Public energy consumption equipment operation and maintenance management system suitable for smart park

Abstract

The invention relates to the technical field of smart energy management, in particular to a public energy consumption equipment operation and maintenance management system suitable for a smart park. The system comprises an equipment operation state sensing module, an energy consumption characteristic dynamic analysis module, an operation parameter optimization module, an equipment state balance regulation and control module, an equipment operation safety constraint verification module and an equipment cooperative operation execution module. According to the invention, through combination of time sequence analysis and load fluctuation trend extraction, dynamic monitoring and trend arrangement are realized, the accuracy and comprehensiveness of operation data are improved, the operation diagnosis capability is improved, the parameter adjustment flexibility is enhanced, multi-device cooperative regulation and control and load balancing are realized, energy consumption fluctuation is reduced, and the system efficiency is improved. And in combination with safety verification and offset risk analysis, potential risk areas are adjusted in real time, the operation reliability is enhanced, a global regulation and control scheme is dynamically generated, energy consumption waste is reduced, the management efficiency of public energy consumption equipment is comprehensively improved, and the efficient energy-saving target of the smart park is achieved.

Description

Technical Field

[0001] The present invention relates to the field of smart energy management technology, and in particular to a public energy-consuming equipment operation and maintenance management system suitable for a smart park. Background Art

[0002] The field of smart energy management technology refers to a management system that uses advanced technologies such as the Internet of Things, cloud computing, big data analysis, and artificial intelligence to intelligently monitor, analyze, and optimize the entire life cycle of energy production, transmission, distribution, and use. This field aims to improve energy utilization efficiency, reduce energy consumption, optimize energy distribution, and realize visualization, real-time monitoring, and intelligent scheduling of energy systems. It is widely used in scenarios such as smart parks, smart grids, and industrial enterprises, and is an important technical direction for promoting energy conservation, emission reduction, and green and low-carbon development.

[0003] Among them, the public energy-consuming equipment operation and maintenance management system is an intelligent management platform for various energy-consuming equipment in smart parks (such as lighting equipment, air conditioners, elevators, etc.). The system collects equipment operation data through smart sensors and combines the energy management platform for real-time monitoring, status diagnosis and operation and maintenance optimization. Its main purpose is to improve the operation and maintenance efficiency of public energy-consuming equipment, reduce energy waste, and ensure the reliability and safety of equipment, thereby helping smart parks achieve green energy saving and efficient management goals.

[0004] In the operation and maintenance management of public energy-consuming equipment, the existing technology mainly relies on static monitoring and regular manual inspections, and fails to deeply analyze the dynamic relationship between load, energy consumption rate and environmental parameters during the operation of the equipment, resulting in a lack of pertinence and real-timeness in diagnosing the operating status of the equipment, and energy consumption anomalies are prone to not being discovered in time. The existing system's analysis of abnormal characteristics of the equipment's operating status is relatively rough, and lacks accurate extraction and grouping of the offset relationship of operating parameters, resulting in poor flexibility in adjusting equipment parameters when dealing with complex operating environments, weak collaborative control capabilities among multiple devices, and unbalanced load distribution, which increases the energy consumption and fluctuation risks of equipment operation. The existing technology mainly focuses on single equipment in equipment operation safety verification, and fails to systematically analyze the potential offset risks in the operation of multiple devices, which easily leads to operating interference between equipment that affects the overall efficiency and stability. This insufficiency leads to low operation and maintenance efficiency of public energy-consuming equipment, and cannot fully achieve energy conservation and green management goals. Summary of the invention

[0005] In order to solve the problem that the existing technology in the operation and maintenance management of public energy-consuming equipment mainly relies on static monitoring and regular manual inspections, and fails to deeply analyze the dynamic relationship between load, energy consumption rate and environmental parameters during the operation of the equipment, resulting in a lack of pertinence and real-time in diagnosing the operation status of the equipment, and it is easy for energy consumption anomalies to not be discovered in time. The existing system is relatively rough in analyzing the abnormal characteristics of the equipment operation status, lacks accurate extraction and grouping of the offset relationship of the operating parameters, resulting in poor flexibility in adjusting equipment parameters when dealing with complex operating environments, weak collaborative control capabilities among multiple devices, unbalanced load distribution, increased equipment operation energy consumption and fluctuation risks, the existing technology is mainly based on single equipment in equipment operation safety verification, and fails to systematically analyze the potential offset risks in the operation of multiple devices, which easily leads to operation interference between equipment that affects the overall efficiency and stability, and the technical problem that the operation and maintenance efficiency of public energy-consuming equipment is low and the energy saving and consumption reduction and green management goals cannot be fully achieved. The embodiment of the present invention provides a public energy-consuming equipment operation and maintenance management system suitable for smart parks. The technical solution is as follows:

[0006] On the one hand, a public energy-consuming equipment operation and maintenance management system suitable for a smart park is provided, the system comprising:

[0007] The equipment operation status perception module compares the load and energy consumption time series based on the equipment operation load, energy consumption rate and operating environment parameters, extracts the change trend, groups and sorts the fluctuation range, and obtains the equipment operation fluctuation trend data;

[0008] The energy consumption characteristic dynamic analysis module analyzes the changes in load, energy consumption and environmental parameters based on the equipment operation fluctuation trend data, calculates trends, sorts out key fluctuation intervals, screens abnormal points, and obtains abnormal operation characteristic data;

[0009] The operation parameter optimization module extracts the change interval in the abnormal operation characteristic data, calibrates the offset threshold, groups the parameter set, combines the working condition fitting calculation, and generates the equipment operation optimization parameter group;

[0010] The equipment state balance control module calculates the load and energy consumption distribution ratio based on the equipment operation optimization parameter group, analyzes the equipment impact, and obtains the equipment balance control data set;

[0011] The equipment operation safety constraint verification module performs threshold comparison on the load balancing parameters of the balancing control data based on the equipment balancing control data set, screens and reorganizes the out-of-range parameters, analyzes the verification data, and obtains the equipment offset correction parameter set;

[0012] The equipment collaborative operation execution module maps the operation parameters of multiple equipment based on the equipment offset correction parameter set, dynamically groups loads and operation optimization parameters, refines and allocates operation and maintenance parameters, and generates a global control plan for common energy-consuming equipment.

[0013] As a further solution of the present invention, the equipment operation fluctuation trend data includes load change trends, energy consumption change trends and environmental parameter fluctuation ranges; the abnormal operation characteristic data includes the associated change range of load and energy consumption rate, key fluctuation intervals and characteristic abnormal areas; the equipment operation optimization parameter group includes parameter offset relationships, threshold calibration results and adaptation parameter combinations; the equipment balance control data set includes load distribution ratios, load change trends between equipment and balance control parameters of high-frequency change items; the equipment offset correction parameter set includes out-of-range parameters, potential offset risk areas and risk parameter adjustment values; the global control scheme for common energy-consuming equipment includes collaborative operation change trends, control path allocation parameters and common energy-consuming equipment requirements.

