BI analysis method and system for energy efficiency management and control data

Abstract

The invention relates to the technical field of data analysis, in particular to an energy efficiency management and control data-oriented BI analysis method and system. According to the method, the energy efficiency of the system can be comprehensively evaluated by combining the equipment operation efficiency, the cost structure, the environmental parameters and other dimension data, the limitation of a single view angle is avoided, so that a more accurate and comprehensive energy efficiency analysis result is obtained, key indexes are extracted from the equipment operation efficiency data, and the energy efficiency analysis efficiency is improved. According to the method, the actual energy efficiency of the equipment can be accurately calculated, it is ensured that the energy efficiency analysis result better fits the actual operation condition, equipment performance problems can be found in time, the energy efficiency of the whole system is optimized and improved, the energy consumption of multiple dimensions is monitored in real time, the visual trend chart is generated, and the energy efficiency of the whole system is improved. The system operation state and the energy efficiency change trend can be visually known, the data visualization result is sent to the visualization analysis system in real time, real-time updating and sharing of data can be achieved, and cross-department cooperation and decision making can be promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and particularly to a BI analysis method and system for energy efficiency control data. Background Art

[0002] The BI analysis method for energy efficiency control data refers to analyzing and processing data related to energy efficiency through Business Intelligence (BI) technology, aiming to improve energy use efficiency, reduce energy waste, and optimize energy management. In the prior art, the energy efficiency analysis for energy efficiency control data usually focuses on a single dimension, such as equipment power or energy consumption, while ignoring the comprehensive impact of other dimensions, resulting in a lack of multi-dimensional comprehensive energy efficiency analysis. Summary of the Invention

[0003] The main object of the present invention is to provide a BI analysis method for energy efficiency control data, aiming to solve the technical problems in the prior art.

[0004] The present invention proposes a BI analysis method for energy efficiency control data, including: Obtaining a plurality of multi-dimensional real-time data of a target object within a preset time period, wherein the multi-dimensional real-time data includes equipment operation efficiency data, cost structure data, and environmental parameter data; Obtaining equipment power, operation duration, and fault characteristics according to each piece of the equipment operation efficiency data, and obtaining the corresponding actual equipment operation energy efficiency according to each piece of the equipment power, operation duration, and fault characteristics; Obtaining energy procurement cost, equipment maintenance cost, carbon emission cost, and manual management cost according to each piece of the cost structure data, and obtaining the corresponding actual cost energy efficiency according to each piece of the energy procurement cost, equipment maintenance cost, carbon emission cost, and manual management cost; Obtaining refrigeration energy consumption, dehumidification energy consumption, light intensity, and ventilation energy consumption according to each piece of the environmental parameter data, and obtaining the corresponding actual environmental energy efficiency according to the refrigeration energy consumption, dehumidification energy consumption, light intensity, and ventilation energy consumption; Obtaining the corresponding energy consumption according to each piece of the actual environmental energy efficiency, actual cost energy efficiency, and actual equipment operation energy efficiency; Generating a visual trend change graph according to the plurality of energy consumptions, and sending the visual trend change graph to a visual analysis system for display.

[0005] Preferably, the step of obtaining the corresponding actual equipment operation energy efficiency according to each piece of the equipment power, operation duration, and fault characteristics includes: Obtaining the load rate and no-load rate according to the equipment operation efficiency data, and obtaining the actual equipment power according to the load rate and the equipment power; Obtain the actual operating duration based on the stated operating duration and no-load rate, and obtain the first actual energy consumption based on the actual operating duration and the actual equipment power; Obtain the load threshold, and obtain the energy consumption penalty coefficient based on the load rate and the load threshold; Obtain the second actual energy consumption based on the energy consumption penalty coefficient and the first actual energy consumption; Obtain the total fault energy consumption based on the fault characteristics, and obtain the actual equipment operating energy efficiency based on the second actual energy consumption and the total fault energy consumption.

[0006] Preferably, the step of obtaining the total fault energy consumption based on the fault characteristics includes: Obtain the number of faults, the energy consumption of the replacement equipment, the instantaneous restart power, and the restart duration based on the fault characteristics, and obtain the restart surge energy consumption based on the instantaneous restart power and the restart duration; Obtain the fault time parameters based on the fault characteristics, where the fault time parameters include the fault warning time, the fault occurrence time, and the latest maintenance start time; Obtain the response time buffer based on the fault occurrence time and the latest maintenance start time; Obtain the deterioration amplitude coefficient and the deterioration rate coefficient based on the fault characteristics, and obtain the equipment deterioration degree index based on the deterioration rate coefficient and the fault warning time; Obtain the additional power consumption index based on the equipment deterioration degree index and the deterioration amplitude coefficient; Obtain the reference power of the target object, and obtain the additional power consumption based on the reference power and the additional power consumption index; Obtain the pre-fault performance degradation energy consumption based on the additional power consumption, the fault warning time, the fault occurrence time, and the response time buffer, and obtain the total fault energy consumption based on the number of faults, the energy consumption of the replacement equipment, the restart surge energy consumption, and the pre-fault performance degradation energy consumption.

[0007] Preferably, the step of obtaining the corresponding actual cost energy efficiency based on each of the energy procurement cost, the equipment maintenance cost, the carbon emission cost, and the labor management cost includes: Obtain the total cost based on the energy procurement cost, the equipment maintenance cost, the carbon emission cost, and the labor management cost; Obtain the total energy consumption of the target object, and obtain the unit energy cost based on the total energy consumption and the total cost; Obtain the carbon emission intensity based on the carbon emission cost and the total energy consumption; Obtain the effective output of the equipment of the target object, and obtain the equipment maintenance efficiency ratio based on the effective output of the equipment and the equipment maintenance cost; Obtain the historical energy consumption data of the target object, and obtain the benchmark average energy consumption and the actual average energy consumption according to the historical energy consumption data; Obtain the energy savings according to the benchmark average energy consumption and the actual average energy consumption, and obtain the management efficiency index according to the energy savings and the manual management cost; Obtain the comprehensive energy efficiency cost index according to the management efficiency index, the equipment maintenance efficiency ratio, the carbon emission intensity, and the unit energy cost.

