Electronic package supervision intelligent analysis and adjustment system

Abstract

The invention, which relates to the energy management field, discloses an electronic package supervision intelligent analysis and adjustment system comprising a plurality of single energy systems and a cooperative work scheduling module. According to the intelligent analysis and adjustment system for electronic package supervision, through reasonable scheduling of adjustable energy on the supply side and the demand side in a single-energy system, the operation cost of the single-energy system is effectively reduced, economic operation of the single-energy system is realized, and the energy consumption of the single-energy system is reduced through cooperation of multiple single-energy systems. Each single energy system can complete an optimization decision without acquiring internal information of other single energy systems, the overall economical efficiency of the system is effectively improved, pollutant emission is reduced, meanwhile, continuous adjustment of prediction data can be achieved through continuous learning adjustment of the learning unit, and the prediction efficiency is improved. And when the load generated by the energy reaches the corresponding switching node, the energy type is automatically switched through the energy switching module, so that the scheduling of the unadjustable energy is realized.

Description

Technical Field

[0001] The present invention relates to energy management technologies, and particularly to an intelligent analysis and regulation system for electronic packaging supervision. Background Art

[0002] With the rapid development of the manufacturing industry and the continuous change of market demands, enterprises increasingly need to improve the flexibility and efficiency of production lines to adapt to the rapid adjustment of production demands. The demand for the logistics industry has increased sharply, especially in the fields of packaging and transshipment. In modern logistics systems, how to effectively manage and control the quantity of packaging bags has become a key factor in improving logistics efficiency, reducing costs, and ensuring the safety of goods.

[0003] During the use of current electronic packaging equipment, the number of loads increases: With the wide application of more and more equipment in production, the quantity and types of loads are constantly increasing. Due to the uncertainty of the usage time and frequency of load equipment by users, it is impossible to better conduct limited supervision on the production costs of electronic packaging for various load equipment. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent analysis and regulation system for electronic packaging supervision to solve the above deficiencies in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: An intelligent analysis and regulation system for electronic packaging supervision, including multiple single-energy systems and a collaborative work scheduling module:

[0006] The multiple single-energy systems are used to achieve distributed energy settings, and each of the multiple single-energy systems includes:

[0007] An energy load module, which is used to receive and use the energy emitted by the energy storage system.

[0008] An energy storage system, which is used to store non-adjustable energy generated by non-adjustable energy generating devices and adjustable energy generated by adjustable energy generating devices, and send the stored energy to the energy load module.

[0009] Non-adjustable energy generating devices, which are used to generate non-adjustable energy, and the non-adjustable energy includes but is not limited to light energy, wind energy, and wave energy.

[0010] Adjustable energy generating devices, which are used to generate adjustable energy, and the adjustable energy includes but is not limited to diesel, fuel cells, and micro gas turbines.

[0011] Non-adjustable energy scheduling module, which is used to schedule the optimal scheduling of non-adjustable energy in a single energy system;

[0012] Adjustable energy scheduling module, which is used to achieve the reasonable scheduling of adjustable energy on both the supply and demand sides;

[0013] The collaborative work scheduling module is used to schedule the collaborative work of multiple single energy systems.

[0014] Furthermore, the specific working steps of the collaborative work scheduling module are as follows:

[0015] Adopt the consistent hashing algorithm to evenly slice multiple single energy systems. At the same time, combine with the Cap theorem to implement fault transfer and replica consistency maintenance, and perform dynamic node addition and removal processing, real-time update and synchronization of sharded data to generate a data synchronization and update solution;

[0016] Based on the data synchronization and update solution, use Redis as the data storage and caching mechanism, combine with the least recently used algorithm to manage cached data, maintain the fast access ability of frequently accessed data, optimize memory resource management, and generate an optimized task storage solution;

[0017] Based on the optimized task storage solution, adopt wavelength division multiplexing technology and load balancing algorithm to optimize the communication between data centers. By allocating multiple wavelengths to corresponding data streams, perform parallel transmission of data to generate a work scheduling strategy; Based on the work scheduling strategy, adopt a sliding window algorithm to process real-time data streams, and at the same time apply a data stream processing model to process data within an event-based window, sort and filter the data, and generate a real-time data processing strategy;