[0014] As a further solution of the present invention, the device operation status perception module includes:

[0015] The time series association submodule extracts the time series data of the equipment operation load and energy consumption rate based on the equipment operation load, energy consumption rate and operation environment parameters, matches the operation environment parameters with the load and energy consumption rate data through timestamps, and establishes an associated time series;

[0016] The change trend analysis submodule extracts the numerical range of load and energy consumption rate in each time period based on the associated time series, determines the load change trend by point-by-point comparison, divides the load fluctuation interval to count the energy consumption rate change amplitude, extracts matching operating environment parameters, and obtains load and energy consumption change trend data;

[0017] The fluctuation trend sorting submodule compares and analyzes the operating environment parameters and energy consumption rate based on the load and energy consumption change trend data in combination with the load fluctuation range, counts the fluctuation range of each group of energy consumption rate and associates it with the load change trend, sorts the fluctuation range and trend classification data, and obtains the equipment operation fluctuation trend data.

[0018] As a further solution of the present invention, the energy consumption characteristics dynamic analysis module includes:

[0019] The variation range calculation submodule matches and calculates the load and energy consumption rate value range according to the timestamp based on the equipment operation fluctuation trend data, processes the environmental parameter classification, and generates a correlation variation range table;

[0020] The dynamic feature summary submodule collects statistics on the distribution of environmental parameters and calculates trends according to the load change interval based on the associated change range table, summarizes the corresponding relationship between the load and the energy consumption rate, and obtains a dynamic feature trend table;

[0021] Based on the dynamic characteristic trend table, the abnormal point screening submodule calculates the weighted change rate within the monitoring period, analyzes the change amplitude and screens the abnormal points, locates the fluctuation range where the abnormal points are located, and classifies and marks the regional characteristics to obtain abnormal operation characteristic data.

[0022] As a further solution of the present invention, the weighted rate of change within the period is monitored, according to the formula:

[0023]

[0024] Among them, R represents the weighted change rate within the monitoring period, L represents the absolute change of the load value, E represents the absolute change of the energy consumption rate, P represents the absolute change of the environmental parameter, T represents the interval span of the change within the time period, and α 1 is the weight coefficient of load change to the overall fluctuation amplitude, α 2 is the weight coefficient of energy consumption rate change to the overall fluctuation range, β 1 is the fluctuation adjustment coefficient of environmental parameters, β 2 is the smoothing coefficient for the time span.

[0025] As a further solution of the present invention, the operating parameter optimization module includes:

[0026] The offset interval calculation submodule calculates and classifies the load and energy consumption rate offsets based on the abnormal operation characteristic data, calibrates the upper and lower limits and thresholds, and establishes an offset parameter interval table;

[0027] The parameter combination induction submodule compares and measures the classification parameters based on the offset parameter interval table, analyzes the load interval parameter offset trend, calculates the matching and summarizes the groups in combination with the energy consumption fluctuation amplitude, and generates an adaptation parameter combination table;

[0028] The optimization combination screening submodule performs load range and energy consumption rate distribution fitting analysis on the parameter combination based on the adaptation parameter combination table, screens the best adaptability combination and combines the working condition data grouping, sorts out the tag association characteristics, and generates the equipment operation optimization parameter group.

[0029] As a further solution of the present invention, the device state balance control module includes:

[0030] The load ratio calculation submodule extracts the equipment operation load data and energy consumption rate based on the equipment operation optimization parameter group, matches the load and energy consumption distribution ratio, normalizes the load proportion interval and calibrates the distribution range data, and generates an equipment load distribution ratio table;

[0031] The load trend analysis submodule compares the distribution ratios between devices based on the equipment load distribution ratio table, extracts the load distribution deviation data and measures the load impact between devices, analyzes the distribution characteristics of the deviation on the operation stability, classifies the load distribution fluctuation trend and integrates the change records, and establishes the load change trend table between devices;

[0032] The high-frequency fluctuation control submodule extracts high-frequency fluctuation parameters and analyzes the impact of load offset based on the load change trend table between the devices, calculates the load control parameters in the high-frequency fluctuation area, adjusts the distribution parameters to the adaptation interval, and reorganizes the load distribution relationship to obtain the equipment balance control data set.

[0033] As a further solution of the present invention, the load control parameters in the high-frequency fluctuation area are calculated according to the formula

[0034]

[0035] Among them, P adj represents the load control parameter in the high-frequency fluctuation area, w i Represents the high-frequency fluctuation weight coefficient, L i represents the load value of the device at the i-th moment, μ represents the overall mean value of the device load, and α j Indicates the low-frequency fluctuation adjustment coefficient, F j represents the characteristic frequency of the jth low-frequency fluctuation between devices, F base represents the base frequency, n represents the total number of moments, and m represents the total number of low-frequency fluctuations.

[0036] As a further solution of the present invention, the device runs a safety constraint verification module including:

[0037] The out-of-range parameter extraction submodule compares the load balancing parameters with the operating safety range threshold based on the equipment balance control data set, extracts and classifies the over-threshold load parameters, selects the corresponding operating scenarios of the parameters, and establishes a load out-of-range parameter set;

[0038] The offset risk analysis submodule matches the equipment dynamic energy consumption relationship parameters based on the load out-of-range parameter set, analyzes the load and dynamic energy consumption offset amplitude, determines the offset area range and compares the energy consumption change trend, and generates a potential offset risk area set;

[0039] The offset correction adjustment submodule matches the balance control parameter value based on the potential offset risk area set, adjusts the parameters in the offset area and analyzes the equipment balance relationship to obtain the equipment offset correction parameter set.

[0040] As a further solution of the present invention, the device collaborative operation execution module includes:

[0041] The operating parameter combination submodule extracts the equipment operating parameters based on the equipment offset correction parameter set and groups them for matching, combines the load parameters with the operating optimization parameters, sorts out the combination relationship under the operating scenario, and generates a multi-equipment operating parameter combination set;

[0042] The path control analysis submodule analyzes the change of the combined parameter operation state based on the multi-device operation parameter combination set, analyzes the dynamic characteristics and change range, extracts the operation path parameter nodes and allocates operation and maintenance parameters, organizes the path control data, and generates the device operation path control parameter set;

[0043] The optimization and control submodule analyzes the operation requirements of public energy-consuming equipment based on the equipment operation path control parameter set, matches node load parameters with energy consumption demand parameters, extracts the global distribution range of path load and dynamically adjusts parameters, summarizes operation data, and generates a global control plan for public energy-consuming equipment.

