Multi-load energy consumption intelligent distribution and control method and system based on distribution box

Abstract

The invention relates to the technical field of energy consumption intelligent distribution and control, in particular to a multi-load energy consumption intelligent distribution and control method and system based on a distribution box, and the method comprises the steps: obtaining initial operation parameter information of multi-load equipment connected with the distribution box, extracting energy consumption fluctuation characteristics and load response characteristics, and generating an energy consumption load incidence matrix; optimizing the energy consumption distribution proportion of the multi-load equipment based on the energy consumption sensitive index to obtain an energy consumption distribution control configuration table, constructing an energy consumption intelligent distribution simulation model based on the energy consumption distribution control configuration table, and constructing an energy consumption parameter correction model based on the design power supply parameters of the distribution box and the rated parameters of the multi-load equipment, and generating a dynamic energy consumption distribution adjustment scheme based on the energy consumption parameter correction coefficient and the energy consumption distribution control configuration table, and executing energy consumption intelligent distribution and control operation of the multi-load equipment based on the dynamic energy consumption distribution adjustment scheme. According to the invention, energy consumption distribution can be dynamically adjusted according to actual conditions through an energy consumption distribution adjustment scheme, and resource waste is avoided.

Description

Technical Field

[0001] This invention relates to the field of intelligent energy distribution and control technology, specifically to a multi-load intelligent energy distribution and control method and system based on a distribution box. Background Technology

[0002] Traditional energy consumption allocation is often based on fixed parameters or empirical formulas of equipment, ignoring the real-time operating status of the equipment. Even if the equipment load changes, the energy consumption allocation scheme is difficult to adjust flexibly, resulting in low energy efficiency. Traditional methods use fixed allocation ratios to distribute energy consumption, which cannot deeply analyze the characteristics of energy consumption fluctuations and the correlation between load. Traditional methods often use simplified optimization algorithms or empirical rules to adjust the energy consumption allocation ratio of equipment, but the optimization effect of these methods is usually limited. Traditional methods do not take into account the parameter deviations or errors that may occur during equipment operation, and often calculate energy consumption based on the rated parameters of the equipment, lacking a correction mechanism. Traditional energy consumption allocation methods rely on manual adjustment, allocating according to equipment type, load characteristics and empirical data, lacking automated and intelligent control means. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a method and system for intelligent distribution and control of multi-load energy consumption based on a distribution box.

[0004] The technical solution adopted to solve the above-mentioned technical problems is: a multi-load energy consumption intelligent distribution and control method based on a distribution box, including:

[0005] Obtain the initial operating parameter information of the multi-load devices connected to the distribution box, and perform energy consumption characteristic analysis on the multi-load devices based on the initial operating parameter information to obtain the initial energy consumption allocation priority area;

[0006] Collect real-time operating status data of the multi-load devices, perform time-frequency domain analysis on the real-time operating status data, extract energy consumption fluctuation characteristics and load response characteristics, and generate an energy consumption load correlation matrix.

[0007] The energy consumption sensitivity index of the multi-load device is calculated based on the initial energy consumption allocation priority region and energy consumption load correlation matrix. The energy consumption allocation ratio of the multi-load device is optimized based on the energy consumption sensitivity index to obtain the energy consumption allocation control configuration table.

[0008] An intelligent energy distribution simulation model is constructed based on the energy distribution control configuration table. The real-time energy distribution under different load combinations and operating conditions is simulated based on the intelligent energy distribution simulation model. An energy consumption parameter correction model is constructed based on the design power supply parameters of the distribution box and the rated parameters of the multi-load equipment.

[0009] Based on the energy consumption parameter correction model and the actual operating environment, deviation analysis is performed on the simulated energy consumption allocation data to obtain energy consumption parameter correction coefficients. Based on the energy consumption parameter correction coefficients and the energy consumption allocation control configuration table, a dynamic energy consumption allocation adjustment scheme is generated. Based on the dynamic energy consumption allocation adjustment scheme, the intelligent energy consumption allocation and control operation of the multi-load device is executed.

[0010] Preferably, the initial operating parameter information includes the rated power, operating voltage range, operating current range, and initial operating mode setting of the multi-load device; the real-time operating status data includes the actual power consumption, operating voltage fluctuation value, operating current fluctuation value, and operating time of the multi-load device; and the energy consumption allocation control configuration table includes energy consumption allocation priority, energy consumption allocation ratio, and device operation protection threshold.

[0011] Preferably, energy consumption characteristic analysis is performed on the multi-load device based on the initial operating parameter information to obtain the initial energy consumption allocation priority region, including:

[0012] Based on the rated power, operating voltage range, operating current range and initial operating mode of the multi-load device, an energy consumption characteristic evaluation index is constructed, wherein the energy consumption characteristic evaluation index includes a basic energy consumption benchmark value, voltage sensitivity coefficient and current fluctuation tolerance.

[0013] The weight coefficients of the energy consumption characteristic evaluation index are determined based on the analytic hierarchy process, and a comprehensive energy consumption characteristic score is calculated based on the weight coefficients to obtain the comprehensive energy consumption characteristic score.

[0014] Based on the comprehensive energy consumption characteristic score, the multi-load devices are divided into high-priority areas, medium-priority areas, and low-priority areas.