[0018] Based on the real-time data processing strategy, adopt a computational fluid dynamics model and network topology analysis to simulate the flow path of energy in the pipeline. By identifying and analyzing the behavior of the energy flow, including flow distribution and potential congestion points, and optimize it to generate a data flow optimization model;

[0019] Based on the energy flow optimization model, adopt a path optimization algorithm and conflict resolution strategy to optimize the work scheduling path. By calculating the optimal path and reducing path conflicts, optimize the energy flow adjustment to generate a path optimization solution;

[0020] Based on the path optimization solution, adopt photon network technology to quickly process data, combine with the pipeline transmission characteristics, optimize the data transmission speed in the pipeline network, and generate an energy transmission solution;

[0021] Based on the above energy transmission solution, adopting the microservices architecture pattern and service orchestration strategy, the energy scheduling process is split into multiple independent and lightweight units, and through the collaborative work among the units, the energy system scheduling process is optimized.

[0022] Further, the adjustable energy scheduling module includes:

[0023] An energy load forecasting module, which is used to forecast the next-day load curve of the energy load module based on a neural network;

[0024] An energy price forecasting module, which is used to obtain the next-day forecast energy price;

[0025] A next-day real-time price optimization module, which is used to optimize the next-day real-time price of energy with the goal of minimizing the economic cost of a single energy system and the loss of user comfort;

[0026] A single energy system state acquisition module, which is used to acquire the state of the single energy system in the next time period;

[0027] An adjustable energy generation device optimization module, which is used to optimize the adjustable energy generation device with the goal of minimizing the cost of a single energy system.

[0028] Further, the specific working steps of the adjustable energy scheduling module are as follows:

[0029] B1. Forecast the next-day load curve of the energy load module based on a neural network;

[0030] B2. Obtain the next-day forecast energy price;

[0031] B3. Optimize the next-day real-time price with the goal of minimizing the economic cost of a single energy system and the loss of user comfort;

[0032] B4. Obtain the state of the single energy system in the next time period;

[0033] B5. Optimize the adjustable energy generation device with the goal of minimizing the cost of a single energy system;

[0034] B6. Periodically detect the system power within the current time period;

[0035] B7. Determine whether the current power meets the balance constraint. If the judgment result is no, execute step B7; if the judgment result is yes, execute step B9;

[0036] B8. Control the energy storage system to charge and discharge with the goal of minimizing the power imbalance;

[0037] B9. Determine whether the current energy storage system exceeds the maximum power. If the judgment result is negative, the adjustable energy generation device intervenes and returns to step B8. If the judgment result is positive, execute step B10;

[0038] B10. Determine whether the total optimization cycle has ended. If the judgment result is negative, return to step B4. If the judgment result is positive, the adjustable energy scheduling ends.

[0039] Further, the non-adjustable energy scheduling module includes:

[0040] A multi-energy processing module for performing energy conversion on multiple types of the energy;

[0041] A carbon emission prediction module for predicting the carbon emission content of the energy system based on the past carbon emission content. The carbon emission prediction module includes a data processing unit, a carbon emission data collection unit, a data storage unit, a data selection unit, a learning unit, a result output unit, a result judgment unit, a data comparison unit, and a result prediction unit. The data processing unit is used to receive and process carbon emission data, receive the data selected by the carbon emission data selection unit, and process the carbon emission data. The carbon emission data processing unit schedules the processed carbon emission work to the carbon emission result prediction unit. The carbon emission data collection unit is used to collect the real-time data of the carbon emissions of the energy system. The data storage unit is used to store the real-time data of the carbon emissions of the energy system collected by the carbon emission data collection unit. The data selection unit is used to select the real-time data of the carbon emissions of the energy system stored in the data storage unit. The learning unit continuously adjusts the accuracy of the result prediction unit through continuous learning. The result output unit outputs the real-time data and predicted data of the carbon emissions of the energy system. The result judgment unit is used to judge the accuracy of the predicted carbon emission data in the result prediction unit. The data comparison unit is used to compare the real-time carbon emission data of the energy system with the predicted carbon emission data. The result prediction unit predicts the carbon emission data in the next working process based on the previous process data of the carbon emissions of the energy system;

[0042] A carbon emission detection module for capturing and detecting the carbon emission content of the energy system;

[0043] An energy switching module for manually inputting the energy switching node value and automatically switching the energy type when the load generated by the energy reaches the switching node.