[0044] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0045] By collecting equipment operating load, energy consumption rate and environmental parameters, combined with time series analysis and load fluctuation trend extraction, we deeply explore the complex relationship between load and energy consumption during equipment operation, realize dynamic monitoring and trend sorting of operating status, enhance the accuracy and comprehensiveness of operating data, and use the correlation distribution analysis of load and energy consumption to screen key fluctuation intervals and dynamically generate characteristic trend tables, significantly improving the ability to accurately diagnose operating status. By extracting abnormal operating characteristics and classifying offset parameters, combined with fitting calculation to optimize operating conditions, we effectively improve the flexibility and adaptability of equipment parameter adjustment. We further analyze load distribution differences and coordinate the operating loads between devices. It realizes coordinated control and load balancing of multiple devices, enhances the overall operation efficiency of the system and reduces energy consumption fluctuations. Based on the safety constraint verification and offset risk analysis of operating parameters, it can discover and adjust potential abnormal risk areas in real time, enhance the reliability and safety of equipment operation, and dynamically generate global control solutions for public energy-consuming equipment by combining the coordinated operation of multiple devices with path optimization, effectively reducing energy waste and improving the management efficiency and green energy-saving effects of the equipment throughout its life cycle. The overall innovative processing logic comprehensively improves the level of refined management of public energy-consuming equipment through dynamic monitoring, real-time optimization, coordinated control and safety verification, helping smart parks achieve efficient energy-saving goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 It is a schematic diagram of a public energy-consuming equipment operation and maintenance management system applicable to a smart park provided by an embodiment of the present invention;

[0048] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0049] Figure 3 This is a flow chart of the device operation status perception module in the present invention;

[0050] Figure 4 It is a flow chart of the energy consumption characteristic dynamic analysis module in the present invention;

[0051] Figure 5 It is a flow chart of the operation parameter optimization module in the present invention;

[0052] Figure 6 This is a flow chart of the equipment state balance control module in the present invention;

[0053] Figure 7 A flowchart of a device running a security constraint verification module in the present invention;

[0054] Figure 8 It is a flow chart of the cooperative operation execution module of the devices in the present invention. DETAILED DESCRIPTION

[0055] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0056] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0057] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0058] In the embodiments of the present invention, sometimes the subscripts such as W 1 It may be written in non-subscript form such as W1. When the difference is not emphasized, the meaning is the same.

[0059] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0060] The embodiment of the present invention provides a public energy-consuming equipment operation and maintenance management system applicable to a smart park, such as Figure 1-2 The schematic diagram of the public energy-consuming equipment operation and maintenance management system applicable to the smart park shown in the figure includes:

[0061] The equipment operation status perception module is based on the equipment operation load, energy consumption rate and operating environment parameters, correlates and compares the time series of load and energy consumption rate, extracts the load change trend, analyzes the environmental parameters and energy consumption change trend, groups and organizes the fluctuation range, and obtains the equipment operation fluctuation trend data;

[0062] The energy consumption characteristics dynamic analysis module extracts the correlation change range of load, energy consumption rate and environmental parameters based on the equipment operation fluctuation trend data, calculates the load and energy consumption rate distribution trend, organizes the key fluctuation interval into a dynamic characteristic trend table, analyzes the parameter change rate, screens abnormal points and classifies the characteristic abnormal areas, and obtains abnormal operation characteristic data;

[0063] The operation parameter optimization module extracts the change interval of load, energy consumption rate and environmental parameters in the abnormal operation characteristic data, screens the parameter offset relationship and calibrates the threshold, classifies and groups the parameter set within the offset interval, combines the operation condition fitting measurement, extracts the adaptation parameter combination, and generates the equipment operation optimization parameter group;

[0064] The equipment status balance control module calculates the distribution ratio of operating load and energy consumption rate based on the equipment operation optimization parameter group, matches the total load capacity, extracts the load distribution difference between devices, analyzes the impact of multiple devices on load offset, organizes the load change trend between devices into a balance control parameter table, controls high-frequency change items, and obtains the equipment balance control data set;

[0065] The equipment operation safety constraint verification module is based on the equipment balance control data set. It compares the thresholds of the load balance parameters and the operation safety range in the equipment balance control data item by item, extracts the out-of-range parameters and screens and reorganizes them, analyzes and verifies the data in conjunction with the equipment dynamic energy consumption relationship parameters, extracts the potential deviation risk area, adjusts the risk parameters, and obtains the equipment deviation correction parameter set.

[0066] The equipment collaborative operation execution module is based on the equipment offset correction parameter set, maps the operating parameters of multiple devices, dynamically groups load parameters and operation optimization parameter combinations, extracts the changing trend of collaborative operation within the group, decomposes the control path according to the equipment operation path, refines and allocates operation and maintenance parameters, organizes and outputs the needs of public energy-consuming equipment, and generates a global control plan for public energy-consuming equipment.

[0067] Equipment operation fluctuation trend data include load change trend, energy consumption change trend and environmental parameter fluctuation range. Abnormal operation characteristic data include the associated change range of load and energy consumption rate, key fluctuation range and characteristic abnormal area. Equipment operation optimization parameter group includes parameter offset relationship, threshold calibration results and adaptation parameter combination. Equipment balance control data set includes load distribution ratio, load change trend between equipment and balance control parameters of high-frequency change items. Equipment offset correction parameter set includes out-of-range parameters, potential offset risk area and risk parameter adjustment value. The global control plan for common energy-consuming equipment includes collaborative operation change trend, control path allocation parameters and common energy-consuming equipment demand.

[0068] Specifically, if Figure 2 , 3 As shown, the device operation status perception module includes:

[0069] The time series association submodule extracts the time series data of the equipment operation load and energy consumption rate based on the equipment operation load, energy consumption rate and operation environment parameters, matches the operation environment parameters with the load and energy consumption rate data through timestamps, and establishes an associated time series;

[0070] The load data and energy consumption rate data collected by the equipment are synchronized according to the timestamp, and the data at each time point are aligned to form a basic correlation matrix to realize the correspondence between various parameters. The operating load data is set as the sampling data points in minutes within a certain period of time, and the energy consumption rate data is used as the energy consumption record within the same time period. The sampling frequency is the same, and the load and energy consumption rate data are mapped one by one through timestamps. The operating environment parameters obtain real-time information through sensors, and the base unit is set per hour. The average value of the real-time environmental parameters is interpolated into the minute-level timestamp sequence and combined with the load and energy consumption rate data. The multi-dimensional time series data is divided into multiple intervals according to the time nodes, so as to facilitate the subsequent in-depth analysis of the correlation between load and energy consumption, obtain the matching operating environment parameters and the combination data of equipment load and energy consumption rate, and establish a correlated time series.

[0071] The change trend analysis submodule extracts the numerical range of load and energy consumption rate in each time period based on the associated time series, determines the load change trend through point-by-point comparison, divides the load fluctuation interval to count the energy consumption rate change amplitude, extracts matching operating environment parameters, and obtains load and energy consumption change trend data;

[0072] The load and energy consumption rate are divided by setting a fixed time period length, and the maximum, minimum and mean values ​​of each time period are gradually extracted to construct an interval data range. The load change trend is determined by point-by-point comparison, and the load value changes are labeled as three types: increase, decrease and stable state to form a trend classification matrix. At the same time, the load fluctuation interval is divided to statistically analyze the energy consumption rate change amplitude. The proportional relationship between the energy consumption rate fluctuation range and the load change in each interval is combined with the time series analysis, and the correlation coefficient between the load fluctuation and the energy consumption rate fluctuation is calculated. By screening the parameter values ​​that are significantly correlated with the load change in environmental parameters such as temperature or humidity, the matching operating environment parameters are extracted, the load and energy consumption change trend data are obtained, and the trend relationship data in different intervals are integrated into a complete time series trend model.