[0015] Preferably, time-frequency domain analysis is performed on the real-time operating status data to extract energy consumption fluctuation characteristics and load response features, generating an energy consumption-load correlation matrix, including:

[0016] The real-time working status data is decomposed in the time-frequency domain based on wavelet transform to obtain energy consumption fluctuation components in different frequency bands, and the energy consumption fluctuation characteristics are determined based on the energy consumption fluctuation components.

[0017] The response amplitude and phase difference of the multi-load device at a specific frequency are analyzed based on Fourier transform to obtain the load response characteristics;

[0018] Based on the energy consumption fluctuation characteristics and load response features, an energy consumption load correlation matrix is ​​constructed with load devices as rows and energy consumption influencing factors as columns. The elements of the energy consumption load correlation matrix represent the correlation strength coefficient between the corresponding load devices and energy consumption influencing factors.

[0019] Preferably, the energy consumption sensitivity index of the multi-load device is calculated based on the initial energy consumption allocation priority region and the energy consumption load correlation matrix, including:

[0020] Based on the initial energy consumption allocation priority region, the multi-load device is assigned different regional weight coefficients, wherein the high priority region has the highest weight coefficient, the medium priority region has the next highest weight coefficient, and the low priority region has the lowest weight coefficient.

[0021] Obtain the correlation strength coefficient corresponding to each load device in the energy consumption load correlation matrix, and multiply the region weight coefficient and the correlation strength coefficient to obtain the initial sensitivity index;

[0022] The initial sensitivity index is weighted and corrected based on the energy consumption fluctuation characteristics and load response characteristics to obtain the energy consumption sensitivity index. The energy consumption sensitivity index is higher for load devices with larger fluctuation amplitude and smaller response delay.

[0023] Preferably, the energy consumption allocation ratio of the multi-load devices is optimized based on the energy consumption sensitivity index to obtain an energy consumption allocation control configuration table, including:

[0024] An energy consumption allocation ratio optimization objective function is constructed based on the energy consumption sensitivity index and the rated power of the multi-load device. The constraints of the energy consumption allocation ratio optimization objective function include the maximum power supply capacity of the distribution box, the minimum operating power threshold of the multi-load device, and the upper limit of the load response delay.

[0025] The objective function for optimizing the energy consumption allocation ratio is solved based on the particle swarm optimization algorithm to obtain the globally optimal energy consumption allocation ratio.

[0026] The energy consumption allocation ratio is matched and verified with the priority area and energy consumption fluctuation characteristics of the multi-load devices to ensure that the energy consumption allocation ratio meets the priority power supply requirements of the highly sensitive devices. Finally, an energy consumption allocation control configuration table is generated, which includes energy consumption allocation priority, energy consumption allocation ratio and device operation protection threshold.

[0027] Preferably, the intelligent energy consumption allocation simulation model includes a load device layer, an energy consumption allocation strategy layer, and a simulation output layer. The load device layer is used to simulate the dynamic operating state of multiple load devices. The energy consumption allocation strategy layer realizes dynamic energy consumption allocation for each load device based on the energy consumption allocation priority and energy consumption allocation ratio. The simulation output layer is used to display the energy consumption allocation curve, total energy consumption trend, and operating status parameters of each device under different load combinations.

[0028] Preferably, an energy consumption parameter correction model is constructed based on the design power supply parameters of the distribution box and the rated parameters of the multi-load equipment, including:

[0029] The design power supply parameters of the distribution box and the rated parameters of the multi-load equipment are set as model input variables to obtain a complete set of model input variables;

[0030] The mathematical relationship between input variables and theoretical energy consumption is constructed based on the least squares method to obtain a parameter mapping relationship based on the theoretical matching value of the design power supply parameters and rated parameters;

[0031] The model is trained and optimized based on the parameter mapping relationship and parameter deviation samples in historical operating data to obtain the energy consumption parameter correction model. The correction dimensions of the energy consumption parameter correction model include voltage fluctuation correction coefficient, current stability compensation coefficient and power factor correction factor.

[0032] Preferably, a deviation analysis is performed on the simulated energy consumption allocation data obtained from the simulation based on the energy consumption parameter correction model and the actual operating environment to obtain the energy consumption parameter correction coefficient, including:

[0033] Collect actual energy consumption data of the multi-load devices in the actual operating environment;

[0034] The difference between the actual energy consumption data and the simulated energy consumption allocation data is calculated to obtain the initial energy consumption deviation value;

[0035] Based on the energy consumption parameter correction model, the initial energy consumption deviation value is decomposed in multiple dimensions to obtain voltage fluctuation deviation component, current stability deviation component and power factor deviation component.

[0036] The correction coefficients corresponding to the voltage fluctuation deviation component, current stability deviation component, and power factor deviation component are calculated, and the correction coefficients are weighted and fused to obtain the energy consumption parameter correction coefficients.

[0037] The technical solution adopted to solve the above-mentioned technical problems is: a multi-load energy consumption intelligent distribution and control system based on a distribution box, which is applicable to the aforementioned multi-load energy consumption intelligent distribution and control method based on a distribution box, including:

[0038] The priority allocation unit is used to obtain the initial operating parameter information of the multi-load devices connected to the distribution box, and to perform energy consumption characteristic analysis on the multi-load devices based on the initial operating parameter information to obtain the initial energy consumption allocation priority area.

[0039] The energy consumption correlation unit is used to collect real-time operating status data of the multi-load devices, perform time-frequency domain analysis on the real-time operating status data, extract energy consumption fluctuation characteristics and load response characteristics, and generate an energy consumption load correlation matrix.

