changes. The electronic device continues this cycle of adjustment, judgment, and switching until all power adjustments for each stage are complete.

[0087] Please refer to Figure 7 , Figure 7 This is a flow chart of adjusting data integration provided by an embodiment of the present application. Based on the above embodiment, as an optional embodiment, S105: After the step of adjusting the power of all production machines at the production site according to the third operating power corresponding to each stage through the preset cloud-edge collaborative energy model, the process of adjusting data integration is also included, which specifically includes the following steps: S1055: Record the adjustment time, output changes and energy consumption data of all production machines during the power adjustment process at each stage.

[0088] In an embodiment of the present application, the production machine will have a corresponding adjustment time during the adjustment process. The adjustment time indicates the time required for each machine to complete the power adjustment at each stage. At the same time, the output of the machine will also change during the adjustment process. The output change refers to the dynamic change of the production quantity during the power adjustment process. After the adjustment, the energy consumption of the production machine will match its efficiency, and corresponding energy consumption data will be generated. The energy consumption data indicates the energy consumption before and after the power adjustment.

[0089] Specifically, the electronic equipment establishes an independent data collection channel for each production machine, recording the start and end times and duration of power adjustments in real time. Simultaneously, a production capacity monitoring system continuously monitors changes in each machine's output, including key indicators such as product quantity and quality. Regarding energy consumption, the electronic equipment uses an energy consumption monitoring module to record data such as power consumption and energy efficiency at each stage. All collected data is accurately timestamped to ensure data timeliness and traceability. The electronic equipment categorizes and stores this data according to dimensions such as machine number and adjustment stage, forming a complete adjustment process database to provide data support for subsequent analysis and optimization.

[0090] S1056: Calculate the adjustment success rate, capacity improvement rate, and energy saving rate of all production machines at each stage based on adjustment time, output changes, and energy consumption data.

[0091] In the embodiment of the present application, when the production machine performs power adjustment, some will succeed and some will fail, and a corresponding adjustment success rate will be generated. The adjustment success rate represents the proportion index of the power adjustment reaching the target requirement. Correspondingly, after the adjustment is successful, the production capacity of the machine will increase, and there will be a corresponding production capacity improvement rate. The production capacity improvement rate refers to the percentage increase in output after adjustment relative to before adjustment. At the same time, after the adjustment is successful, the resource loss will also be reduced accordingly, and there will be a corresponding energy-saving rate. The energy-saving rate represents the relative proportion of energy consumption reduction.

[0092] Specifically, the electronic equipment first collects statistics on the adjustment duration of each machine and calculates the proportion of successfully completed adjustments in each stage to the total number of adjustments, thereby obtaining the adjustment success rate. Simultaneously, the electronic equipment calculates the difference in output before and after the adjustment based on production records and converts this into a production capacity improvement rate by factoring in time. Regarding energy consumption, the electronic equipment compares the energy consumption data before and after the adjustment and calculates the proportion of energy saved to the original energy consumption to obtain the energy saving rate.

[0093] S1057: Score the adjustment effects of all production machines based on the adjustment success rate, capacity improvement rate, and energy saving rate to obtain a scoring result.

[0094] Specifically, the electronic equipment company first normalizes the three key indicators for comparability. Then, based on production management objectives, it assigns appropriate weights to the adjustment success rate, capacity improvement rate, and energy saving rate. The scoring process uses a weighted calculation method, comparing the actual values of each indicator with the standard values. Each score is determined according to pre-set scoring rules. Correction factors are then introduced to optimize the results. Finally, the weighted summation of each score is used to generate a comprehensive score for each machine.

[0095] S1058: The scoring results, adjustment duration, output change, and energy consumption data are stored in historical data for subsequent power adjustment optimization.

[0096] Specifically, the electronic device first standardizes the format of the scoring results, adjustment duration, output change, and energy consumption data to ensure consistency with the historical database format. The device then generates a unique record identifier and writes this data as a complete data packet to the historical database. During the writing process, the device automatically adds metadata such as timestamps and device information to establish a connection with existing historical records. The device also performs data verification to ensure the accuracy and completeness of the stored data. After storage is complete, the device updates the data index, making the newly added data immediately available for subsequent analysis and optimization.