[0073] The fluctuation trend sorting submodule compares and analyzes the operating environment parameters and energy consumption rate based on the load and energy consumption change trend data and the load fluctuation range, counts the fluctuation range of each group of energy consumption rate and associates it with the load change trend, sorts out the fluctuation range and trend classification data, and obtains the equipment operation fluctuation trend data;

[0074] By calculating the correspondence between the fluctuation amplitude of the load fluctuation range and the variation range of environmental parameters, a dynamic correspondence matrix between trend data is established, and at the same time, the fluctuation range of each group of energy consumption rate is counted. By dividing the energy consumption fluctuation data into fluctuation ranges with different amplitudes, the frequency and cumulative proportion of load changes within the energy consumption rate fluctuation range are calculated, and the energy consumption rate fluctuation range is classified in combination with the change trend of the load fluctuation range. The variable values ​​in the environmental parameters that have a significant impact on the energy consumption rate fluctuation are extracted, and the fluctuation range and trend classification data are sorted out through correlation analysis. The equipment operation fluctuation trend is described in a hierarchical manner, and finally the equipment operation fluctuation trend data is obtained to form the load and energy consumption rate fluctuation classification and trend analysis results based on the time period.

[0075] Specifically, if Figure 2 , 4 As shown, the energy consumption characteristics dynamic analysis module includes:

[0076] The variation range calculation submodule matches and calculates the load and energy consumption rate value range based on the equipment operation fluctuation trend data according to the timestamp, processes the environmental parameter classification, and generates the associated variation range table;

[0077] For the calculation of operation fluctuation trend data, according to the periodic fluctuation characteristics of equipment operation, the load data and energy consumption rate data in equipment operation are matched one by one with precise timestamp as the matching basis, and the range of load and energy consumption rate is calculated by interval data difference accumulation. The specific operation is to record the load increment and energy consumption increment in each time interval separately, and calculate the upper and lower bounds of numerical fluctuations in the interval. Based on the measurement results, the load and energy consumption rate data are divided into different change intervals, and the fluctuation characteristics of each interval are extracted. The collected environmental parameters (such as temperature, humidity, wind speed, etc.) are classified parameter by parameter, and the environmental parameters are matched and merged with the equipment load according to a specific time series. The classified parameters are further associated with the calculated load and energy consumption range to generate an associated change range table to describe the load and energy consumption rate change range of the equipment under different time and environmental conditions. To ensure the accuracy of the range table, the reliability of the results is improved by removing some abnormal values ​​and adjusting the boundary values, which ultimately provides a scientific basis for subsequent data analysis.

[0078] The dynamic feature summary submodule collects statistics on the distribution of environmental parameters and calculates trends based on the associated change range table and load change interval, summarizes the corresponding relationship between load and energy consumption rate, and obtains a dynamic feature trend table;

[0079] On the basis of the associated change range table, with the load change interval as the main line, the distribution characteristics of the environmental parameters in different intervals are counted one by one, and the environmental parameters (such as temperature, humidity, wind speed, etc.) in each load change interval are distributed and counted, and the median, maximum, minimum and distribution deviation of the distribution are calculated. In this process, the corresponding relationship between the change trend of the environmental parameters and the energy consumption rate is dynamically summarized. In the process of induction, the influence weight of the environmental parameters on the load and the energy consumption rate is further calculated to determine the main influencing factors in each interval. For example, whether the increase in the energy consumption rate in the higher temperature interval is significantly greater than that in other intervals, the dynamic trend characteristics are summarized by calculating the proportional relationship between the load change rate and the energy consumption rate change rate, and the above analysis results are sorted out to obtain a dynamic characteristic trend table between the load and the energy consumption rate. This trend table can be used to describe the energy consumption change law of the equipment under different load conditions, as well as the mechanism of action of environmental parameters on the equipment operation.

[0080] The abnormal point screening submodule measures the weighted change rate within the monitoring period based on the dynamic characteristic trend table, analyzes the change amplitude and screens the abnormal points, locates the fluctuation range where the abnormal points are located, and classifies and marks the regional characteristics to obtain abnormal operation characteristic data;

[0081] The weighted rate of change during the monitoring period is calculated according to the formula:

[0082]

[0083] Among them, R represents the weighted change rate within the monitoring period, L represents the absolute change of the load value, E represents the absolute change of the energy consumption rate, P represents the absolute change of the environmental parameter, T represents the interval span of the change within the time period, and α 1 is the weight coefficient of load change to the overall fluctuation amplitude, α 2 is the weight coefficient of energy consumption rate change to the overall fluctuation range, β 1 is the fluctuation adjustment coefficient of environmental parameters, β 2 is the smoothing coefficient of the time span;

[0084] Detailed explanation of the formula and the process of formula calculation and derivation:

[0085] This formula is used to calculate the weighted rate of change R of load, energy consumption rate and environmental parameters, and the result is used to identify abnormal points and locate fluctuation ranges;

[0086] L is the absolute change in load value, which is set to 15%, reflecting the degree of change in system load during the monitoring period;

[0087] E is the absolute change in energy consumption rate, which is set to 10%, indicating the change in the system energy consumption rate within the same period;

[0088] P is the absolute change in environmental parameters, such as temperature change, which is set to 5°C to represent the fluctuation of environmental conditions;

[0089] T is the span of the change interval within the time period, which is set to 60 minutes, indicating the length of the monitoring period;

[0090] α 1 is the weight coefficient of load change, which is set to 0.6 and adjusted according to the sensitivity of load change based on the impact of load on system performance;

[0091] α 2 is the weight coefficient of the energy consumption rate change, which is set to 0.4 and adjusted according to the sensitivity of energy consumption changes based on the impact of energy consumption on system efficiency;

[0092] β 1 is the adjustment coefficient for environmental parameter fluctuations, which is set to 1.2 to increase the sensitivity to environmental fluctuations and adjust according to the severity of environmental parameter changes;

[0093] β 2 is the smoothing coefficient of the time span, which is set to 0.8 and is used to balance the calculation results of different time intervals and is adjusted with the length of the monitoring period;

[0094] Substitute the parameters into the formula for calculation:

[0095] Calculate the absolute value of the weighted difference: |α 1·L-α 2 ·E|=|0.6×0.15-0.4×0.10|=|0.09-0.04|=|0.05|=0.05;

[0096] Calculate the square root of the denominator: β 1 |P|+β 2 ·T=1.2×5+0.8×60=6+48=54;

[0097] Calculate the rate of change R:

[0098] The result R≈0.0068 shows that the weighted change rate of the system during the monitoring period is 0.0068. This value is used to evaluate the comprehensive fluctuation degree of load, energy consumption rate and environmental parameters. The larger the value, the more significant the fluctuation, indicating the existence of abnormal points.