[0040] The allocation configuration unit is used to calculate the energy consumption sensitivity index of the multi-load device based on the initial energy consumption allocation priority area and the energy consumption load correlation matrix, and optimize the energy consumption allocation ratio of the multi-load device based on the energy consumption sensitivity index to obtain the energy consumption allocation control configuration table.

[0041] The simulation distribution unit is used to construct an intelligent energy distribution simulation model based on the energy distribution control configuration table, simulate the real-time energy distribution under different load combinations and operating conditions based on the intelligent energy distribution simulation model, and construct an energy consumption parameter correction model based on the design power supply parameters of the distribution box and the rated parameters of the multi-load equipment.

[0042] The energy consumption allocation unit is used to perform deviation analysis on the simulated energy consumption allocation data obtained from the simulation based on the energy consumption parameter correction model and the actual operating environment, so as to obtain the energy consumption parameter correction coefficient, generate a dynamic energy consumption allocation adjustment scheme based on the energy consumption parameter correction coefficient and the energy consumption allocation control configuration table, and execute the intelligent energy consumption allocation and control operation of the multi-load device based on the dynamic energy consumption allocation adjustment scheme.

[0043] The beneficial effects of the present invention are as follows: (1) The present invention calculates the energy consumption sensitivity index and optimizes the energy consumption allocation ratio of the equipment based on this, which can more accurately adjust the energy consumption allocation of each load, effectively improve the energy consumption utilization efficiency, reduce ineffective energy consumption, and optimize the overall operating efficiency of the system; (2) The present invention constructs an intelligent energy consumption allocation simulation model, which can simulate the energy consumption allocation in real time under different load combinations and operating conditions, thereby realizing real-time dynamic adjustment, so that the system can optimize energy efficiency in a timely manner according to actual load changes; (3) The present invention constructs an energy consumption parameter correction model by combining the power supply parameters of the distribution box design with the rated parameters of the equipment, which can better reflect the performance of the equipment in the actual environment. This correction process can significantly improve the accuracy of energy consumption allocation, reduce errors, and ensure more stable and efficient energy consumption management; (4) The present invention uses a dynamic energy consumption allocation adjustment scheme for distributed control, which can automatically adjust the energy consumption allocation ratio of each load according to real-time energy consumption data and equipment operating status without manual intervention, realizing full automation and intelligence of energy consumption control, effectively reducing manual operation costs and response delays. Attached Figure Description

[0044] Figure 1 This is a schematic diagram of the overall method steps in one embodiment of the present invention;

[0045] Figure 2This is a schematic diagram of the overall system flow in one embodiment of the present invention;

[0046] Reference numerals in the attached figures: 1. Priority allocation unit; 2. Energy consumption association unit; 3. Allocation configuration unit; 4. Simulation allocation unit; 5. Energy consumption allocation unit. Detailed Implementation

[0047] Example 1, as Figure 1 As shown, the present invention proposes a multi-load energy consumption intelligent allocation and control method based on a distribution box, comprising:

[0048] S1. Obtain the initial operating parameter information of the multi-load devices connected to the distribution box, and perform energy consumption characteristic analysis on the multi-load devices based on the initial operating parameter information to obtain the initial energy consumption allocation priority area.

[0049] S2. Collect real-time operating status data of multi-load devices, perform time-frequency domain analysis on the real-time operating status data, extract energy consumption fluctuation characteristics and load response characteristics, and generate an energy consumption load correlation matrix.

[0050] S3. Calculate the energy consumption sensitivity index of multi-load devices based on the initial energy consumption allocation priority area and energy consumption load correlation matrix, and optimize the energy consumption allocation ratio of multi-load devices based on the energy consumption sensitivity index to obtain the energy consumption allocation control configuration table.

[0051] S4. Construct an intelligent energy distribution simulation model based on the energy distribution control configuration table, simulate the real-time energy distribution under different load combinations and operating conditions based on the intelligent energy distribution simulation model, and construct an energy consumption parameter correction model based on the design power supply parameters of the distribution box and the rated parameters of multi-load equipment.

[0052] S5. Based on the energy consumption parameter correction model and the actual operating environment, perform deviation analysis on the simulated energy consumption distribution data obtained from the simulation to obtain the energy consumption parameter correction coefficient. Based on the energy consumption parameter correction coefficient and the energy consumption distribution control configuration table, generate a dynamic energy consumption distribution adjustment scheme. Based on the dynamic energy consumption distribution adjustment scheme, execute intelligent energy consumption distribution and control operations for multi-load devices.