[0008] Preferably, the step of obtaining the corresponding actual environmental energy efficiency according to the refrigeration energy consumption, the dehumidification energy consumption, the light intensity, and the ventilation energy consumption includes: Obtain the refrigeration capacity through a temperature sensor, and obtain the refrigeration energy efficiency ratio according to the refrigeration capacity and the refrigeration energy consumption; Obtain the dehumidification amount through a humidity sensor, and obtain the dehumidification energy efficiency ratio according to the dehumidification amount and the dehumidification energy consumption; Obtain the ventilation volume through a wind speed sensor, and obtain the ventilation energy efficiency ratio according to the ventilation volume and the ventilation energy consumption; Obtain the maximum allowable light intensity according to the light intensity, and obtain the natural light utilization rate according to the maximum allowable light intensity and the light intensity; Obtain the actual environmental energy efficiency according to the natural light utilization rate, the ventilation energy efficiency ratio, the dehumidification energy efficiency ratio, and the refrigeration energy efficiency ratio.

[0009] Preferably, the step of generating a visualization trend change graph according to the multiple energy consumptions includes: Obtain the corresponding time point according to each energy consumption; Establish a time - energy consumption coordinate axis with the time point as the X - axis and the energy consumption as the Y - axis; Plot the energy consumption corresponding to each time point as a connection point on the time - energy consumption coordinate axis; Connect the multiple connection points in sequence with broken lines to obtain the visualization trend change graph.

[0010] This application also provides a BI analysis system for energy efficiency control data, including: A first acquisition module, configured to acquire a plurality of multi - dimensional real - time data of a target object within a preset time period, where the multi - dimensional real - time data includes equipment operation efficiency data, cost structure data, and environmental parameter data; A second acquisition module, configured to obtain the equipment power, operation duration, and fault characteristics according to each equipment operation efficiency data, and obtain the corresponding actual equipment operation energy efficiency according to each equipment power, operation duration, and fault characteristics; A third acquisition module, configured to obtain energy procurement costs, equipment maintenance costs, carbon emission costs, and labor management costs according to each of the cost structure data, and obtain corresponding actual cost energy efficiencies according to each of the energy procurement costs, equipment maintenance costs, carbon emission costs, and labor management costs; A fourth acquisition module, configured to obtain refrigeration energy consumption, dehumidification energy consumption, light intensity, and ventilation energy consumption according to each of the environmental parameter data, and obtain corresponding actual environmental energy efficiencies according to the refrigeration energy consumption, dehumidification energy consumption, light intensity, and ventilation energy consumption; A fifth acquisition module, configured to obtain corresponding energy consumption amounts according to each of the actual environmental energy efficiencies, actual cost energy efficiencies, and actual equipment operation energy efficiencies; A generation module, configured to generate a visual trend change graph according to multiple of the energy consumption amounts, and send the visual trend change graph to a visual analysis system for display.

[0011] Preferably, the second acquisition module includes: A first acquisition unit, configured to obtain a load rate and an idle rate according to the equipment operation efficiency data, and obtain an actual equipment power according to the load rate and the equipment power; A second acquisition unit, configured to obtain an actual operation duration according to the operation duration and the idle rate, and obtain a first actual energy consumption according to the actual operation duration and the actual equipment power; A third acquisition unit, configured to obtain a load threshold, and obtain an energy consumption penalty coefficient according to the load rate and the load threshold; A fourth acquisition unit, configured to obtain a second actual energy consumption according to the energy consumption penalty coefficient and the first actual energy consumption; A fifth acquisition unit, configured to obtain a total fault energy consumption according to the fault characteristics, and obtain an actual equipment operation energy efficiency according to the second actual energy consumption and the total fault energy consumption.

[0012] The present invention also provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the above BI analysis method for energy efficiency control data are implemented.

[0013] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above BI analysis method for energy efficiency control data are implemented.

[0014] The beneficial effects of the present invention are as follows: By combining data from multiple dimensions such as the operating efficiency of equipment, cost structure, and environmental parameters, the present invention can comprehensively evaluate the energy efficiency of the system, avoiding the limitations of a single perspective, thereby obtaining more accurate and comprehensive energy efficiency analysis results. By extracting these key indicators from the equipment operating efficiency data, the actual energy efficiency of the equipment can be accurately calculated, ensuring that the energy efficiency analysis results are more in line with the actual operating conditions, helping to promptly discover equipment performance problems and optimize them, and improving the overall energy efficiency of the system. By comprehensively considering multiple cost factors such as energy procurement costs, equipment maintenance costs, carbon emission costs, and labor management costs, the actual cost energy efficiency can be comprehensively evaluated and calculated, ensuring that the analysis not only focuses on energy consumption but also covers all possible economic and environmental protection costs. By obtaining these environmental parameter data in real time and analyzing their effects on energy efficiency, the role of environmental factors in energy efficiency performance can be revealed, providing a more scientific basis for subsequent optimization plans. By real-time monitoring the energy consumption in multiple dimensions and generating a visual trend chart, it can help managers intuitively understand the system operating status and energy efficiency change trends, and send the data visualization results to the visual analysis system in real time, which can not only achieve real-time update and sharing of data but also promote cross-departmental collaboration and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic flowchart of the method according to an embodiment of the present invention.

[0016] Figure 2 It is a schematic structural diagram of the system according to an embodiment of the present invention.

[0017] Figure 3 It is a schematic internal structure diagram of a computer device according to an embodiment of the present application.