[0044] Further, it is characterized in that the specific working steps of the carbon emission prediction module are:

[0045] Capture CO2 to generate a fixed power consumption δ of the electric energy cap , this value is related to the amount of captured CO2, so:

[0046] Q cap = P cap ·Δt / δ cap

[0047] Where Q cap represents the CO2 capture amount; δ cap represents the power consumption relationship, representing the power consumed to capture a unit of CO2. The reuse of the captured CO2 is achieved by electrolyzing water, and the power of electrolyzing water varies with the CH4 production process. The power consumption of electrolyzing water is So we get:

[0048]

[0049] After simplification, the relationship between the amount of natural gas produced by electrolyzing water and the power of electrolyzing water is:

[0050]

[0051] In the formula: Q P2G is the amount of natural gas produced by electrolyzing water; H gas is the calorific value of natural gas combustion. Rearranging the above formula gives:

[0052]

[0053] P P2G is the total power consumed by electrolyzing water; η P2G is the gas production efficiency of electrolyzing water; λ P2G is the proportion of the CH4 gas production power of electrolyzing water to the total power. Here, P P2G needs to satisfy the following formula:

[0054]

[0055] Among them, the left and right ends of the above formula respectively represent the minimum and maximum powers for the safe operation of electrolyzing water. In addition, according to the law of conservation of mass, the amount of CH4 generated should not exceed the actual CO2 capture amount. So finally, the actual amount of CH4 generated and the CO2 capture amount are as follows:

[0056]

[0057] Furthermore, S1, realizing distributed energy setting through a single energy system;

[0058] S11, generating adjustable energy through an adjustable energy generating device;

[0059] S12, generating non-adjustable energy through a non-adjustable energy generating device;

[0060] S13, achieve reasonable scheduling of adjustable energy on both the supply and demand sides through the adjustable energy scheduling module;

[0061] S14, schedule the optimal dispatching of non-adjustable energy within a single energy system through the non-adjustable energy scheduling module;

[0062] S15, store non-adjustable energy generated by non-adjustable energy generating equipment and adjustable energy generated by adjustable energy generating equipment through the energy storage system;

[0063] S16, receive the energy sent by the energy storage system through the energy load module and use it;

[0064] S2, schedule the collaborative work between multiple single energy systems through the collaborative work scheduling module.

[0065] Compared with the prior art, an intelligent analysis and regulation system for electronic packaging supervision provided by the present invention effectively reduces the operating cost of a single energy system and realizes the economic operation of the single energy system by reasonably scheduling the adjustable energy on both the supply and demand sides within the single energy system. In the collaborative environment of multiple single energy systems, each single energy system can complete the optimization decision without knowing the internal information of other single energy systems, effectively improving the overall economy of the system and reducing pollutant emissions. At the same time, through continuous learning and adjustment of the learning unit, continuous adjustment of the prediction data can be achieved, and when the load generated by the energy reaches the corresponding switching node, the energy type is automatically switched through the energy switching module, thereby realizing the scheduling of non-adjustable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0067] Figure 1 It is a schematic diagram of the overall structure provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0068] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the following will further introduce the present invention in detail with reference to the drawings.