[0053] In this invention, the distribution box is a device in a power system used to distribute electrical energy to various loads (such as appliances, machines, etc.). The initial operating parameters of multi-load devices refer to the basic operating parameters of these devices at the beginning of operation, such as voltage, current, power, and frequency. These initial parameters help analyze the energy demand of the devices. The initial energy consumption allocation priority area refers to the ranking of energy demand by different devices during operation. Based on the functional importance or energy consumption characteristics of the devices, some devices may be assigned higher energy consumption priority to ensure they receive sufficient power during operation. Real-time operating status data refers to the dynamic data continuously collected during actual operation, such as power usage and equipment load changes. This data helps monitor the energy efficiency and operating status of the devices. Time-frequency domain analysis is a signal processing technique used to analyze the behavior of devices at different times and frequencies. By analyzing the time and frequency domain characteristics of the signal, the energy consumption fluctuation characteristics and load response characteristics of the devices can be extracted. Energy consumption fluctuation characteristics refer to how energy consumption changes over time during operation, while load response characteristics refer to the device's response to external loads. The response to load changes allows us to determine the energy consumption performance of equipment under different load conditions. Deviation analysis involves analyzing the differences between simulated and actual energy consumption data to identify the sources of error in the model and implement corrective measures. The energy consumption parameter correction coefficient, derived from deviation analysis, is used to adjust the energy consumption allocation in the simulation model to better match the actual situation. The dynamic energy consumption allocation adjustment scheme is a dynamic adjustment strategy based on the energy consumption parameter correction coefficient and the energy consumption allocation control configuration table. This scheme can adjust the energy consumption allocation ratio of each device in real time according to the actual operating status and energy efficiency of the equipment. Intelligent energy consumption allocation and control operations are a series of control operations implemented according to the dynamic energy consumption allocation adjustment scheme. Through these operations, we can ensure that the energy consumption of multiple load devices under different operating conditions is intelligently and optimally allocated and controlled, thereby improving energy utilization efficiency.

[0054] Example 2: The present invention proposes a multi-load energy consumption intelligent allocation and control method based on a distribution box. Compared with Example 1, this example also includes initial operating parameter information including the rated power, operating voltage range, operating current range and initial operating mode settings of the multi-load devices; real-time operating status data including the actual power consumption, operating voltage fluctuation value, operating current fluctuation value and running time of the multi-load devices; and an energy consumption allocation control configuration table including energy consumption allocation priority, energy consumption allocation ratio and equipment operation protection threshold.

[0055] In an optional embodiment, energy consumption characteristics of multi-load devices are analyzed based on initial operating parameter information to obtain an initial energy consumption allocation priority region, including:

[0056] A1. Based on the rated power, operating voltage range, operating current range and initial operating mode settings of multi-load devices, construct energy consumption characteristic evaluation indicators, including basic energy consumption benchmark value, voltage sensitivity coefficient and current fluctuation tolerance.

[0057] A2. Determine the weight coefficients of the energy consumption characteristic evaluation indicators based on the analytic hierarchy process, and calculate the comprehensive energy consumption characteristic score based on the weight coefficients to obtain the comprehensive energy consumption characteristic score.

[0058] A3. Based on the comprehensive energy consumption characteristic score, multi-load devices are divided into high-priority areas, medium-priority areas and low-priority areas.

[0059] It should be noted that the baseline energy consumption value refers to the energy consumption level of the equipment under standard operating conditions, usually referring to the energy consumed by the equipment in its rated operating state; the voltage sensitivity coefficient indicates the sensitivity of the equipment to voltage fluctuations, which may cause changes in the equipment's energy consumption. A higher voltage sensitivity coefficient means that the equipment's energy consumption is more sensitive to voltage changes; the current fluctuation tolerance indicates the range of current changes that the equipment can withstand. Within a certain range, the equipment can tolerate current fluctuations without affecting its performance or causing excessive energy consumption; each energy consumption characteristic evaluation index has different importance. The weight of different indexes is allocated through the analytic hierarchy process. For example, the baseline energy consumption value may be more important than the voltage sensitivity coefficient, so its weight coefficient will be higher.

[0060] In an optional embodiment, time-frequency domain analysis is performed on real-time operating status data to extract energy consumption fluctuation characteristics and load response features, generating an energy consumption-load correlation matrix, including:

[0061] B1. Based on wavelet transform, perform time-frequency domain decomposition on real-time working status data to obtain energy consumption fluctuation components in different frequency bands, and determine energy consumption fluctuation characteristics based on energy consumption fluctuation components.

[0062] B2. Analyze the response amplitude and phase difference of multiple load devices at a specific frequency based on Fourier transform to obtain load response characteristics;

[0063] B3. Based on the energy consumption fluctuation characteristics and load response features, construct an energy consumption load correlation matrix with load devices as rows and energy consumption influencing factors as columns. The elements of the energy consumption load correlation matrix represent the correlation strength coefficient between the corresponding load devices and energy consumption influencing factors.

[0064] It should be noted that wavelet transform is a signal analysis method that can decompose data simultaneously in the time and frequency domains; Fourier transform is used to convert data from the time domain to the frequency domain, analyzing the amplitude and phase information of the signal at different frequencies; load response amplitude refers to the strength or magnitude of the load device's response at a specific frequency, reflecting the degree of power or load change at that frequency; load response phase difference refers to the phase difference between the load device's response and the input signal, reflecting the signal's timing delay or relative change; the energy consumption load correlation matrix is ​​a matrix representing the relationship between different load devices and energy consumption influencing factors. Each element of the matrix represents the correlation strength coefficient between a certain load device and a certain energy consumption influencing factor (such as operating mode, load, environment, etc.). This matrix can be used to quantify the impact of different factors on equipment energy consumption; energy consumption influencing factors refer to various factors that affect changes in equipment energy consumption, such as equipment operating status, load size, environmental conditions, and equipment efficiency.

[0065] In an optional embodiment, the energy consumption sensitivity index of multi-load devices is calculated based on the initial energy consumption allocation priority region and the energy consumption load correlation matrix, including:

[0066] C1. Based on the initial energy consumption allocation priority area, assign different area weight coefficients to multi-load devices. Among them, the weight coefficient of the high priority area is the highest, the weight coefficient of the medium priority area is the second highest, and the weight coefficient of the low priority area is the lowest.