[0018] The realization, functional characteristics, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0020] As Figures 1 - 3 shown, the present application provides a BI analysis method for energy efficiency control data, including: S1. Obtain multiple multi-dimensional real-time data of a target object within a preset time period, where the multi-dimensional real-time data includes equipment operating efficiency data, cost structure data, and environmental parameter data; S2. Obtain the equipment power, operating duration, and fault characteristics according to each piece of equipment operating efficiency data, and obtain the corresponding actual equipment operating energy efficiency according to each piece of equipment power, operating duration, and fault characteristics; S3. Obtain the energy procurement cost, equipment maintenance cost, carbon emission cost, and labor management cost according to each of the cost structure data, and obtain the corresponding actual cost energy efficiency according to each of the energy procurement cost, equipment maintenance cost, carbon emission cost, and labor management cost; S4. Obtain the refrigeration energy consumption, dehumidification energy consumption, light intensity, and ventilation energy consumption according to each of the environmental parameter data, and obtain the corresponding actual environmental energy efficiency according to the refrigeration energy consumption, dehumidification energy consumption, light intensity, and ventilation energy consumption; S5. Obtain the corresponding energy consumption according to each of the actual environmental energy efficiency, actual cost energy efficiency, and actual equipment operation energy efficiency; S6. Generate a visual trend change graph according to the multiple energy consumptions, and send the visual trend change graph to a visual analysis system for display.

[0021] As described in the above steps S1 - S6, the present invention obtains device operation efficiency data, cost structure data, and environmental parameter data of multiple multi - dimensional real - time data of a target object within a preset time period. From each device operation efficiency data, it obtains device power, operation duration, and fault characteristics, and based on each device power, operation duration, and fault characteristics, it obtains the corresponding actual device operation energy efficiency. From each cost structure data, it obtains energy procurement cost, device maintenance cost, carbon emission cost, and labor management cost, and based on each energy procurement cost, device maintenance cost, carbon emission cost, and labor management cost, it obtains the corresponding actual cost energy efficiency. From each environmental parameter data, it obtains refrigeration energy consumption, dehumidification energy consumption, light intensity, and ventilation energy consumption, and based on the refrigeration energy consumption, dehumidification energy consumption, light intensity, and ventilation energy consumption, it obtains the corresponding actual environmental energy efficiency. From each actual environmental energy efficiency, actual cost energy efficiency, and actual device operation energy efficiency, it obtains the corresponding energy consumption. It generates a visual trend change graph from multiple energy consumptions and sends the visual trend change graph to a visual analysis system for display. By comprehensively understanding the power consumption, operation duration, and fault characteristics of the device, the working state of the device can be accurately evaluated, which helps to identify whether there is energy waste in the device or a performance decline caused by long - term operation, so as to perform maintenance in advance, reduce the risk of sudden failures, optimize the energy utilization efficiency of the device life cycle. Combining the operation duration and fault characteristics can more accurately reflect the actual energy efficiency of the device, and further provide a more refined basis for formulating energy - saving optimization plans. Incorporating multiple cost dimensions into the analysis can comprehensively reflect the actual operating expenses of the device. The energy procurement cost and carbon emission cost are directly related to the goals of environmental protection and energy conservation and emission reduction. The device maintenance cost helps to monitor the operating health of the device, while the labor management cost reflects the efficiency of human resource allocation. Considering these factors comprehensively can help enterprises accurately identify the sources of various costs, and thus targetedly conduct cost control and optimization. Integrating comprehensive factors such as energy procurement, device maintenance, carbon emission, and labor management costs can obtain a more comprehensive "cost - benefit ratio", which helps enterprises more accurately judge which costs can be controlled or optimized when optimizing energy - saving plans, and thus improve the overall operating efficiency. Environmental factors (such as temperature, humidity, light, and ventilation) have a significant impact on energy consumption. For example, the energy consumption of refrigeration and dehumidification is closely related to the environmental temperature and humidity, and the light intensity and ventilation effect also affect the operating load of devices such as air conditioners. Considering these environmental parameters in the energy efficiency analysis can more accurately evaluate the impact of the actual environment on energy efficiency, so as to achieve more precise energy management and optimization. Combining the actual environmental energy efficiency data helps to better utilize natural resources, reduce the dependence on artificial control systems, and thus reduce the total energy consumption. Analyzing the energy consumption from a single dimension often cannot comprehensively reflect the energy usage situation, while comprehensively considering the data of the three dimensions of device, cost, and environment can help to more comprehensively evaluate the overall energy consumption. This can not only reduce energy waste,It can also help to identify potential optimization spaces and improve the overall efficiency of energy use. Through dynamic trend charts, users can intuitively see the fluctuations in energy efficiency and make adjustments promptly. In addition, visual charts help to improve communication efficiency among teams and assist various departments in collaborating to solve energy consumption problems. Comprehensive analysis of multi-dimensional data can reveal the complex relationships between equipment energy efficiency and factors such as the environment and cost, thereby better identifying optimization spaces and implementing more precise energy efficiency management and energy-saving measures. Through this comprehensive analysis, one-sided judgments can be avoided and more comprehensive optimization suggestions can be provided. By combining data from multiple dimensions such as equipment operation efficiency, cost structure, and environmental parameters, the energy efficiency of the system can be comprehensively evaluated, avoiding the limitations of a single perspective and thus obtaining more accurate and comprehensive energy efficiency analysis results. The power, operation duration, and fault characteristics of equipment are key factors affecting equipment energy efficiency. By extracting these key indicators from equipment operation efficiency data, the actual energy efficiency of the equipment can be accurately calculated, ensuring that the energy efficiency analysis results are more in line with the actual operation situation. This helps to promptly identify equipment performance problems and optimize them, improving the energy efficiency of the overall system. Multiple cost factors such as energy procurement costs, equipment maintenance costs, carbon emission costs, and manual management costs directly affect the total cost and energy efficiency performance of the system. By comprehensively considering this cost structure data, the actual cost energy efficiency can be comprehensively evaluated and calculated, ensuring that the analysis not only focuses on energy consumption but also covers all possible economic and environmental costs. By obtaining these environmental parameter data in real time and analyzing their effects on energy efficiency, the role of environmental factors in energy efficiency performance can be revealed, providing a more scientific basis for subsequent optimization plans. By real-time monitoring the energy consumption in multiple dimensions and generating visual trend charts, it can help managers intuitively understand the system operation status and energy efficiency change trends. Sending the data visualization results to the visual analysis system in real time can not only achieve real-time update and sharing of data but also promote cross-departmental collaboration and decision-making. By displaying the trend charts in the visualization system, managers can promptly understand the system energy efficiency change situation and make adjustments quickly.,