[0099] Specifically, if Figure 2 , 5 As shown, the operating parameter optimization module includes:

[0100] The offset interval calculation submodule calculates and classifies the load and energy consumption rate offsets based on the abnormal operation characteristic data, calibrates the upper and lower limits and thresholds, and establishes an offset parameter interval table;

[0101] By analyzing the abnormal operation characteristic data, the offset of load and energy consumption rate is divided into different characteristic types. The calculation of the offset is based on the deviation between the actual load value and the historical load average during the operation cycle. At the same time, the difference between the actual value of the energy consumption rate and the theoretical energy consumption rate is superimposed and calculated, and the specific values ​​of the positive offset and the negative offset are respectively calculated. The offset obtained by the above calculation is classified according to the time period and operating conditions, and is divided into three categories: high load offset, medium load offset and low load offset. After classification, based on the offset distribution characteristics of each category, the upper and lower limits and warning thresholds of the offset are calibrated one by one, and the offset that exceeds the warning range is recorded separately. The environmental parameters (such as temperature, humidity, etc.) are associated with the offset characteristics, and the offset parameter interval table is further established. The table covers the upper and lower limits of each load interval, the average offset, the abnormal offset mark and the distribution range of related environmental parameters, providing an accurate reference for subsequent analysis.

[0102] The parameter combination induction submodule compares and measures the classification parameters based on the offset parameter interval table, analyzes the load interval parameter offset trend, calculates the matching and summarizes the groups based on the energy consumption fluctuation amplitude, and generates an adaptation parameter combination table;

[0103] According to the upper and lower limits and offset distribution characteristics in the offset parameter interval table, the classified parameters are selected for comparative measurement. By calculating the proportion of the offset amplitude of each classified parameter, the changing trend in different load intervals is analyzed. The parameter combination in each load interval is combined with the energy consumption rate fluctuation amplitude, and the matching degree between each parameter combination and the actual energy consumption fluctuation amplitude is calculated one by one. The parameter groups with higher adaptability are extracted and summarized according to the trend. When summarizing, each parameter combination is divided into three levels: high, medium and low according to adaptability. The combination characteristics of different levels are further refined, the environmental conditions and offset trend characteristics are classified, and the dynamic characteristics in the load interval are combined to perform cross-interval parameter matching tests to generate an adaptive parameter combination table covering each interval. The table records the adaptability score, environmental parameter correlation range and load offset correspondence of each parameter combination, laying the foundation for screening and optimizing the combination.

[0104] The optimization combination screening submodule performs load range and energy consumption rate distribution fitting analysis on the parameter combination based on the adaptive parameter combination table, screens the best adaptability combination and groups it according to the working condition data, sorts out the tag association characteristics, and generates the equipment operation optimization parameter group;

[0105] Based on the adaptation parameter combination table, a fitting analysis is performed on the load range and energy consumption rate distribution of each parameter combination. The degree of fit is calculated based on the degree of agreement between the actual energy consumption rate and the theoretical energy consumption rate within the load range. The parameter combinations with higher degrees of fit are screened as preliminary optimized combinations. The preliminary optimized combinations are further grouped in combination with the operating condition data. By matching the equipment operating condition characteristics in the historical operating data, the applicability of the parameter combination in different operating scenarios is analyzed, and the differences between the combinations are annotated. The correlation characteristics of each parameter combination are sorted and marked, including environmental parameter association, load offset range, energy consumption rate matching, etc., to generate an equipment operation optimization parameter group to guide the parameter setting of the actual equipment operation, ensuring that the equipment can achieve efficient and stable energy consumption management in a variety of operating scenarios.

[0106] Specifically, if Figure 2 , 6 As shown, the equipment state balance control module includes:

[0107] The load ratio calculation submodule extracts the equipment operation load data and energy consumption rate based on the equipment operation optimization parameter group, matches the load and energy consumption distribution ratio, normalizes the load ratio interval and calibrates the distribution range data, and generates the equipment load distribution ratio table;

[0108] According to the key parameters in the equipment operation optimization parameter group, the load data and energy consumption rate within the equipment operation cycle are gradually extracted, and the distribution ratio of the load and the corresponding energy consumption rate of each device is calculated by pair-by-pair matching. To ensure the rationality of the distribution ratio, the calculated load and energy consumption rate data are classified and processed within the same time span, and the load and energy consumption ratio is divided into three ranges of high, medium and low according to the interval of the operation cycle. The upper and lower limits and the average distribution ratio are calculated respectively. For the distribution ratio of different equipment, the normalization processing method is adopted to uniformly map the load proportion of all equipment to the interval of 0 to 1, and at the same time, the load proportion range boundary of each equipment is calibrated, and the operating condition data marked in the optimization parameter group are combined to confirm the rationality of the load and energy consumption distribution, and finally generate the equipment load distribution ratio table, which records in detail the upper and lower limits of the load proportion of each equipment, the normalized ratio value and the operating condition adaptation range, and provides data support for the subsequent load trend analysis.

[0109] The load trend analysis submodule compares the distribution ratios between devices based on the equipment load distribution ratio table, extracts the load distribution deviation data and measures the impact of the load between devices, analyzes the distribution characteristics of the deviation on the operation stability, classifies the load distribution fluctuation trend and integrates the change records, and establishes the load change trend table between devices;

[0110] Combined with the data in the equipment load distribution ratio table, a horizontal comparison is made for the load distribution ratios of different equipment. By measuring the load distribution deviation of each device in the same period, the differences in load distribution between devices are identified. Based on the deviation value, the impact of load distribution between devices on operational stability is further calculated. For example, the deviation amplitude between high-load devices and low-load devices is correlated with the overall operational stability fluctuation rate, and the influencing characteristics of load distribution are extracted. The deviation characteristics are classified and sorted according to equipment type, operating conditions and load range, the load distribution fluctuation trend is analyzed, and the load change characteristics within the continuous operation cycle are extracted. The load change trend table between devices is integrated to generate a trend table. The trend table records the fluctuation law, deviation amplitude and corresponding operating conditions of the load distribution between devices, providing basic data for regulating high-frequency fluctuations.

[0111] The high-frequency fluctuation control submodule extracts high-frequency fluctuation parameters and analyzes the impact of load offset based on the load change trend table between devices, calculates the load control parameters in the high-frequency fluctuation area, adjusts the distribution parameters to the adaptation interval, and reorganizes the load distribution relationship to obtain the equipment balance control data set;

[0112] Calculate the load control parameters in the high-frequency fluctuation area according to the formula

[0113]

[0114] Among them, P adjrepresents the load control parameter in the high-frequency fluctuation area, w i Represents the high-frequency fluctuation weight coefficient, L i represents the load value of the device at the i-th moment, μ represents the overall mean value of the device load, and α j Indicates the low-frequency fluctuation adjustment coefficient, F j represents the characteristic frequency of the jth low-frequency fluctuation between devices, F base represents the base frequency, n represents the total number of moments, and m represents the total number of low-frequency fluctuations;

[0115] Detailed explanation of the formula and the process of formula calculation and derivation:

[0116] The formula is used to calculate the load control parameters to balance the equipment load distribution and analyze the control parameters in the high-frequency fluctuation area. The load parameter values ​​are calculated by weight fitting of fluctuations of different frequencies and quantifying the fluctuation amplitude to generate a control data set.