[0069] Please refer to Figure 1 , an intelligent analysis and regulation system for electronic packaging supervision, including multiple single energy systems and a collaborative work scheduling module:

[0070] Multiple single energy systems are used to implement distributed energy settings, and multiple single energy systems all include:

[0071] Energy load module, which is used to receive and utilize the energy sent by the energy storage system;

[0072] Energy storage system, which is used to store the non-adjustable energy generated by the non-adjustable energy generating device and the adjustable energy generated by the adjustable energy generating device, and send the stored energy to the energy load module;

[0073] Non-adjustable energy generating device, which is used to generate non-adjustable energy, and the non-adjustable energy includes but is not limited to light energy, wind energy and wave energy;

[0074] Adjustable energy generating device, which is used to generate adjustable energy, and the adjustable energy includes but is not limited to diesel, fuel cell and micro gas turbine;

[0075] Non-adjustable energy scheduling module, which is used to schedule the optimal scheduling of non-adjustable energy in the single energy system;

[0076] Adjustable energy scheduling module, which is used to achieve the reasonable scheduling of adjustable energy on both the supply and demand sides;

[0077] The collaborative work scheduling module is used to schedule the collaborative work of multiple single energy systems.

[0078] The specific working steps of the collaborative work scheduling module are as follows:

[0079] The specific working steps of the collaborative work scheduling module are as follows:

[0080] Adopt the consistent hashing algorithm to evenly shard multiple single energy systems, and at the same time combine the Cap theorem to implement failover and replica consistency maintenance, perform dynamic node addition and removal processing, real-time update and synchronization of sharded data, and generate a data synchronization and update scheme;

[0081] Based on the data synchronization and update scheme, use Redis as the data storage and caching mechanism, combine the least recently used algorithm to manage the cached data, maintain the fast access ability of frequently accessed data, optimize the memory resource management, and generate an optimized task storage scheme;

[0082] Based on the optimized task storage scheme, adopt the wavelength division multiplexing technology and the load balancing algorithm to optimize the communication between data centers. By allocating multiple wavelengths to the corresponding data streams, parallel transmission of data is carried out to generate a work scheduling strategy; Based on the work scheduling strategy, use the sliding window algorithm to process real-time data streams, and at the same time apply the data stream processing model to process data within the event-based window, sort and filter the data, and generate a real-time data processing strategy;

[0083] Based on the real-time data processing strategy, a computational fluid dynamics model and network topology analysis are used to simulate the flow path of energy in pipelines. By identifying and analyzing the behavior of the energy flow, including flow distribution and potential congestion points, and optimizing them, a data flow optimization model is generated;

[0084] Based on the energy flow optimization model, a path optimization algorithm and conflict resolution strategy are used to optimize the work scheduling path. By calculating the optimal path and reducing path conflicts, the energy flow is optimized and adjusted to generate a path optimization solution;

[0085] Based on the path optimization solution, photon network technology is used to quickly process data. Combining with the pipeline transmission characteristics, the transmission speed of data in the pipeline network is optimized to generate an energy transmission solution;

[0086] Based on the energy transmission solution, a microservices architecture pattern and service orchestration strategy are adopted to split the energy scheduling process into multiple independent and lightweight units. Through the collaborative work of the units, the energy system scheduling process is optimized.

[0087] Such a setting enables each single energy system to complete the optimization decision without knowing the internal information of other single energy systems in the collaborative environment of the multi-single energy system, effectively improving the overall economy of the system and reducing pollutant emissions.

[0088] The adjustable energy scheduling module includes:

[0089] An energy load prediction module, which is used to predict the next-day load curve of the energy load module based on a neural network;

[0090] An energy price prediction module, which is used to obtain the predicted energy price for the next day;

[0091] A next-day real-time price optimization module, which is used to optimize the real-time price of the next-day energy with the goal of minimizing the economic cost of the single energy system and the loss of user comfort;

[0092] A single energy system status acquisition module, which is used to acquire the status of the single energy system in the next time period;

[0093] An adjustable energy generation equipment optimization module, which is used to optimize the adjustable energy generation equipment with the goal of minimizing the cost of the single energy system.