[0022] In one embodiment, the step S2 of obtaining the corresponding actual equipment operation energy efficiency according to each of the equipment power, operation duration, and fault characteristics includes: S21. Obtain the load rate and no-load rate according to the equipment operation efficiency data, and obtain the actual equipment power according to the product of the load rate and the equipment power; S22. Obtain the actual operation duration according to the operation duration and the no-load rate, and obtain the first actual energy consumption according to the product of the actual operation duration and the actual equipment power; S23. Obtain a load threshold, and calculate an energy consumption penalty coefficient according to the load rate and the load threshold, where the calculation formula is: ; where represents the energy consumption penalty coefficient, represents the shape parameter (which is used to control the steepness of the transition region. The larger the shape parameter, the steeper the transition, and usually takes = 5 - 10), represents the load factor, represents the load threshold; S24. Obtain the second actual energy consumption according to the product of the energy consumption penalty coefficient and the first actual energy consumption; S25. Obtain the total fault energy consumption according to the fault characteristics, and obtain the actual energy efficiency of the device operation according to the difference between the second actual energy consumption and the total fault energy consumption.

[0023] As described in the above steps S21 - S25, the calculation formulas of the energy consumption penalty coefficient all perform normalization processing on the parameters in advance to eliminate the dimensional differences between different variables. The purpose is to ensure that all variables are on the same order of magnitude, so as to make the calculation more stable and effective. The present invention obtains the load rate and no - load rate through the equipment operation efficiency data, obtains the actual equipment power according to the product of the load rate and the equipment power, obtains the actual operation duration through the operation duration and the no - load rate, and obtains the first actual energy consumption according to the product of the actual operation duration and the actual equipment power. By obtaining the load threshold, calculates the energy consumption penalty coefficient according to the load rate and the load threshold, obtains the second actual energy consumption through the product of the energy consumption penalty coefficient and the first actual energy consumption, obtains the total fault energy consumption through the fault characteristics, and obtains the actual equipment operation energy efficiency according to the difference between the second actual energy consumption and the total fault energy consumption. The load rate and the no - load rate directly reflect the operation efficiency of the equipment. A high load rate indicates that the equipment is working efficiently, while a high no - load rate indicates that the equipment is idle or has low work efficiency. By considering these two indicators simultaneously, the overall efficiency of the equipment can be evaluated more comprehensively. According to the real - time data of the load rate and the no - load rate, the management personnel can dynamically adjust the operation strategy of the equipment, optimize the equipment scheduling, reduce energy waste, and improve the utilization rate of the equipment. The power consumption of the equipment is different under different load conditions. By multiplying the load rate and the equipment power, the actual power of the equipment in the current working state can be accurately calculated, avoiding the overly idealized estimation relying only on the rated power of the equipment. This calculation method can more accurately reflect the energy consumption of the equipment under different working conditions, and thus provide more reliable energy efficiency analysis data. The operation duration is usually a key parameter in energy efficiency analysis, but only considering the actual operation duration of the equipment and ignoring the no - load time is likely to lead to deviation in energy efficiency evaluation. By adjusting the actual operation duration through the no - load rate, it can be avoided that the equipment is still calculated as the effective working duration when it is in the no - load state, so as to obtain more real energy efficiency data, which can help identify the no - load time, and then optimize the use and management of the equipment, reduce the energy waste caused by no - load, and improve the energy efficiency performance. Multiplying the operation duration and the actual power can comprehensively calculate the actual energy consumption of the equipment, avoiding errors in a single dimension, so as to improve the accuracy of energy efficiency evaluation. Accurate energy consumption data can help the management personnel analyze the peak and low - efficiency periods of equipment use, contribute to the reasonable arrangement of equipment work plans, and reduce unnecessary energy waste. The load threshold can be used as a reference standard for evaluating whether the equipment is in the high - efficiency working range. When the load rate exceeds a certain critical value, it may cause excessive consumption or loss of the equipment. Calculating the energy consumption penalty coefficient can monitor the working state of the equipment in real time to ensure that the equipment operates within the optimal load range. Through the penalty coefficient mechanism, the equipment is encouraged to work within a reasonable load range, avoiding low - efficiency energy consumption under over - load or too - low load, so as to optimize the overall energy efficiency performance. The energy consumption evaluation considering the energy consumption penalty coefficient not only reflects the energy efficiency performance of the equipment under different loads, but also adds the penalty for abnormal load on energy consumption.This can more accurately reflect the additional energy consumption of the device under abnormal operating conditions. Through the calculation of the penalty coefficient, the unreasonable use of the device can be effectively inhibited, thereby reducing unnecessary energy losses and improving the overall energy use efficiency of the enterprise or system. The fault characteristics can reflect whether the device is in an abnormal working state. Equipment failures often lead to unnecessary increases in energy consumption. By calculating the fault energy consumption separately, potential problems of the device can be detected in a timely manner, providing data support for preventive maintenance. By comparing the difference between the second actual energy consumption and the fault energy consumption, the energy efficiency level of the device during normal operation can be accurately reflected. This difference provides a more realistic evaluation standard for the actual energy efficiency of the device. Through the evaluation of the actual energy efficiency, the root causes of energy waste, such as equipment failures or improper use, can be identified, and then effective energy-saving and optimization measures can be taken.