[0067] C2. Obtain the correlation strength coefficient corresponding to each load device in the energy consumption load correlation matrix, and multiply the regional weight coefficient and the correlation strength coefficient to obtain the initial sensitivity index.

[0068] C3. The initial sensitivity index is weighted and corrected based on the energy consumption fluctuation characteristics and load response characteristics to obtain the energy consumption sensitivity index. Among them, the load device with the larger fluctuation amplitude and the smaller the response delay has a higher energy consumption sensitivity index.

[0069] It should be noted that the initial sensitivity index is calculated by multiplying the regional weight coefficient and the correlation strength coefficient in the energy consumption load correlation matrix. This index measures the sensitivity of each load device to energy consumption changes under different regional weights. The higher the index value, the more sensitive the device is to energy consumption changes, and it may need to be prioritized for control. The weighted correction is a method to adjust the initial sensitivity index. By considering the characteristics of energy consumption fluctuations and load response characteristics, combined with the fluctuation amplitude and response delay of the device, the initial sensitivity index is adjusted. Generally, devices with larger fluctuation amplitudes or smaller response delays will have higher energy sensitivity indices, indicating that these devices are more sensitive to energy consumption changes and may need more energy efficiency optimization. The energy sensitivity index is the final sensitivity index, which reflects the degree of response of each load device to energy consumption fluctuations in actual operation. The larger the fluctuation amplitude (i.e., the more obvious the energy consumption fluctuation of the device) and the smaller the response delay (i.e., the faster the device responds), the higher the energy sensitivity index of the device. This index can help identify which devices are most critical in energy consumption management and need to be optimized and controlled in a focused manner.

[0070] In an optional embodiment, the energy consumption allocation ratio of multi-load devices is optimized based on the energy consumption sensitivity index to obtain an energy consumption allocation control configuration table, including:

[0071] D1. Construct an objective function for optimizing the energy consumption allocation ratio based on the energy consumption sensitivity index and the rated power of multi-load devices. The constraints of the objective function for optimizing the energy consumption allocation ratio include the maximum power supply capacity of the distribution box, the minimum operating power threshold of multi-load devices, and the upper limit of load response delay.

[0072] D2. Solve the objective function of energy consumption allocation ratio optimization based on particle swarm optimization algorithm to obtain the globally optimal energy consumption allocation ratio;

[0073] D3. Match and verify the energy consumption allocation ratio with the priority areas and energy consumption fluctuation characteristics of multi-load devices to ensure that the energy consumption allocation ratio meets the priority power supply needs of highly sensitive devices. Finally, generate an energy consumption allocation control configuration table that includes energy consumption allocation priority, energy consumption allocation ratio and device operation protection threshold.

[0074] It should be noted that the objective function of energy consumption allocation ratio optimization aims to optimize the energy consumption allocation ratio among multiple load devices, so that each device receives a reasonable energy allocation based on its energy consumption sensitivity index and rated power. The optimization objective is to minimize the total energy consumption or maximize the energy consumption allocation of certain devices. Particle swarm optimization (PSO) is an optimization algorithm that simulates the foraging behavior of flocks of birds in nature. In the energy consumption allocation optimization problem, it finds the optimal solution by continuously updating the velocity and position of particles. The globally optimal energy consumption allocation ratio refers to the energy consumption allocation ratio obtained by PSO that is the globally optimal solution. This means that among all possible energy consumption allocations, this solution can most effectively satisfy the optimization objective and constraints. The globally optimal allocation ratio can maximize energy efficiency and ensure a reasonable balance between device priority and energy consumption sensitivity. Energy consumption allocation priority refers to determining the power supply priority of different devices based on their energy consumption sensitivity index and priority region. Device operation protection threshold refers to the minimum operating power and protection threshold of each device, ensuring that the device operates at a power level no lower than the minimum operating power.

[0075] In an optional embodiment, the intelligent energy allocation simulation model includes a load device layer, an energy allocation strategy layer, and a simulation output layer. The load device layer is used to simulate the dynamic operating status of multiple load devices. The energy allocation strategy layer realizes dynamic energy allocation of each load device based on energy allocation priority and energy allocation ratio. The simulation output layer is used to display the energy allocation curve, total energy consumption trend, and operating status parameters of each device under different load combinations.

[0076] In an optional embodiment, an energy consumption parameter correction model is constructed based on the design power supply parameters of the distribution box and the rated parameters of the multi-load equipment, including:

[0077] E1. Set the design power supply parameters of the distribution box and the rated parameters of the multi-load equipment as model input variables to obtain a complete set of model input variables;

[0078] E2. Construct the mathematical relationship between input variables and theoretical energy consumption based on the least squares method to obtain the parameter mapping relationship based on the theoretical matching value of the design power supply parameters and rated parameters;

[0079] E3. The model is trained and optimized based on parameter mapping relationships and parameter deviation samples in historical operating data to obtain an energy consumption parameter correction model. The correction dimensions of the energy consumption parameter correction model include voltage fluctuation correction coefficient, current stability compensation coefficient, and power factor correction factor.