[0094] The specific working steps of the adjustable energy scheduling module are:

[0095] B1. Predict the next-day load curve of the energy load module based on a neural network;

[0096] B2. Obtain the predicted energy price for the next day;

[0097] B3. Optimize the real-time price for the next day with the goal of minimizing the economic cost of the single-energy system and the loss of user comfort;

[0098] B4. Obtain the state of the single-energy system for the next time period;

[0099] B5. Optimize the adjustable energy generation equipment with the goal of minimizing the cost of the single-energy system;

[0100] B6. Periodically detect the system power within the current time period;

[0101] B7. Determine whether the current power meets the balance constraint. If the judgment result is negative, execute step B7; if the judgment result is positive, execute step B9;

[0102] B8. Control the energy storage system to charge and discharge with the goal of minimizing the power imbalance;

[0103] B9. Determine whether the current energy storage system exceeds the maximum power. If the judgment result is negative, the adjustable energy generation equipment intervenes and return to step B8; if the judgment result is positive, execute step B10;

[0104] B10. Determine whether the total optimization period is over. If the judgment result is negative, return to step B4; if the judgment result is positive, the adjustable energy scheduling ends.

[0105] Such a setting effectively reduces the operating cost of the single-energy system and realizes the economic operation of the single-energy system through the reasonable scheduling of the adjustable energy on both the supply and demand sides within the single-energy system; at the same time, through collaborative optimization, it greatly alleviates the impact of multi-source uncertainties such as renewable energy output and user load on the system power balance and realizes the safe operation of the single-energy system.

[0106] The non-adjustable energy scheduling module includes:

[0107] A multi-energy processing module for performing energy conversion on multiple types of energy;

[0108] The carbon emission prediction module is used to predict the carbon emission content of the energy system based on the previous carbon emission content. The carbon emission prediction module includes a data processing unit, a carbon emission data collection unit, a data storage unit, a data selection unit, a learning unit, a result output unit, a result judgment unit, a data comparison unit, and a result prediction unit. The data processing unit is used to receive and process carbon emission data, receive the data selected by the carbon emission data selection unit, and process the carbon emission data. The carbon emission data processing unit schedules the completed carbon emission work to the carbon emission result prediction unit. The carbon emission data collection unit is used to collect the real-time data of the carbon emissions of the energy system. The data storage unit is used to store the real-time data of the carbon emissions of the energy system collected by the carbon emission data collection unit. The data selection unit is used to select the real-time data of the carbon emissions of the energy system stored in the data storage unit. The learning unit continuously adjusts the accuracy of the result prediction unit through continuous learning. The result output unit outputs the real-time data and predicted data of the carbon emissions of the energy system. The result judgment unit is used to judge the accuracy of the predicted carbon emission data in the result prediction unit. The data comparison unit is used to compare the real-time carbon emission data of the energy system with the predicted carbon emission data. The result prediction unit predicts the carbon emission data in the next working process based on the previous process data of the carbon emissions of the energy system;

[0109] The carbon emission detection module is used to capture and detect the carbon emission content of the energy system;

[0110] The energy switching module is used to manually input the energy switching node value and automatically switch the energy type when the load generated by the energy reaches the switching node.

[0111] The real-time data of the carbon emissions of the energy system is collected through the carbon emission data collection unit. The real-time data of the carbon emissions of the energy system collected by the carbon emission data collection unit is received and stored through the data storage unit. The data in the data storage unit is randomly selected through the data selection unit. The carbon emission data in the next working process of the energy system is predicted through the result prediction. At the same time, the prediction result is transmitted to the result output unit. The data comparison unit compares the real-time carbon emission data of the energy system with the predicted carbon emission data. The result judgment unit judges the received predicted data and real-time data, so as to judge the prediction accuracy of the result prediction unit. The data output unit outputs the predicted data, real-time data, and judgment result at the same time. The staff can judge whether data adjustment is needed according to the accuracy of the prediction result. The continuous adjustment of the prediction data can be realized through the continuous learning and adjustment of the learning unit. When the load generated by the energy reaches the corresponding switching node, the energy type is automatically switched through the energy switching module, so as to realize the scheduling of non-adjustable energy.