[0117] Parameter meaning and setting value:

[0118] Parameter w i , represents the high-frequency fluctuation weight coefficient, which is used to give different high-frequency signal fluctuations different priorities. This parameter is set by collecting the amplitude variation range of the high-frequency fluctuation of the equipment load, and is based on the standardized weight of the high-frequency fluctuation amplitude of the equipment load. In the load change, when the amplitude variation range is 10 to 100 units, the weight is set to a linear mapping value between 0.1 and 1. For example, if the amplitude of a device load change is 50, the corresponding weight w i =0.5;

[0119] Parameter L i , represents the load value of the device at the i-th moment, in kilowatts. Data is collected by real-time monitoring of the device operating load. For example, the observed values ​​of the device load at time 1 to time 5 are 60 kilowatts, 70 kilowatts, 80 kilowatts, 75 kilowatts, and 65 kilowatts, respectively.

[0120] The parameter μ represents the overall mean of the equipment load in kilowatts. The mean is calculated by the observed load values. For example, if the observed data is 60, 70, 80, 75, and 65 kilowatts, the mean is calculated as:

[0121]

[0122] Parameter α j , represents the low-frequency fluctuation adjustment coefficient, which is used to reflect the control priority of low-frequency fluctuations. It is adjusted and calculated according to the standard deviation of low-frequency fluctuations. For example, in the range of 5 to 50 units of low-frequency fluctuation standard deviation, the adjustment coefficient is set to a linear mapping value between 0.2 and 2. For example, if the standard deviation of a fluctuation is 30, the corresponding adjustment coefficient α j =1.2;

[0123] Parameter F j , represents the characteristic frequency of the jth low-frequency fluctuation between devices, in Hertz. The low-frequency fluctuation characteristics of the devices are obtained through Fourier transform, and the data values ​​are 10 Hz, 15 Hz, and 20 Hz respectively;

[0124] Parameter F base , represents the base frequency, in Hertz, which is determined by the rated operating frequency of the equipment, and the base frequency is 10 Hertz;

[0125] The observation data are as follows:

[0126] High frequency fluctuation weight coefficient: w 1 =0.4,w 2 =0.5,w 3 =0.3,w 4 =0.6,w 5 =0.5;

[0127] Equipment load value: L 1 =60,L 2 =70,L 3 =80,L 4 =75,L 5 =65;

[0128] Load average: μ=70;

[0129] Low frequency fluctuation adjustment coefficient: α 1 =0.8,α 2 =1.2,α 3 =1.5;

[0130] Low frequency fluctuation characteristic frequency: F 1 =10,F 2 =15,F 3 =20;

[0131] Reference frequency: F base =10;

[0132] Calculate the numerator part, calculate the absolute value of the fluctuation amplitude at each moment and multiply it by the corresponding weight:

[0133] w 1 ·|L 1 -μ|=0.4·|60-70|=0.4·10=4;

[0134] w 2 ·|L 2 -μ|=0.5·|70-70|=0.5·0=0;

[0135] w 3 ·|L 3-μ|=0.3·|80-70|=0.3·10=3;

[0136] w 4 ·|L 4 -μ|=0.6·|75-70|=0.6·5=3;

[0137] w 5 ·|L 5 -μ|=0.5·|65-70|=0.5·5=2.5;

[0138] Numerator total: 4+0+3+3+2.5=12.5;

[0139] Denominator calculation, calculate each low-frequency fluctuation adjustment term and sum them:

[0140] α 1 ·(F 1 -F base ) 2 =0.8·(10-10) 2 =0.8·0=0;

[0141] α 2 ·(F 2 -F base ) 2 =1.2·(15-10) 2 =1.2·25=30;

[0142] α 3 ·(F 3 -F base ) 2 =1.5·(20-10) 2 =1.5·100=150;

[0143] Denominator Total:

[0144] Substituting the numerator and denominator results into the formula:

[0145] The results show that the load control parameter is 0.93, which represents the control effect value of the equipment under the combined effect of high-frequency fluctuations and low-frequency fluctuations. This result can be used to adjust the load distribution relationship, optimize the balanced operating conditions of the equipment, and help to achieve the overall load optimization distribution strategy.

[0146] Specifically, if Figure 2 , 7 As shown, the equipment operation safety constraint verification module includes:

[0147] The out-of-range parameter extraction submodule compares the load balancing parameters with the operating safety range threshold based on the equipment balance control data set, extracts and classifies the load parameters that exceed the threshold, selects the parameters corresponding to the operating scenarios, and establishes the load out-of-range parameter set;

[0148] Key load balancing parameters are extracted from the equipment balance control data set, and compared with the operating safety range thresholds one by one. The load parameters that exceed the safety threshold range are accurately calibrated. By analyzing the upper and lower limits of the load parameters, the variation range and the operating status of the associated equipment, the over-threshold parameters are classified and processed by type, such as high load over-range parameters, medium load over-range parameters and low load over-range parameters. In the classification process, the distribution law of over-threshold parameters in specific operating scenarios is screened in combination with the historical records of equipment operation and real-time monitoring data. The load parameters are further associated with the actual environmental conditions (such as the external temperature and humidity of the equipment) for classification and sorting. The screened and classified parameters are integrated to establish a load over-range parameter set, which clearly records the load value, corresponding operating scenario, over-range amplitude and associated environmental conditions of each over-range parameter, providing complete basic data for subsequent offset risk analysis.

[0149] The offset risk analysis submodule matches the equipment dynamic energy consumption relationship parameters based on the load out-of-range parameter set, analyzes the load and dynamic energy consumption offset amplitude, determines the offset area range and compares the energy consumption change trend, and generates a set of potential offset risk areas;

[0150] By matching the load values ​​in the load out-of-range parameter set with the dynamic energy consumption relationship parameters of the equipment, the dynamic offset amplitude of the out-of-range parameters is analyzed one by one. The offset amplitude is calculated based on the difference between the actual load value of the equipment and the standard load value in the dynamic energy consumption model. Combined with the change frequency and fluctuation amplitude of the out-of-range parameters during the operation cycle, the upper and lower limits of the offset amplitude are calibrated. While calculating, a detailed analysis is conducted on the energy consumption change trend caused by the out-of-range load parameters, such as the situation where high load out-of-range parameters lead to an abnormal increase in the energy consumption rate. Combined with the historical data records of the equipment, the scope of the potential offset risk area is determined. By further classifying the risk areas, the offset characteristics are grouped and sorted according to the load type and fluctuation characteristics to generate a set of potential offset risk areas. The offset amplitude, energy consumption fluctuation trend and operation impact of each risk area are clearly marked to provide targeted reference for correction and optimization work.