[0080] It should be noted that the design power supply parameters of the distribution box refer to the power supply capacity set during the design phase, including but not limited to maximum output power, voltage range, and current limits. These parameters provide the upper limit of power supply and basic operating conditions for the entire system, and are the basis for energy consumption allocation and optimization. The model input variable set is the collection of all input data required to build the energy consumption model, including the design power supply parameters of the distribution box and the rated parameters of the equipment. These input variables provide the basic data for subsequent energy consumption analysis, correction, and optimization. The least squares method is a mathematical optimization method used to find a set of parameters that minimizes the sum of squares of the errors (residuals) between the actual data and the theoretical model. In energy consumption modeling, the least squares method is often used to establish the mathematical relationship between input variables and theoretical energy consumption, and to fit the model by minimizing the error, so as to more accurately predict or correct energy consumption. The mathematical relationship between input variables and theoretical energy consumption describes how these inputs affect the energy consumption of the equipment. For example, there may be a certain proportional relationship between the rated power of equipment and the actual energy consumed, and the mathematical relationship that the maximum output power of the distribution box affects the upper limit of energy consumption of all equipment can be constructed using the least squares method; the parameter mapping relationship refers to the conversion relationship between the output of the theoretical model (such as the theoretical matching value of the design power supply parameters and rated parameters) and the actual energy consumption. By establishing this mapping relationship, the deviation between the theoretical energy consumption and the actual energy consumption of the equipment can be mapped out and then corrected; training and optimization refer to adjusting the energy consumption model through historical data so that it can more accurately reflect the actual operating conditions. By using parameter deviation samples in historical data, the model can be trained to correct the deviations in the original model; the energy consumption parameter correction model refers to a model that, after training and optimization, can adjust the theoretical energy consumption parameters of the equipment to better match the actual operating data. The purpose of this model is to reduce the energy consumption prediction error caused by the difference between theoretical assumptions and actual conditions.

[0081] In an optional embodiment, a deviation analysis is performed on the simulated energy consumption allocation data obtained from the simulation based on the energy consumption parameter correction model and the actual operating environment to obtain the energy consumption parameter correction coefficient, including:

[0082] F1. Collect actual energy consumption data of multi-load devices in actual operating environments;

[0083] F2. Calculate the difference between the actual energy consumption data and the simulated energy consumption distribution data to obtain the initial energy consumption deviation value;

[0084] F3. Based on the energy consumption parameter correction model, the initial energy consumption deviation value is decomposed in multiple dimensions to obtain the voltage fluctuation deviation component, current stability deviation component and power factor deviation component.

[0085] F4. Calculate the correction coefficients corresponding to the voltage fluctuation deviation component, current stability deviation component, and power factor deviation component, and then weight and fuse the correction coefficients to obtain the energy consumption parameter correction coefficients.

[0086] It's important to note that difference calculation refers to comparing actual energy consumption data with simulated energy consumption data and calculating the difference between the two. This difference is called the initial energy consumption deviation value, which reflects the error between the actual and theoretical energy consumption of the equipment. Multi-dimensional decomposition refers to breaking down the initial energy consumption deviation value into different components, each corresponding to a specific factor or dimension (such as voltage fluctuation, current stability, and power factor), in order to more accurately identify the contribution of each factor to the total energy consumption deviation. Through this decomposition, corrections can be made for each factor individually. The voltage fluctuation deviation component refers to the energy consumption deviation caused by grid voltage fluctuations. Voltage fluctuations may lead to changes in the power consumption of the equipment, thus affecting its energy efficiency. The current stability deviation component refers to the energy consumption deviation caused by current instability. If the current used by the equipment is unstable, it may lead to power waste or excessive consumption. This component is used to quantify the impact of current instability on energy consumption. The power factor deviation component refers to the energy consumption deviation caused by a low or unstable power factor. The power factor is an indicator of power system efficiency. A low power factor means low energy efficiency, and the equipment may consume more ineffective power. This component reflects the impact of power factor deviation on energy consumption. The correction coefficient is a value used to adjust the energy consumption of the equipment. Its purpose is to minimize the difference between the theoretical model and the actual energy consumption. Different correction coefficients are calculated for different deviation components (such as voltage fluctuations, current stability, power factor, etc.). Weighted fusion refers to weighting each correction coefficient according to the importance of different deviation components, so as to adjust the final energy consumption correction coefficient according to the degree of influence of each dimension. The weighted fusion correction coefficient can more comprehensively consider the impact of various factors on the total energy consumption. The energy consumption parameter correction coefficient is the final result obtained by weighted fusion of the correction coefficients of each deviation component. The energy consumption parameter correction coefficient is a scaling factor used to correct the deviation between theoretical simulation data and actual operating environment, ensuring that the subsequently generated energy consumption allocation scheme is more accurate and reliable.

[0087] Example 3, as Figure 2 As shown, the present invention proposes a multi-load energy consumption intelligent distribution and control system based on a distribution box, which is applicable to a multi-load energy consumption intelligent distribution and control method based on a distribution box, including:

[0088] Priority allocation unit 1 is used to obtain the initial operating parameter information of the multi-load devices connected to the distribution box, and to perform energy consumption characteristic analysis on the multi-load devices based on the initial operating parameter information in order to obtain the initial energy consumption allocation priority area.

[0089] Energy consumption correlation unit 2 is used to collect real-time operating status data of multi-load devices, perform time-frequency domain analysis on the real-time operating status data, extract energy consumption fluctuation characteristics and load response characteristics, and generate an energy consumption load correlation matrix.

[0090] The allocation configuration unit 3 is used to calculate the energy consumption sensitivity index of multi-load devices based on the initial energy consumption allocation priority area and energy consumption load correlation matrix, and optimize the energy consumption allocation ratio of multi-load devices based on the energy consumption sensitivity index to obtain the energy consumption allocation control configuration table.