[0112] It is characterized in that the specific working steps of the carbon emission prediction module are as follows:

[0113] Power consumption δ for generating fixed electric energy by CO₂ capture cap , this value is related to the amount of CO₂ captured, thus:

[0114] Q cap = P cap ·Δt / δ cap

[0115] Where Q cap represents the amount of CO₂ captured; δ cap represents the power consumption relationship, representing the power consumed for capturing unit CO₂, and realizes the reuse of the captured CO₂ through electrolyzing water. The power of electrolyzing water varies with the CH₄ production process. The power consumption of electrolyzing water is Thus, we get:

[0116]

[0117] After simplification, the relationship between the amount of natural gas produced by electrolyzing water and the power of electrolyzing water is:

[0118]

[0119] In the formula: Q P2G is the amount of natural gas produced by electrolyzing water; H gas is the calorific value of natural gas combustion. Rearranging the above formula, we can get:

[0120]

[0121] P P2G is the total power consumed by electrolyzing water; η P2G is the gas production efficiency of electrolyzing water; λ P2G is the proportion of the power of CH₄ gas produced by electrolyzing water to the total power. Here, P P2G needs to satisfy the following formula:

[0122]

[0123] Among them, the left and right ends of the above formula respectively represent the minimum and maximum powers for the safe operation of electrolyzing water. In addition, according to the law of conservation of mass, the amount of CH₄ generated should not exceed the actual amount of CO₂ captured. Therefore, the actual amount of CH₄ generated and the amount of CO₂ captured are finally obtained as follows:

[0124]

[0125] The specific working steps of the intelligent analysis and regulation system for electronic packaging supervision are as follows:

[0126] S1, realizing distributed energy setting through a single energy system;

[0127] S11, generating adjustable energy through an adjustable energy generating device;

[0128] S12, generating non-adjustable energy through a non-adjustable energy generating device;

[0129] S13, achieving reasonable scheduling of adjustable energy on both the supply and demand sides through an adjustable energy scheduling module;

[0130] S14, scheduling the optimal scheduling of non-adjustable energy within a single energy system through a non-adjustable energy scheduling module;

[0131] S15, storing the non-adjustable energy generated by the non-adjustable energy generating device and the adjustable energy generated by the adjustable energy generating device through an energy storage system;

[0132] S16, receiving the energy emitted by the energy storage system through an energy load module and using it;

[0133] S2, scheduling the collaborative work between multiple single energy systems through a collaborative work scheduling module.

[0134] Only some exemplary embodiments of the present invention have been described above by way of illustration. Undoubtedly, for those of ordinary skill in the art, the described embodiments can be modified in various different ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. An intelligent analysis and adjustment system for electronic packaging supervision, characterized in that, It includes multiple single - energy systems and a collaborative working scheduling module: The multiple single - energy systems are used to implement distributed energy settings, and each of the multiple single - energy systems includes: An energy load module, which is used to receive and use the energy sent by the energy storage system; An energy storage system, which is used to store the non - adjustable energy generated by the non - adjustable energy generating equipment and the adjustable energy generated by the adjustable energy generating equipment, and send the stored energy to the energy load module; Non - adjustable energy generating equipment, which is used to generate non - adjustable energy, and the non - adjustable energy includes but is not limited to light energy, wind energy, and wave energy; Adjustable energy generating equipment, which is used to generate adjustable energy, and the adjustable energy includes but is not limited to diesel, fuel cells, and micro gas turbines; A non - adjustable energy scheduling module, which is used to schedule the optimal scheduling of non - adjustable energy within the single - energy system; An adjustable energy scheduling module, which is used to achieve the reasonable scheduling of adjustable energy on both the supply and demand sides; The collaborative working scheduling module is used to schedule the collaborative work of the multiple single - energy systems.