[0151] The offset correction adjustment submodule matches the balance control parameter values ​​based on the potential offset risk area set, adjusts the parameters in the offset area and analyzes the equipment balance relationship to obtain the equipment offset correction parameter set;

[0152] Combined with the offset amplitude and dynamic energy consumption trend characteristics in the set of potential offset risk areas, the existing control parameter values ​​in the balance control data set are matched, and the key parameters in the offset area are gradually adjusted. According to the relationship between the actual load value and the energy consumption rate in the offset area, the load adjustment amplitude is calculated, and optimized layer by layer according to the control priority to ensure that the load in the offset area gradually moves closer to the balance range. Combined with the real-time operating data of the equipment, the impact of parameter adjustment on the balance relationship of the equipment is re-evaluated, and the corrected equipment operating status is analyzed to see whether it meets the balance requirements. Finally, the adjusted correction parameters are included in the equipment offset correction parameter set, which records the adjustment amplitude, control target value and balance relationship changes between devices in each offset area, providing accurate correction basis for equipment load offset management in smart parks and ensuring that the distribution of energy consumption and load reaches the optimal state.

[0153] Specifically, if Figure 2 , 8 As shown, the equipment collaborative operation execution module includes:

[0154] The operating parameter combination submodule extracts the equipment operating parameters based on the equipment offset correction parameter set and groups them for matching, combines the load parameters with the operating optimization parameters, sorts out the combination relationship under the operating scenario, and generates a combination set of multi-equipment operating parameters;

[0155] The real-time operating parameters of the equipment are extracted from the equipment offset correction parameter set, including key data such as load status, energy consumption rate, and control range. The parameters are grouped and matched based on the equipment operating characteristics and adjustment requirements. During the grouping process, the load parameters of the equipment are classified according to the characteristics in different operating scenarios, such as high-load scenarios, medium-load scenarios, and low-load scenarios, and combined with the operating optimization parameters to form a more targeted parameter combination. The interactive relationship between the load parameters and the operating optimization parameters is gradually analyzed, such as the fluctuation range and parameter distribution law of the equipment energy consumption rate under high load conditions, and summarized into specific operating scenarios. By combing the linkage characteristics and load distribution laws between devices, the parameter combination relationships under various operating scenarios are sorted out to generate a multi-device operating parameter combination set. This combination set not only covers the load distribution characteristics between devices, but also clearly marks the operating scenarios to which each set of parameters is applicable, providing accurate basic data for equipment linkage regulation and load balancing management.

[0156] The path control analysis submodule is based on the combination set of multi-device operation parameters, analyzes the changes in the operation status of the combination parameters, analyzes the dynamic characteristics and change range, extracts the operation path parameter nodes and allocates operation and maintenance parameters, organizes the path control data, and generates the device operation path control parameter set;

[0157] By analyzing the parameter operation status recorded in the combination of multiple device operation parameters, parsing the dynamic characteristics of parameter changes over time and load, extracting the range of changes in different operation scenarios, and identifying the key nodes on the operation path according to the matching relationship between load parameters and operation status, including the peak points and valley points of load fluctuations of equipment in different time periods, the path parameter nodes are classified and sorted one by one, and the operation and maintenance parameters of each node are allocated, such as the load adjustment amplitude of the path node, the corresponding energy consumption rate optimization value and the operating environment adaptation conditions, etc. In the process of sorting out the path control data, the dynamic correlation between the path nodes is analyzed in combination with the correlation characteristics between devices, and the operation parameters of adjacent nodes are gradually summarized into the same control path to ensure the continuity and optimization of the operation status between paths. Finally, a device operation path control parameter set is generated. This parameter set clarifies the load, energy consumption distribution and dynamic adjustment requirements of the operation path nodes, providing systematic data support for the path control of smart park equipment operation.

[0158] The optimization and control submodule analyzes the operation requirements of public energy-consuming equipment based on the equipment operation path control parameter set, matches the node load parameters with the energy demand parameters, extracts the global distribution range of the path load and dynamically adjusts the parameters, summarizes the operation data, and generates a global control plan for public energy-consuming equipment;

[0159] Combined with the key node information marked in the equipment operation path control parameters, the load distribution characteristics of public energy-consuming equipment under different operation requirements are gradually analyzed, and the node load parameters are matched with the corresponding energy consumption demand parameters. According to the global allocation requirements of the operation path, the upper and lower limits of the load parameters are extracted, and the difference between the load and energy consumption demand of each node in the path is gradually calculated. Combined with the correlation characteristics between the nodes, the path load parameters are dynamically adjusted. Through the optimization and adjustment of the global allocation range, the load distribution ratio is ensured to meet the equipment operation requirements while achieving balanced management of the overall energy consumption. The adjusted operation data is summarized, including the load distribution value, energy consumption rate and dynamic adjustment range of the path nodes, and a global control plan for public energy-consuming equipment is formed. The plan clarifies the load distribution relationship between equipment, the path control range and dynamic optimization measures, and provides efficient and reliable control strategies for the refined management of smart park equipment operation and maintenance.

[0160] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A public energy-consuming equipment operation and maintenance management system suitable for a smart park, characterized in that: The system comprises: The equipment operation status perception module compares the load and energy consumption time series based on the equipment operation load, energy consumption rate and operating environment parameters, extracts the change trend, groups and sorts the fluctuation range, and obtains the equipment operation fluctuation trend data; The energy consumption characteristic dynamic analysis module analyzes the changes in load, energy consumption and environmental parameters based on the equipment operation fluctuation trend data, calculates trends, sorts out key fluctuation intervals, screens abnormal points, and obtains abnormal operation characteristic data; The operation parameter optimization module extracts the change interval in the abnormal operation characteristic data, calibrates the offset threshold, groups the parameter set, combines the working condition fitting calculation, and generates the equipment operation optimization parameter group; The equipment state balance control module calculates the load and energy consumption distribution ratio based on the equipment operation optimization parameter group, analyzes the equipment impact, and obtains the equipment balance control data set; The equipment operation safety constraint verification module performs threshold comparison on the load balancing parameters of the balancing control data based on the equipment balancing control data set, screens and reorganizes the out-of-range parameters, analyzes the verification data, and obtains the equipment offset correction parameter set; The equipment collaborative operation execution module maps the operation parameters of multiple equipment based on the equipment offset correction parameter set, dynamically groups loads and operation optimization parameters, refines and allocates operation and maintenance parameters, and generates a global control plan for common energy-consuming equipment.

2. The public energy-consuming equipment operation and maintenance management system applicable to a smart park according to claim 1 is characterized in that: The equipment operation fluctuation trend data includes load change trend, energy consumption change trend and environmental parameter fluctuation range, the abnormal operation characteristic data includes the associated change range of load and energy consumption rate, key fluctuation interval and characteristic abnormal area, the equipment operation optimization parameter group includes parameter offset relationship, threshold calibration result and adaptation parameter combination, the equipment balance control data set includes load distribution ratio, load change trend between equipment and balance control parameters of high-frequency change items, the equipment offset correction parameter set includes out-of-range parameters, potential offset risk area and risk parameter adjustment value, and the global control scheme for common energy-consuming equipment includes collaborative operation change trend, control path allocation parameters and common energy-consuming equipment requirements.