[0024] In one embodiment, the step S25 of obtaining the total fault energy consumption according to the fault characteristics includes: S251. Obtain the number of faults, the energy consumption of the replacement device, the instantaneous restart power, and the restart duration according to the fault characteristics, and obtain the restart surge energy consumption according to the product of the instantaneous restart power and the restart duration; S252. Obtain the fault time parameters according to the fault characteristics, where the fault time parameters include the fault warning time, the fault occurrence time, and the latest maintenance start time; S253. Obtain the response time buffer according to the difference between the fault occurrence time and the latest maintenance start time; S254. Obtain the deterioration amplitude coefficient and the deterioration rate coefficient according to the fault characteristics, and obtain the equipment deterioration degree index according to the deterioration rate coefficient and the fault warning time, where the calculation formula is: ; where represents the equipment deterioration degree index, represents the deterioration rate coefficient, represents the current time, represents the fault warning time; S255. Obtain the additional power consumption index according to the product of the equipment deterioration degree index and the deterioration amplitude coefficient; S256. Obtain the reference power of the target object, and obtain the additional power consumption according to the reference power and the additional power consumption index; S257. Obtain the pre-fault performance degradation energy consumption according to the additional power, the fault warning time, the fault occurrence time, and the response time buffer, where the calculation formula is: ; where represents the pre-fault performance degradation energy consumption, represents the fault warning time, represents the additional power, Indicates the fault occurrence time, Indicates the response time buffer; S258. Obtain the total fault energy consumption based on the number of faults, the energy consumption of the replacement device, the restart surge energy consumption, and the performance degradation energy consumption before the fault.

[0025] As described in the above steps S251 - S258, the calculation formulas of the equipment deterioration degree index and the energy consumption decay before failure performance are both normalized for parameters in advance to eliminate the dimensional differences between different variables. The purpose is to ensure that all variables are on the same order of magnitude, so as to make the calculation more stable and effective. The present invention obtains the number of failures, the energy consumption of the replacement equipment, the instantaneous restart power, and the restart duration through the fault characteristics, and obtains the restart surge energy consumption according to the product of the instantaneous restart power and the restart duration, which helps to comprehensively evaluate the energy consumption impact brought by the failure, especially the surge energy consumption during the restart process. Usually, the instantaneous restart power of the equipment will increase sharply after a failure, and the surge energy consumption also rises accordingly. By quantifying this part of the energy consumption, the operation efficiency of the equipment can be evaluated more accurately, and the energy waste caused by equipment failures can be avoided. By calculating the restart surge energy consumption, the maintenance and management decision-making of the equipment can be effectively improved. The fault time parameters are obtained through the fault characteristics, where the fault time parameters include the fault warning time, the fault occurrence time, and the latest maintenance start time. The response time buffer is obtained by the difference between the fault occurrence time and the latest maintenance start time. Accurately grasping the fault time parameters helps to optimize the equipment maintenance cycle. If the fault warning time is advanced, the operation personnel will have more time to handle it, avoiding the further expansion of the fault. By the difference between the fault occurrence time and the latest maintenance start time, the enterprise can evaluate the timeliness of the fault response, providing a basis for formulating a more effective equipment maintenance strategy. Mastering the fault occurrence and response time in advance is crucial for improving the equipment reliability and reducing the unplanned downtime. The response time buffer is an important indicator of the equipment maintenance response speed. By obtaining this difference, it can be ensured that measures can be taken promptly after the fault occurs, thus reducing the impact of equipment downtime on production and energy efficiency. Optimizing the response time buffer helps to reduce the maintenance cost and the energy efficiency loss caused by the equipment being in a non-optimal working state for a long time. The deterioration amplitude coefficient and the deterioration rate coefficient are obtained through the fault characteristics, and the equipment deterioration degree index is obtained according to the deterioration rate coefficient and the fault warning time. The deterioration of the equipment usually intensifies gradually over time. By quantifying the deterioration amplitude and rate, the degree of performance decline of the equipment within a period of time can be clearly understood. By predicting the deterioration degree, effective measures (such as upgrading or replacing parts) can be taken in advance, thus avoiding the energy efficiency waste caused by the performance decline of the equipment. The equipment deterioration degree index can provide data support for the optimization, overhaul, and replacement of the equipment. The additional power consumption index is obtained by the product of the equipment deterioration degree index and the deterioration amplitude coefficient, which helps to quantify the direct impact of equipment deterioration on energy efficiency. Equipment deterioration often leads to additional power consumption, especially when the deterioration amplitude is large. Through the additional power consumption index, the energy efficiency loss caused by deterioration can be quantified, and a basis for the enterprise to optimize the operation can be provided, thus reducing the operation cost. By obtaining the reference power of the target object and obtaining the additional power consumption according to the reference power and the additional power consumption index,It is possible to clearly evaluate the additional power consumption of the equipment after deterioration. By comparing with the benchmark power, it can be clearly seen whether there is an energy efficiency loss in the equipment after a period of time. By calculating the additional power consumption, enterprises can targetedly adjust the operating parameters or take other measures to reduce the energy efficiency loss and improve the overall efficiency of the equipment. By obtaining the energy consumption during performance decay before the fault based on the additional power, consumption fault warning time, fault occurrence time, and response time buffer, and obtaining the total fault energy consumption based on the number of faults, energy consumption of alternative equipment, restart surge energy consumption, and energy consumption during performance decay before the fault, considering the time window of fault warning and equipment response, it can help enterprises comprehensively understand the energy efficiency changes before and after equipment faults. It not only focuses on the performance decay of the equipment but also incorporates the energy consumption changes caused by different time periods before and after the fault into the analysis, so as to more comprehensively identify potential energy efficiency losses. By combining energy consumption factors from multiple dimensions and evaluating the comprehensive energy efficiency impact brought by faults from a global perspective, by statistically analyzing factors such as the number of faults, energy consumption of alternative equipment, and restart surge energy consumption, it is possible to accurately quantify the contribution of each fault to the overall energy consumption and help enterprises identify which links need to optimize operations to reduce the total energy consumption caused by faults.,

[0026] In one embodiment, the step S3 of obtaining the corresponding actual cost energy efficiency according to each of the energy procurement cost, equipment maintenance cost, carbon emission cost, and manual management cost includes: S31. Obtain the total cost according to the sum of the energy procurement cost, equipment maintenance cost, carbon emission cost, and manual management cost; S32. Obtain the total energy consumption of the target object, and obtain the unit energy cost according to the ratio of the total energy consumption and the total cost; S33. Obtain the carbon emission intensity according to the ratio of the carbon emission cost and the total energy consumption; S34. Obtain the effective output of the equipment of the target object, and obtain the equipment maintenance efficiency ratio according to the effective output of the equipment and the equipment maintenance cost; S35. Obtain the historical energy consumption data of the target object, and obtain the benchmark average energy consumption and the actual average energy consumption according to the historical energy consumption data; S36. Obtain the energy savings according to the benchmark average energy consumption and the actual average energy consumption, and obtain the management efficiency index according to the energy savings and the manual management cost; S37. Obtain the comprehensive energy efficiency cost index according to the management efficiency index, equipment maintenance efficiency ratio, carbon emission intensity, and unit energy cost, where the calculation formula is: ; where represents the comprehensive energy efficiency cost index, represents the weight of the unit energy cost, represents the unit energy cost, The weight representing the equipment maintenance efficiency ratio, The equipment maintenance efficiency ratio, The weight representing the carbon emission intensity, The carbon emission intensity, The weight representing the management efficiency index, The management efficiency index.