2. An intelligent analysis and adjustment system for electronic packaging supervision according to claim 1, characterized in that, The specific working steps of the collaborative working scheduling module are as follows: Adopt the consistent hashing algorithm to evenly slice the multiple single - energy systems. At the same time, combine the Cap theorem to implement failover and replica consistency maintenance, perform dynamic node addition and removal processing, real - time update and synchronization of sharded data, and generate a data synchronization and update plan; Based on the data synchronization and update plan, use Redis as the data storage and caching mechanism, combine the least recently used algorithm to manage cached data, maintain the fast access ability of frequently accessed data, optimize memory resource management, and generate an optimized task storage plan; Based on the optimized task storage plan, adopt wavelength - division multiplexing technology and a load - balancing algorithm to optimize the communication between data centers. By allocating multiple wavelengths to the corresponding data streams, perform parallel data transmission, and generate a work scheduling strategy; Based on the work scheduling strategy, use the sliding window algorithm to process real - time data streams. At the same time, apply the data stream processing model to process data within an event - based window, sort and filter the data, and generate a real - time data processing strategy; Based on the real - time data processing strategy, adopt the computational fluid dynamics model and network topology analysis to simulate the flow path of energy in the pipeline. By identifying and analyzing the behavior of the energy flow, including flow distribution and potential congestion points, and optimize them, generate a data flow optimization model; Based on the energy flow optimization model, adopt a path optimization algorithm and a conflict resolution strategy to optimize the work scheduling path. By calculating the optimal path and reducing path conflicts, optimize the adjustment of energy flow, and generate a path optimization solution; Based on the path optimization solution, adopt photon network technology to quickly process data. Combine the pipeline transmission characteristics to optimize the data transmission speed in the pipeline network, and generate an energy transmission plan; Based on the described energy transmission solution, adopting the microservices architecture mode and service orchestration strategy, the energy scheduling process is split into multiple independent and lightweight units. Through the collaborative work among the units, the energy system scheduling process is optimized.

3. An intelligent analysis and adjustment system for electronic packaging supervision according to claim 2, characterized in that The adjustable energy scheduling module includes: An energy load forecasting module, which is used to forecast the next-day load curve of the energy load module based on a neural network; An energy price forecasting module, which is used to obtain the next-day forecast energy price; A next-day real-time price optimization module, which is used to optimize the next-day real-time price of energy with the goal of minimizing the economic cost of a single energy system and the loss of user comfort; A single energy system state acquisition module, which is used to acquire the state of the single energy system in the next time period; An adjustable energy generation device optimization module, which is used to optimize the adjustable energy generation device with the goal of minimizing the cost of a single energy system; 4. An intelligent analysis and adjustment system for electronic packaging supervision according to claim 3, characterized in that, The specific working steps of the adjustable energy scheduling module are as follows: B1, Forecast the next-day load curve of the energy load module based on a neural network; B2, Obtain the next-day forecast energy price; B3, Optimize the next-day real-time price with the goal of minimizing the economic cost of a single energy system and the loss of user comfort; B4, Acquire the state of the single energy system in the next time period; B5, Optimize the adjustable energy generation device with the goal of minimizing the cost of a single energy system; B6, Periodically detect the system power within the current time period; B7, Determine whether the current power meets the balance constraint. If the judgment result is no, then execute step B7. If the judgment result is yes, then execute step B9; B8, Control the energy storage system to charge and discharge with the goal of minimizing the power imbalance; B9, Determine whether the current energy storage system exceeds the maximum power. If the judgment result is no, then the adjustable energy generation device intervenes and return to step B8. If the judgment result is yes, then execute step B10; B10, Determine whether the total optimization period has ended. If the judgment result is no, then return to step B4. If the judgment result is yes, then the adjustable energy scheduling ends.