[0091] Simulation distribution unit 4 is used to build an intelligent energy distribution simulation model based on the energy distribution control configuration table, simulate the real-time energy distribution under different load combinations and operating conditions based on the intelligent energy distribution simulation model, and build an energy consumption parameter correction model based on the design power supply parameters of the distribution box and the rated parameters of multi-load equipment.

[0092] Energy consumption allocation unit 5 is used to perform deviation analysis on the simulated energy consumption allocation data obtained from the simulation based on the energy consumption parameter correction model and the actual operating environment, so as to obtain the energy consumption parameter correction coefficient, generate a dynamic energy consumption allocation adjustment scheme based on the energy consumption parameter correction coefficient and the energy consumption allocation control configuration table, and execute intelligent energy consumption allocation and control operations of multi-load devices based on the dynamic energy consumption allocation adjustment scheme.

[0093] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A method for intelligent allocation and control of multi-load energy consumption based on a distribution box, characterized in that, include: Obtain the initial operating parameter information of the multi-load devices connected to the distribution box, and perform energy consumption characteristic analysis on the multi-load devices based on the initial operating parameter information to obtain the initial energy consumption allocation priority area; Collect real-time operating status data of the multi-load devices, perform time-frequency domain analysis on the real-time operating status data, extract energy consumption fluctuation characteristics and load response characteristics, and generate an energy consumption load correlation matrix. The energy consumption sensitivity index of the multi-load device is calculated based on the initial energy consumption allocation priority region and energy consumption load correlation matrix. The energy consumption allocation ratio of the multi-load device is optimized based on the energy consumption sensitivity index to obtain the energy consumption allocation control configuration table. An intelligent energy distribution simulation model is constructed based on the energy distribution control configuration table. The real-time energy distribution under different load combinations and operating conditions is simulated based on the intelligent energy distribution simulation model. An energy consumption parameter correction model is constructed based on the design power supply parameters of the distribution box and the rated parameters of the multi-load equipment. Based on the energy consumption parameter correction model and the actual operating environment, deviation analysis is performed on the simulated energy consumption allocation data to obtain energy consumption parameter correction coefficients. Based on the energy consumption parameter correction coefficients and the energy consumption allocation control configuration table, a dynamic energy consumption allocation adjustment scheme is generated. Based on the dynamic energy consumption allocation adjustment scheme, the intelligent energy consumption allocation and control operation of the multi-load device is executed.

2. The method for intelligent allocation and control of multi-load energy consumption based on a distribution box according to claim 1, characterized in that, The initial operating parameter information includes the rated power, operating voltage range, operating current range, and initial operating mode setting of the multi-load device. The real-time operating status data includes the actual power consumption, operating voltage fluctuation value, operating current fluctuation value, and operating time of the multi-load device. The energy consumption allocation control configuration table includes energy consumption allocation priority, energy consumption allocation ratio, and device operation protection threshold.

3. The method for intelligent allocation and control of multi-load energy consumption based on a distribution box according to claim 2, characterized in that, Based on the initial operating parameter information, energy consumption characteristics of the multi-load devices are analyzed to obtain the initial energy consumption allocation priority region, including: Based on the rated power, operating voltage range, operating current range and initial operating mode of the multi-load device, an energy consumption characteristic evaluation index is constructed, wherein the energy consumption characteristic evaluation index includes a basic energy consumption benchmark value, voltage sensitivity coefficient and current fluctuation tolerance. The weight coefficients of the energy consumption characteristic evaluation index are determined based on the analytic hierarchy process, and a comprehensive energy consumption characteristic score is calculated based on the weight coefficients to obtain the comprehensive energy consumption characteristic score. Based on the comprehensive energy consumption characteristic score, the multi-load devices are divided into high-priority areas, medium-priority areas, and low-priority areas.

4. The method for intelligent allocation and control of multi-load energy consumption based on a distribution box according to claim 3, characterized in that, Time-frequency domain analysis is performed on the real-time operating status data to extract energy consumption fluctuation characteristics and load response features, generating an energy consumption-load correlation matrix, including: The real-time working status data is decomposed in the time-frequency domain based on wavelet transform to obtain energy consumption fluctuation components in different frequency bands, and the energy consumption fluctuation characteristics are determined based on the energy consumption fluctuation components. The response amplitude and phase difference of the multi-load device at a specific frequency are analyzed based on Fourier transform to obtain the load response characteristics; Based on the energy consumption fluctuation characteristics and load response features, an energy consumption load correlation matrix is ​​constructed with load devices as rows and energy consumption influencing factors as columns. The elements of the energy consumption load correlation matrix represent the correlation strength coefficient between the corresponding load devices and energy consumption influencing factors.

5. The method for intelligent allocation and control of multi-load energy consumption based on a distribution box according to claim 4, characterized in that, The energy sensitivity index of the multi-load device is calculated based on the initial energy consumption allocation priority region and the energy consumption load correlation matrix, including: Based on the initial energy consumption allocation priority region, the multi-load device is assigned different regional weight coefficients, wherein the high priority region has the highest weight coefficient, the medium priority region has the next highest weight coefficient, and the low priority region has the lowest weight coefficient. Obtain the correlation strength coefficient corresponding to each load device in the energy consumption load correlation matrix, and multiply the region weight coefficient and the correlation strength coefficient to obtain the initial sensitivity index; The initial sensitivity index is weighted and corrected based on the energy consumption fluctuation characteristics and load response characteristics to obtain the energy consumption sensitivity index. The energy consumption sensitivity index is higher for load devices with larger fluctuation amplitude and smaller response delay.