[0097] refer to Figure 8 , the present application also provides a production management system 10 based on a cloud-edge collaborative energy model, specifically including: A data acquisition module 11 is configured to acquire usage data and historical data of any production machine in the current production site, wherein the usage data includes a first operating power and actual production efficiency, and the historical data includes historical power data, historical temperature data, and historical vibration data; a power matching module 12, configured to convert the first operating power of the production machine into a second operating power according to the actual production efficiency of the production machine; a power division module 13, configured to determine the number of stages to be divided between the first operating power and the second operating power according to the first operating power, the second operating power, and the historical data; The power division module 13 is further configured to determine a third operating power corresponding to each stage according to the number of stages, the first operating power, and the second operating power; The power adjustment module 14 is used to adjust the power of all production machines at the production site according to the third operating power corresponding to each stage through a preset cloud-edge collaborative energy model.

[0098] Optionally, the power matching module 12 is specifically configured to: Obtaining a target production efficiency of the production machine, and calculating an efficiency ratio between the actual production efficiency of the production machine and the target production efficiency; Determining a power adjustment coefficient corresponding to the production machine according to a preset mapping relationship between the efficiency ratio and the power adjustment coefficient; The first operating power is multiplied by a corresponding power adjustment coefficient to obtain the second operating power.

[0099] Optionally, the power division module 13 is specifically configured to: Obtaining a power adjustment range of the production machine according to the first operating power and the second operating power of the production machine; Calculating a single safety adjustment threshold of the production machine based on the historical data; Based on the power adjustment amplitude and the corresponding single safety adjustment threshold, the number of stages that need to be divided between the first operating power and the second operating power is obtained.

[0100] Optionally, the power division module 13 is further configured to: detecting operating data of the production machine in a first stage, determining a first adjustment coefficient based on the operating data, subtracting the second operating power from the first operating power and dividing the result by the number of stages to obtain a first adjustment reference value, multiplying the first adjustment reference value by the first adjustment coefficient to obtain a first product, and adding the first operating power to the first product to obtain a third operating power for the first stage, where the first stage is the first of the multiple stages; The next stage is taken as the first stage, and the operating data of the production machine in the first stage is detected, a first adjustment coefficient is determined according to the operating data, the second operating power is subtracted from the first operating power and the resultant is divided by the number of stages to obtain a first adjustment reference value, the first adjustment reference value is multiplied by the first adjustment coefficient to obtain a first product, and the first operating power is added to the first product to obtain the third operating power of the first stage, until the third operating power of the last stage is calculated and the third operating power corresponding to each stage is obtained.

[0101] Calculate a first adjustment coefficient according to a first formula; The first formula is: in, is the first adjustment coefficient; is the preset basic coefficient; The preset weights for the corresponding indicators of temperature data, vibration data, pressure data, and output data in the operating data; The scores of the corresponding indicators of temperature data, vibration data, pressure data, and output data in the operation data; is the safety factor; n is the number of evaluation indicators; Among them, the temperature data corresponds to the temperature deviation rate score of the indicator , is the temperature data, The standard operating temperature is the preset optimal operating temperature of the production machine; the vibration data corresponds to the vibration intensity ratio score of the indicator , is the vibration data, The maximum vibration intensity allowed for the production machine; the pressure data corresponds to the pressure stability score of the indicator , is the pressure data, The preset standard working pressure of the production machine; the output data corresponds to the output achievement rate score of the indicator , For production data, The target output of the preset production machine; safety factor , is the safety pressure threshold, the maximum pressure allowed by the equipment, The safety temperature threshold is the maximum safe temperature allowed by the device.

[0102] Optionally, the power adjustment module 14 is specifically configured to: Detecting the real-time power values of all production machines in the current stage; determining whether the real-time power value reaches the third operating power corresponding to the current stage; if not, calculating a power difference between the real-time power value and the third operating power corresponding to the current stage; Through the cloud-edge collaborative energy model, all the production machines are controlled to adjust their power according to the power difference to obtain the adjusted real-time power value; It is determined whether the adjusted real-time power value reaches the third operating power of the current stage. If so, the power adjustment of the next stage is performed until the power adjustment of all stages is completed.