5. An intelligent analysis and adjustment system for electronic packaging supervision according to claim 4, characterized in that The non-adjustable energy scheduling module includes: A multi-energy processing module, which is used to perform energy conversion on multiple types of the energy; A carbon emission prediction module, which is used to predict the carbon emission content of the energy system according to the previous carbon emission content. The carbon emission prediction module includes a data processing unit, a carbon emission data collection unit, a data storage unit, a data selection unit, a learning unit, a result output unit, a result judgment unit, a data comparison unit, and a result prediction unit. The data processing unit is used to receive and process carbon emission data, receive the data selected by the carbon emission data selection unit, and process the carbon emission data. The carbon emission data processing unit schedules the completed carbon emission work to the carbon emission result prediction unit. The carbon emission data collection unit is used to collect the real-time data of the carbon emission of the energy system. The data storage unit is used to store the real-time data of the carbon emission of the energy system collected by the carbon emission data collection unit. The data selection unit is used to select the real-time data of the carbon emission of the energy system stored in the data storage unit. The learning unit continuously adjusts the accuracy of the result prediction unit through continuous learning. The result output unit outputs the real-time data and predicted data of the carbon emission of the energy system. The result judgment unit is used to judge the accuracy of the predicted carbon emission data in the result prediction unit. The data comparison unit is used to compare the real-time carbon emission data of the energy system with the predicted carbon emission data. The result prediction unit predicts the carbon emission data in the next working process based on the previous process data of the carbon emission of the energy system; A carbon emission detection module, which is used to capture and detect the carbon emission content of the energy system; An energy switching module, which is used to manually input the energy switching node value and automatically switch the energy type when the load generated by the energy reaches the switching node.

6. The intelligent analysis and adjustment system for electronic packaging supervision according to claim 5, characterized in that The specific working steps of the carbon emission prediction module are as follows: The specific working steps of the collaborative work scheduling module are as follows: Adopt the consistent hashing algorithm to evenly slice multiple single energy systems, and at the same time combine the Cap theorem to implement fault transfer and replica consistency maintenance, perform dynamic node addition and removal processing, real-time update and synchronization of sharded data, and generate a data synchronization and update plan; Based on the data synchronization and update plan, use Redis as the data storage and caching mechanism, combine the least recently used algorithm to manage the cached data, maintain the fast access ability of frequently accessed data, optimize the memory resource management, and generate an optimized task storage plan; Based on the optimized task storage plan, adopt the wavelength division multiplexing technology and the load balancing algorithm to optimize the communication between data centers, allocate multiple wavelengths to the corresponding data streams for parallel transmission of data, and generate a work scheduling strategy; Based on the work scheduling strategy, adopt the sliding window algorithm to process the real-time data stream, and at the same time apply the data stream processing model to process the data within the event-based window, sort and filter the data, and generate a real-time data processing strategy; Based on the real-time data processing strategy, adopt the computational fluid dynamics model and network topology analysis to simulate the flow path of energy in the pipeline, identify and analyze the behavior of the energy flow, including flow distribution and potential congestion points, and optimize it to generate a data flow optimization model; Based on the above energy flow optimization model, a path optimization algorithm and a conflict resolution strategy are adopted to optimize the work scheduling path. By calculating the optimal path and reducing path conflicts, the energy flow is optimized and adjusted to generate a path optimization solution; Based on the above path optimization solution, photon network technology is adopted to quickly process data. Combining with the pipeline transmission characteristics, the transmission speed of data in the pipeline network is optimized to generate an energy transmission solution; Based on the above energy transmission solution, a microservice architecture mode and a service orchestration strategy are adopted to split the energy scheduling process into multiple independent and lightweight units. Through the collaborative work between units, the energy system scheduling process is optimized.

7. An intelligent analysis and adjustment system for electronic packaging supervision according to claim 6, characterized in that, The specific working steps of the intelligent analysis and regulation system for electronic packaging supervision are as follows: S1. Implement distributed energy setting through a single energy system; S11. Generate adjustable energy through an adjustable energy generating device; S12. Generate non-adjustable energy through a non-adjustable energy generating device; S13. Achieve reasonable scheduling of adjustable energy on both the supply and demand sides through an adjustable energy scheduling module; S14. Optimize the scheduling of non-adjustable energy within the single energy system through a non-adjustable energy scheduling module; S15. Store the non-adjustable energy generated by the non-adjustable energy generating device and the adjustable energy generated by the adjustable energy generating device through an energy storage system; S16. Receive the energy emitted by the energy storage system through an energy load module and use it; S2. Schedule the collaborative work between multiple single energy systems through a collaborative work scheduling module.

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