6. The method for intelligent allocation and control of multi-load energy consumption based on a distribution box according to claim 5, characterized in that, Based on the energy consumption sensitivity index, the energy consumption allocation ratio of the multi-load devices is optimized to obtain an energy consumption allocation control configuration table, including: An energy consumption allocation ratio optimization objective function is constructed based on the energy consumption sensitivity index and the rated power of the multi-load device. The constraints of the energy consumption allocation ratio optimization objective function include the maximum power supply capacity of the distribution box, the minimum operating power threshold of the multi-load device, and the upper limit of the load response delay. The objective function for optimizing the energy consumption allocation ratio is solved based on the particle swarm optimization algorithm to obtain the globally optimal energy consumption allocation ratio. The energy consumption allocation ratio is matched and verified with the priority area and energy consumption fluctuation characteristics of the multi-load devices to ensure that the energy consumption allocation ratio meets the priority power supply requirements of the highly sensitive devices. Finally, an energy consumption allocation control configuration table is generated, which includes energy consumption allocation priority, energy consumption allocation ratio and device operation protection threshold.

7. The method for intelligent allocation and control of multi-load energy consumption based on a distribution box according to claim 6, characterized in that, The intelligent energy consumption allocation simulation model includes a load device layer, an energy consumption allocation strategy layer, and a simulation output layer. The load device layer is used to simulate the dynamic operating status of multiple load devices. The energy consumption allocation strategy layer realizes dynamic energy consumption allocation for each load device based on the energy consumption allocation priority and energy consumption allocation ratio. The simulation output layer is used to display the energy consumption allocation curve, total energy consumption trend, and operating status parameters of each device under different load combinations.

8. The method for intelligent allocation and control of multi-load energy consumption based on a distribution box according to claim 7, characterized in that, An energy consumption parameter correction model is constructed based on the design power supply parameters of the distribution box and the rated parameters of the multi-load equipment, including: The design power supply parameters of the distribution box and the rated parameters of the multi-load equipment are set as model input variables to obtain a complete set of model input variables; The mathematical relationship between input variables and theoretical energy consumption is constructed based on the least squares method to obtain a parameter mapping relationship based on the theoretical matching value of the design power supply parameters and rated parameters; The model is trained and optimized based on the parameter mapping relationship and parameter deviation samples in historical operating data to obtain the energy consumption parameter correction model. The correction dimensions of the energy consumption parameter correction model include voltage fluctuation correction coefficient, current stability compensation coefficient and power factor correction factor.

9. A method for intelligent allocation and control of multi-load energy consumption based on a distribution box according to claim 8, characterized in that, Based on the energy consumption parameter correction model and the actual operating environment, a deviation analysis is performed on the simulated energy consumption allocation data obtained from the simulation to obtain the energy consumption parameter correction coefficients, including: Collect actual energy consumption data of the multi-load devices in the actual operating environment; The difference between the actual energy consumption data and the simulated energy consumption allocation data is calculated to obtain the initial energy consumption deviation value; Based on the energy consumption parameter correction model, the initial energy consumption deviation value is decomposed in multiple dimensions to obtain voltage fluctuation deviation component, current stability deviation component and power factor deviation component. The correction coefficients corresponding to the voltage fluctuation deviation component, current stability deviation component, and power factor deviation component are calculated, and the correction coefficients are weighted and fused to obtain the energy consumption parameter correction coefficients.

10. A multi-load energy consumption intelligent distribution and control system based on a distribution box, applicable to the multi-load energy consumption intelligent distribution and control method based on a distribution box as described in any one of claims 1-9, characterized in that, include: The priority allocation unit (1) is used to obtain the initial operating parameter information of the multi-load equipment connected to the distribution box, and to perform energy consumption characteristic analysis on the multi-load equipment based on the initial operating parameter information to obtain the initial energy consumption allocation priority area. Energy consumption correlation unit (2) is used to collect real-time working status data of the multi-load device, perform time-frequency domain analysis on the real-time working status data, extract energy consumption fluctuation characteristics and load response characteristics, and generate energy consumption load correlation matrix. The allocation configuration unit (3) is used to calculate the energy consumption sensitivity index of the multi-load device based on the initial energy consumption allocation priority area and energy consumption load correlation matrix, and optimize the energy consumption allocation ratio of the multi-load device based on the energy consumption sensitivity index to obtain the energy consumption allocation control configuration table. The simulation allocation unit (4) is used to build an intelligent energy allocation simulation model based on the energy allocation control configuration table, simulate the real-time energy allocation under different load combinations and operating conditions based on the intelligent energy allocation simulation model, and build an energy consumption parameter correction model based on the design power supply parameters of the distribution box and the rated parameters of the multi-load equipment. The energy consumption allocation unit (5) is used to perform deviation analysis on the simulated energy consumption allocation data obtained from the simulation based on the energy consumption parameter correction model and the actual operating environment, so as to obtain the energy consumption parameter correction coefficient, generate a dynamic energy consumption allocation adjustment scheme based on the energy consumption parameter correction coefficient and the energy consumption allocation control configuration table, and execute the intelligent energy consumption allocation and control operation of the multi-load device based on the dynamic energy consumption allocation adjustment scheme.

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