[0027] As described in the above steps S31 - S37, the calculation formulas of the comprehensive energy efficiency cost index all perform normalization processing on parameters in advance to eliminate the dimensional differences between different variables. The purpose is to ensure that all variables are on the same order of magnitude, so as to make the calculation more stable and effective. The present invention obtains the total cost by summing the energy procurement cost, equipment maintenance cost, carbon emission cost, and labor management cost. By aggregating various costs, the overall economic impact of energy consumption can be comprehensively evaluated. This method considers the direct cost of energy procurement, the continuous cost of equipment maintenance, the environmental cost of carbon emissions, and the indirect cost of labor management, ensuring a comprehensive financial perspective, thus making cost control more accurate and efficient. By obtaining the total energy consumption of the target object and obtaining the unit energy cost according to the ratio of the total energy consumption and the total cost, the unit energy cost is an important indicator to measure energy efficiency. By correlating the total cost with the energy consumption, the economic benefits of energy consumption can be judged more clearly. This helps to discover areas with high energy consumption but relatively low cost, or vice versa, so as to optimize energy consumption targeted. By obtaining the ratio of the carbon emission cost and the total energy consumption, the carbon emission intensity is obtained. The carbon emission intensity can not only reflect the environmental impact of energy consumption, but also help enterprises make better decisions in terms of sustainable development. By calculating the carbon emission intensity, the environmental impact during the energy consumption process can be intuitively evaluated, promoting the optimization of low carbon emissions. By obtaining the effective output of the equipment of the target object and obtaining the equipment maintenance efficiency ratio according to the effective output of the equipment and the equipment maintenance cost, the equipment maintenance efficiency ratio can reflect the working efficiency of the equipment during the maintenance period. Efficient equipment maintenance can reduce downtime, lower maintenance costs, and improve productivity, thus enhancing the overall operation efficiency of the equipment. This provides an important basis for optimizing equipment management and increasing output. By obtaining the historical energy consumption data of the target object and obtaining the benchmark average energy consumption and the actual average energy consumption according to the historical energy consumption data, by comparing the benchmark and actual energy consumption data, possible inefficiencies or waste phenomena in energy use can be discovered. This helps enterprises identify and correct the energy efficiency gap, further improve energy utilization rate, and reduce unnecessary energy waste. By obtaining the energy savings according to the benchmark average energy consumption and the actual average energy consumption, and obtaining the management efficiency index according to the energy savings and the labor management cost. The comprehensive energy efficiency cost index is obtained through the management efficiency index, the equipment maintenance efficiency ratio, the carbon emission intensity, and the unit energy cost. The energy savings is the core indicator to evaluate the effectiveness of energy-saving measures. By quantifying the amount of energy saved, enterprises can intuitively understand which measures are effective and which may need to be adjusted or strengthened to achieve the goal of energy conservation and emission reduction. The management efficiency index reflects the decision-making and execution ability of the management layer during the energy-saving process. By optimizing the management process and improving the management efficiency, unnecessary labor costs can be reduced, thus achieving the best energy utilization efficiency while controlling costs. The comprehensive energy efficiency cost index is an integrated indicator,Taking into account various factors such as management, equipment, environment, and energy consumption, through this index, enterprises can comprehensively evaluate the effectiveness of overall energy management, identify the optimal management strategies and energy-saving solutions, optimize the energy efficiency cost structure, and further improve the overall competitiveness of enterprises.

[0028] In one embodiment, the step S4 of obtaining the corresponding actual environmental energy efficiency according to the refrigeration energy consumption, dehumidification energy consumption, light intensity, and ventilation energy consumption includes: S41. Obtain the refrigeration capacity through a temperature sensor, and obtain the refrigeration energy efficiency ratio according to the refrigeration capacity and refrigeration energy consumption; S42. Obtain the dehumidification amount through a humidity sensor, and obtain the dehumidification energy efficiency ratio according to the dehumidification amount and dehumidification energy consumption; S43. Obtain the ventilation volume through a wind speed sensor, and obtain the ventilation energy efficiency ratio according to the ventilation volume and ventilation energy consumption; S44. Obtain the maximum allowable light intensity according to the light intensity, and obtain the natural light utilization rate according to the maximum allowable light intensity and the light intensity; S45. Obtain the actual environmental energy efficiency according to the natural light utilization rate, ventilation energy efficiency ratio, dehumidification energy efficiency ratio, and refrigeration energy efficiency ratio.