[0103] Optionally, a production management system based on a cloud-edge collaborative energy model further includes an adjustment data integration module, specifically for: Record the adjustment time, output changes and energy consumption data of all production machines during the power adjustment process at each stage; Calculate the adjustment success rate, production capacity improvement rate and energy saving rate of all production machines at each stage based on the adjustment time, output change and energy consumption data; Scoring the adjustment effects of all the production machines according to the adjustment success rate, capacity improvement rate, and energy saving rate to obtain a scoring result; The scoring result, the adjustment duration, the output change and the energy consumption data are stored in the historical data for subsequent power adjustment optimization.

[0104] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0105] This embodiment also discloses an electronic device, referring to Figure 9 , Figure 9 Schematic diagram of the structure of an electronic device disclosed in an embodiment of the present application. The electronic device 013 may include: at least one processor 901, at least one communication bus 902, a user interface 903, a network interface 904, and at least one memory 905.

[0106] The communication bus 902 is used to implement connection and communication between these components.

[0107] The user interface 903 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 903 may also include a standard wired interface and a wireless interface.

[0108] The network interface 904 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0109] The processor 901 may include one or more processing cores. Using various interfaces and circuits, the processor 901 connects to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in the memory 905, as well as accesses data stored in the memory 905, to perform various server functions and process data. Optionally, the processor 901 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 901 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display screen; and the modem handles wireless communications. It is understood that the modem may also be implemented independently of the processor 901 and implemented on a separate chip.

[0110] Among them, the memory 905 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 905 includes a non-transitory computer-readable storage medium. The memory 905 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 905 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 a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 905 may also be optionally at least one storage device located away from the aforementioned processor 901. Reference Figure 9 , the memory 905 as a computer storage medium may include an operating system, a network communication module, a user interface module and a production management application.

[0111] exist Figure 9 In the electronic device shown, the user interface 903 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 901 can be used to call the production management application stored in the memory 905. When executed by one or more processors 901, the electronic device 013 executes one or more methods in the above embodiments.

[0112] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0113] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0114] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0115] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0116] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0117] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of this application. The aforementioned memory includes various media that can store program code, such as USB flash drives, mobile hard drives, magnetic disks, or optical disks.

[0118] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any variations, uses or adaptive changes of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A production management method based on a cloud-edge collaborative energy model, characterized in that: Applied to electronic equipment, the method includes: Obtaining usage data and historical data of any production machine in the current production site, wherein the usage data includes a first operating power and actual production efficiency, and the historical data includes historical power data, historical temperature data, and historical vibration data; converting the first operating power of the production machine into a second operating power according to the actual production efficiency of the production machine; determining, according to the first operating power, the second operating power, and the historical data, the number of stages required to be divided between the first operating power and the second operating power; determining a third operating power corresponding to each stage according to the number of stages, the first operating power, and the second operating power; Through the preset cloud-edge collaborative energy model, the power of all production machines at the production site is adjusted according to the third operating power corresponding to each stage.

2. The method according to claim 1, characterized in that The converting the first operating power of the production machine into the second operating power according to the actual production efficiency of the production machine includes: Obtaining a target production efficiency of the production machine, and calculating an efficiency ratio between the actual production efficiency of the production machine and the target production efficiency; Determining a power adjustment coefficient corresponding to the production machine according to a preset mapping relationship between the efficiency ratio and the power adjustment coefficient; The first operating power is multiplied by a corresponding power adjustment coefficient to obtain the second operating power.

3. The method according to claim 1, characterized in that The determining, according to the first operating power, the second operating power, and the historical data, the number of stages required to be divided between the first operating power and the second operating power includes: Obtaining a power adjustment range of the production machine according to the first operating power and the second operating power of the production machine; Calculating a single safety adjustment threshold of the production machine based on the historical data; Based on the power adjustment amplitude and the corresponding single safety adjustment threshold, the number of stages that need to be divided between the first operating power and the second operating power is obtained.