3. The public energy-consuming equipment operation and maintenance management system applicable to a smart park according to claim 1 is characterized in that: The device operation status perception module includes: The time series association submodule extracts the time series data of the equipment operation load and energy consumption rate based on the equipment operation load, energy consumption rate and operation environment parameters, matches the operation environment parameters with the load and energy consumption rate data through timestamps, and establishes an associated time series; The change trend analysis submodule extracts the numerical range of load and energy consumption rate in each time period based on the associated time series, determines the load change trend by point-by-point comparison, divides the load fluctuation interval to count the energy consumption rate change amplitude, extracts matching operating environment parameters, and obtains load and energy consumption change trend data; The fluctuation trend sorting submodule compares and analyzes the operating environment parameters and energy consumption rate based on the load and energy consumption change trend data in combination with the load fluctuation range, counts the fluctuation range of each group of energy consumption rate and associates it with the load change trend, sorts the fluctuation range and trend classification data, and obtains the equipment operation fluctuation trend data.

4. The public energy-consuming equipment operation and maintenance management system applicable to a smart park according to claim 1 is characterized in that: The energy consumption characteristics dynamic analysis module includes: The variation range calculation submodule matches and calculates the load and energy consumption rate value range according to the timestamp based on the equipment operation fluctuation trend data, processes the environmental parameter classification, and generates a correlation variation range table; The dynamic feature summary submodule collects statistics on the distribution of environmental parameters and calculates trends according to the load change interval based on the associated change range table, summarizes the corresponding relationship between the load and the energy consumption rate, and obtains a dynamic feature trend table; Based on the dynamic characteristic trend table, the abnormal point screening submodule calculates the weighted change rate within the monitoring period, analyzes the change amplitude and screens the abnormal points, locates the fluctuation range where the abnormal points are located, and classifies and marks the regional characteristics to obtain abnormal operation characteristic data.

5. The public energy-consuming equipment operation and maintenance management system applicable to a smart park according to claim 4 is characterized in that: Monitor the weighted rate of change during the period, according to the formula: Among them, R represents the weighted change rate within the monitoring period, L represents the absolute change of the load value, E represents the absolute change of the energy consumption rate, P represents the absolute change of the environmental parameter, T represents the interval span of the change within the time period, α1 is the weight coefficient of the load change to the overall fluctuation amplitude, α2 is the weight coefficient of the energy consumption rate change to the overall fluctuation amplitude, β1 is the fluctuation adjustment coefficient of the environmental parameter, and β2 is the smoothing coefficient of the time span.

6. The public energy-consuming equipment operation and maintenance management system applicable to a smart park according to claim 1 is characterized in that: The operating parameter optimization module includes: The offset interval calculation submodule calculates and classifies the load and energy consumption rate offsets based on the abnormal operation characteristic data, calibrates the upper and lower limits and thresholds, and establishes an offset parameter interval table; The parameter combination induction submodule compares and measures the classification parameters based on the offset parameter interval table, analyzes the load interval parameter offset trend, calculates the matching and summarizes the groups in combination with the energy consumption fluctuation amplitude, and generates an adaptation parameter combination table; The optimization combination screening submodule performs load range and energy consumption rate distribution fitting analysis on the parameter combination based on the adaptation parameter combination table, screens the best adaptability combination and combines the working condition data grouping, sorts out the tag association characteristics, and generates the equipment operation optimization parameter group.

7. The public energy-consuming equipment operation and maintenance management system applicable to a smart park according to claim 1 is characterized in that: The equipment state balance control module includes: The load ratio calculation submodule extracts the equipment operation load data and energy consumption rate based on the equipment operation optimization parameter group, matches the load and energy consumption distribution ratio, normalizes the load proportion interval and calibrates the distribution range data, and generates an equipment load distribution ratio table; The load trend analysis submodule compares the distribution ratios between devices based on the equipment load distribution ratio table, extracts the load distribution deviation data and measures the load impact between devices, analyzes the distribution characteristics of the deviation on the operation stability, classifies the load distribution fluctuation trend and integrates the change records, and establishes the load change trend table between devices; The high-frequency fluctuation control submodule extracts high-frequency fluctuation parameters and analyzes the impact of load offset based on the load change trend table between the devices, calculates the load control parameters in the high-frequency fluctuation area, adjusts the distribution parameters to the adaptation interval, and reorganizes the load distribution relationship to obtain the equipment balance control data set.

8. The public energy-consuming equipment operation and maintenance management system applicable to a smart park according to claim 7 is characterized in that: Calculate the load control parameters in the high-frequency fluctuation area according to the formula Among them, P adj represents the load control parameter in the high-frequency fluctuation area, w i Represents the high-frequency fluctuation weight coefficient, L i represents the load value of the device at the i-th moment, μ represents the overall mean value of the device load, and α j Indicates the low-frequency fluctuation adjustment coefficient, F j represents the characteristic frequency of the jth low-frequency fluctuation between devices, F base represents the base frequency, n represents the total number of moments, and m represents the total number of low-frequency fluctuations.

9. The public energy-consuming equipment operation and maintenance management system applicable to a smart park according to claim 1 is characterized in that: The device operation safety constraint verification module includes: The out-of-range parameter extraction submodule compares the load balancing parameters with the operating safety range threshold based on the equipment balance control data set, extracts and classifies the over-threshold load parameters, selects the corresponding operating scenarios of the parameters, and establishes a load out-of-range parameter set; The offset risk analysis submodule matches the equipment dynamic energy consumption relationship parameters based on the load out-of-range parameter set, analyzes the load and dynamic energy consumption offset amplitude, determines the offset area range and compares the energy consumption change trend, and generates a potential offset risk area set; The offset correction adjustment submodule matches the balance control parameter value based on the potential offset risk area set, adjusts the parameters in the offset area and analyzes the equipment balance relationship to obtain the equipment offset correction parameter set.

10. The public energy-consuming equipment operation and maintenance management system applicable to a smart park according to claim 1, characterized in that: The equipment collaborative operation execution module includes: The operating parameter combination submodule extracts the equipment operating parameters based on the equipment offset correction parameter set and groups them for matching, combines the load parameters with the operating optimization parameters, sorts out the combination relationship under the operating scenario, and generates a multi-equipment operating parameter combination set; The path control analysis submodule analyzes the change of the combined parameter operation state based on the multi-device operation parameter combination set, analyzes the dynamic characteristics and change range, extracts the operation path parameter nodes and allocates operation and maintenance parameters, organizes the path control data, and generates the device operation path control parameter set; Based on the equipment operation path control parameter set, the operation requirements of public energy-consuming equipment are analyzed, the node load parameters are matched with the energy consumption demand parameters, the global distribution range of the path load is extracted and the parameters are dynamically adjusted, the operation data is summarized, and a global control plan for public energy-consuming equipment is generated.

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