[0029] As described in the above steps S41 - S45, the present invention obtains the cooling capacity through a temperature sensor, and obtains the cooling energy efficiency ratio based on the cooling capacity and cooling energy consumption. It obtains the dehumidification amount through a humidity sensor, and obtains the dehumidification energy efficiency ratio based on the dehumidification amount and dehumidification energy consumption. It obtains the ventilation volume through a wind speed sensor, and obtains the ventilation energy efficiency ratio based on the ventilation volume and ventilation energy consumption. It obtains the maximum allowable light intensity according to the light intensity, and obtains the natural light utilization rate based on the maximum allowable light intensity and the light intensity. It obtains the actual environmental energy efficiency based on the natural light utilization rate, ventilation energy efficiency ratio, dehumidification energy efficiency ratio, and cooling energy efficiency ratio. Existing technologies may only consider the energy efficiency in a single dimension, such as cooling power or simply energy consumption, while ignoring the energy utilization efficiency in other aspects. By combining the energy efficiencies in multiple dimensions such as cooling, dehumidification, ventilation, and lighting for analysis, the contribution of each link to energy efficiency can be comprehensively evaluated, so as to find out the optimization space. For example, in the actual environment, cooling and dehumidification often work together, and ignoring this interaction may lead to inaccurate energy efficiency assessment. After comprehensively analyzing the energy efficiency, the overall system can be optimized more precisely, thus achieving energy-saving effects in multiple aspects. By obtaining various types of sensor data (such as temperature, humidity, wind speed, light intensity, etc.) in real time, the operating modes of different devices can be dynamically adjusted. For example, when the light intensity reaches a certain level, the utilization rate of natural light can be increased to reduce the demand for artificial lighting;When the ventilation efficiency is high, the energy consumption of the air conditioner can be reduced. This real-time adjustment helps to avoid excessive energy consumption, improve the utilization efficiency of the equipment, and automatically optimize the energy efficiency according to the changes in the environment. By considering the comprehensive impacts of different factors in the environment such as refrigeration, dehumidification, ventilation, and lighting, the energy efficiency of the environment can be evaluated more comprehensively. For example, an efficient ventilation system may play an auxiliary role in dehumidification and refrigeration, thus improving the overall energy utilization rate. Based on the comprehensive consideration of different factors, a more precise energy-saving plan can be formulated to reduce unnecessary energy waste. This method not only focuses on energy efficiency but also helps to maintain a good environmental comfort level through the comprehensive consideration of factors such as humidity, temperature, ventilation, and lighting. For example, too high humidity may lead to an uncomfortable living environment. By optimizing the operating efficiency of dehumidification and refrigeration equipment, both energy efficiency can be improved and the environmental comfort can be ensured. By optimizing the balance of these dimensions, the situation of simply pursuing energy conservation while ignoring comfort is avoided, thereby improving the comprehensive quality of the environment. By obtaining various sensor data and combining with the comprehensive energy efficiency assessment, it can help to formulate more accurate energy-saving strategies. For example, if it is found that the energy efficiency of certain equipment is low during a specific time period, the system can improve the efficiency by adjusting the equipment operation time or adjusting the equipment settings. At the same time, personalized energy-saving strategies can be customized according to different environmental conditions instead of adopting a one-size-fits-all energy-saving measure. In this way, the energy-saving effect can be improved targeted. By comprehensively considering data in multiple dimensions such as temperature, humidity, wind speed, and lighting, the above method can provide a more comprehensive, accurate, and dynamic energy efficiency analysis tool to help achieve the maximum optimization of energy use and improve the energy efficiency and comfort of the overall environment. This comprehensive method can more effectively solve the limitations in the existing technology compared with the traditional single-dimensional analysis.;

[0030] In one embodiment, step S6 of generating a visual trend change graph according to the multiple energy consumption amounts includes: S61. Obtain the corresponding time points according to each of the energy consumption amounts; S62. Establish a time - energy consumption amount coordinate axis with the time points as the X-axis and the energy consumption amounts as the Y-axis; S63. Plot the energy consumption amount corresponding to each time point as a connection point on the time - energy consumption amount coordinate axis; S64. Connect the multiple connection points in sequence with broken lines to obtain a visual trend change graph.

[0031] As described in the above steps S61 - S64, the present invention obtains the corresponding time for each energy consumption amount, takes the time point as the X - axis and the energy consumption amount as the Y - axis to establish a time - energy consumption coordinate axis. The energy consumption amount corresponding to each time point is used as a connection point and plotted on the time - energy consumption coordinate axis. Multiple connection points are connected in sequence by broken lines to obtain a visual trend change graph. By connecting each time point and the corresponding energy consumption amount with broken lines, the change trend of energy consumption over time can be intuitively displayed, and the peak period, trough period and abnormal fluctuations of energy consumption can be quickly identified, which helps to better understand the energy usage pattern. Displaying the energy consumption data of different time periods in the same chart makes the trend change clearer, which can help managers quickly find energy efficiency problems and is convenient for adjustment and optimization. By converting data into graphics, especially trend charts, a large amount of complex data can be simplified into easy - to - interpret visual images. By analyzing the trend of the broken line chart, the future energy consumption trend can be predicted, which helps relevant personnel to carry out preventive management, avoid unnecessary waste or save more energy. The dynamic characteristics of data visualization enable managers to adjust the energy efficiency monitoring strategy at any time, quickly respond to changes in energy demand, and improve the flexibility and response speed of overall energy efficiency management.

[0032] This application also provides a BI analysis system for energy efficiency control data, including: The first acquisition module is used to acquire multiple multi - dimensional real - time data of a target object within a preset time period, where the multi - dimensional real - time data includes equipment operation efficiency data, cost structure data and environmental parameter data; The second acquisition module is used to obtain the equipment power, operation duration and fault characteristics according to each of the equipment operation efficiency data, and obtain the corresponding actual equipment operation energy efficiency according to each of the equipment power, operation duration and fault characteristics; The third acquisition module is used to obtain the energy procurement cost, equipment maintenance cost, carbon emission cost and labor management cost according to each of the cost structure data, and obtain the corresponding actual cost energy efficiency according to each of the energy procurement cost, equipment maintenance cost, carbon emission cost and labor management cost; The fourth acquisition module is used to obtain the refrigeration energy consumption, dehumidification energy consumption, light intensity and ventilation energy consumption according to each of the environmental parameter data, and obtain the corresponding actual environmental energy efficiency according to the refrigeration energy consumption, dehumidification energy consumption, light intensity and ventilation energy consumption; The fifth acquisition module is used to obtain the corresponding energy consumption amount according to each of the actual environmental energy efficiency, actual cost energy efficiency and actual equipment operation energy efficiency; The generation module is used to generate a visual trend change graph according to multiple energy consumption amounts and send the visual trend change graph to a visual analysis system for display.