4. The method according to claim 1, wherein The determining, according to the number of stages, the first operating power, and the second operating power, a third operating power corresponding to each stage includes: detecting operating data of the production machine in a first stage, determining a first adjustment coefficient based on the operating data, subtracting the second operating power from the first operating power and dividing the result by the number of stages to obtain a first adjustment reference value, multiplying the first adjustment reference value by the first adjustment coefficient to obtain a first product, and adding the first operating power to the first product to obtain a third operating power for the first stage, where the first stage is the first of the multiple stages; The next stage is taken as the first stage, and the operating data of the production machine in the first stage is detected, a first adjustment coefficient is determined according to the operating data, the second operating power is subtracted from the first operating power and the resultant is divided by the number of stages to obtain a first adjustment reference value, the first adjustment reference value is multiplied by the first adjustment coefficient to obtain a first product, and the first operating power is added to the first product to obtain the third operating power of the first stage, until the third operating power of the last stage is calculated and the third operating power corresponding to each stage is obtained.

5. The method according to claim 4, characterized in that The operating data of the production machines include temperature data, vibration data, pressure data, and output data. The detecting the operating data of each production machine in the first phase and determining the first adjustment coefficient based on the operating data include: Calculate a first adjustment coefficient according to a first formula; The first formula is: in, is the first adjustment coefficient; is the preset basic coefficient; The preset weights for the corresponding indicators of temperature data, vibration data, pressure data, and output data in the operating data; The scores of the corresponding indicators of temperature data, vibration data, pressure data, and output data in the operation data; is the safety factor; n is the number of evaluation indicators; Among them, the temperature data corresponds to the temperature deviation rate score of the indicator , is the temperature data, The standard operating temperature is the preset optimal operating temperature of the production machine; the vibration data corresponds to the vibration intensity ratio score of the indicator , is the vibration data, The maximum vibration intensity allowed for the production machine; the pressure data corresponds to the pressure stability score of the indicator , is the pressure data, The preset standard working pressure of the production machine; the output data corresponds to the output achievement rate score of the indicator , For production data, The target output of the preset production machine; safety factor , is the safety pressure threshold, the maximum pressure allowed by the equipment, The safety temperature threshold is the maximum safe temperature allowed by the device.

6. The method according to claim 1, characterized in that The preset cloud-edge collaborative energy model is used to adjust the power of all production machines at the production site according to the third operating power corresponding to each stage, including: Detecting the real-time power values of all production machines in the current stage; determining whether the real-time power value reaches the third operating power corresponding to the current stage; if not, calculating a power difference between the real-time power value and the third operating power corresponding to the current stage; Through the cloud-edge collaborative energy model, all the production machines are controlled to adjust their power according to the power difference to obtain the adjusted real-time power value; It is determined whether the adjusted real-time power value reaches the third operating power of the current stage. If so, the power adjustment of the next stage is performed until the power adjustment of all stages is completed.

7. The method according to claim 6, characterized in that After adjusting the power of all production machines at the production site according to the third operating power corresponding to each stage, the method further includes: Record the adjustment time, output changes and energy consumption data of all production machines during the power adjustment process at each stage; Calculate the adjustment success rate, production capacity improvement rate and energy saving rate of all production machines at each stage based on the adjustment time, output change and energy consumption data; Scoring the adjustment effects of all the production machines according to the adjustment success rate, capacity improvement rate, and energy saving rate to obtain a scoring result; The scoring result, the adjustment duration, the output change and the energy consumption data are stored in the historical data for subsequent power adjustment optimization.

8. A production management system based on a cloud-edge collaborative energy model, characterized in that: Applied to electronic equipment, the system includes: A data acquisition module is used to acquire usage data and historical data of any production machine in the current production site, wherein the usage data includes a first operating power and actual production efficiency, and the historical data includes historical power data, historical temperature data, and historical vibration data; a power matching module, configured to convert the first operating power of the production machine into a second operating power according to an actual production efficiency of the production machine; a power division module, configured to determine the number of stages required to be divided between the first operating power and the second operating power according to the first operating power, the second operating power, and the historical data; The power division module is further configured to determine a third operating power corresponding to each stage according to the number of stages, the first operating power, and the second operating power; The power adjustment module is used to adjust the power of all production machines at the production site according to the third operating power corresponding to each stage through a preset cloud-edge collaborative energy model.

9. An electronic device, characterized in that: The electronic device comprises 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 so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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