[0033] In one embodiment, the second acquisition module includes: A first acquisition unit, configured to acquire a load rate and an idle rate according to the device operation efficiency data, and acquire an actual device power according to the load rate and the device power; A second acquisition unit, configured to acquire an actual operation duration according to the operation duration and the idle rate, and acquire a first actual energy consumption according to the actual operation duration and the actual device power; A third acquisition unit, configured to acquire a load threshold, and acquire an energy consumption penalty coefficient according to the load rate and the load threshold; A fourth acquisition unit, configured to acquire a second actual energy consumption according to the energy consumption penalty coefficient and the first actual energy consumption; A fifth acquisition unit, configured to acquire a total fault energy consumption according to the fault characteristics, and acquire an actual device operation energy efficiency according to the second actual energy consumption and the total fault energy consumption.

[0034] It should be noted that each module and unit in the BI analysis system for energy efficiency control data corresponds one by one to the steps in the BI analysis method for energy efficiency control data.

[0035] As Figure 3 shown, the present application further provides a computer device, which may be a server, and its internal structure may be as Figure 3 shown. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store all data required for the process of the BI analysis method for energy efficiency control data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements the BI analysis method for energy efficiency control data.

[0036] Those skilled in the art can understand that Figure 3 the structure shown in

[0037] is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied.

[0038] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided in this application and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0039] It should be noted that in this text, the terms "including", "comprising", or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that includes a series of elements includes not only those elements but also other elements not explicitly listed, or elements that are inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, apparatus, article, or method that includes the element.

[0040] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structural or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A BI analysis method for energy efficiency management data, characterized in that: include: Acquire multiple multi-dimensional real-time data of the target object within a preset time period, wherein the multi-dimensional real-time data includes equipment operation efficiency data, cost structure data and environmental parameter data; Obtaining device power, operating time and fault characteristics according to each of the device operating efficiency data, and obtaining corresponding actual device operating energy efficiency according to each of the device power, operating time and fault characteristics; Obtaining energy procurement cost, equipment maintenance cost, carbon emission cost and labor management cost according to each of the cost structure data, and obtaining corresponding actual cost energy efficiency according to each of the energy procurement cost, equipment maintenance cost, carbon emission cost and labor management cost; Obtaining cooling energy consumption, dehumidification energy consumption, light intensity and ventilation energy consumption according to each of the environmental parameter data, and obtaining the corresponding actual environmental energy efficiency according to the cooling energy consumption, dehumidification energy consumption, light intensity and ventilation energy consumption; Obtaining corresponding energy consumption according to each of the actual environmental energy efficiency, actual cost energy efficiency and actual equipment operation energy efficiency; A visualized trend change graph is generated according to the plurality of energy consumptions, and the visualized trend change graph is sent to a visualization analysis system for display.

2. The BI analysis method for energy efficiency management data according to claim 1 is characterized in that: The step of obtaining the corresponding actual equipment operation energy efficiency according to the power, operation time and fault characteristics of each of the equipment includes: Obtaining a load rate and an idle rate according to the equipment operation efficiency data, and obtaining an actual equipment power according to the load rate and the equipment power; Acquire the actual operation time according to the operation time and the no-load rate, and acquire the first actual energy consumption according to the actual operation time and the actual device power; Obtaining a load threshold, and obtaining an energy consumption penalty coefficient according to the load rate and the load threshold; Obtaining a second actual energy consumption according to the energy consumption penalty coefficient and the first actual energy consumption; The total fault energy consumption is obtained according to the fault characteristics, and the actual equipment operation energy efficiency is obtained according to the second actual energy consumption and the total fault energy consumption.

3. The BI analysis method for energy efficiency management data according to claim 2 is characterized in that: The step of obtaining the total fault energy consumption according to the fault characteristics comprises: Obtaining the number of faults, energy consumption of alternative equipment, instantaneous restart power and restart duration according to the fault characteristics, and obtaining restart surge energy consumption according to the instantaneous restart power and restart duration; Acquire a fault time parameter according to the fault feature, wherein the fault time parameter includes a fault warning time, a fault occurrence time, and a latest maintenance start time; Obtaining a response time buffer according to the fault occurrence time and the latest maintenance start time; Obtaining a degradation amplitude coefficient and a degradation rate coefficient according to the fault characteristics, and obtaining an equipment degradation degree index according to the degradation rate coefficient and the fault warning time; Obtaining an additional power consumption index according to the equipment degradation degree index and the degradation amplitude coefficient; Acquire a baseline power of the target object, and acquire additional power consumption according to the baseline power and an additional power consumption index; The pre-fault performance degradation energy consumption is obtained according to the additional power consumption fault warning time, fault occurrence time and response time buffer, and the total fault energy consumption is obtained according to the number of faults, replacement equipment energy consumption, restart surge energy consumption and pre-fault performance degradation energy consumption.

4. The BI analysis method for energy efficiency management data according to claim 1 is characterized in that: The step of obtaining the corresponding actual cost energy efficiency according to each of the energy procurement cost, equipment maintenance cost, carbon emission cost and labor management cost includes: Obtain the total cost based on the energy procurement cost, equipment maintenance cost, carbon emission cost and labor management cost; Obtaining total energy consumption of the target object, and obtaining unit energy cost according to the total energy consumption and the total cost; Obtaining carbon emission intensity based on the carbon emission cost and total energy consumption; Obtaining the effective output of the equipment of the target object, and obtaining the equipment maintenance efficiency ratio according to the effective output of the equipment and the equipment maintenance cost; Obtaining historical energy consumption data of the target object, and obtaining a baseline average energy consumption and an actual average energy consumption based on the historical energy consumption data; Obtaining energy savings based on the benchmark average energy consumption and the actual average energy consumption, and obtaining a management efficiency index based on the energy savings and labor management costs; A comprehensive energy efficiency cost index is obtained according to the management efficiency index, equipment maintenance efficiency ratio, carbon emission intensity and unit energy cost.

5. The BI analysis method for energy efficiency management data according to claim 1 is characterized in that: The step of obtaining the corresponding actual environment energy efficiency according to the refrigeration energy consumption, dehumidification energy consumption, light intensity and ventilation energy consumption comprises: Acquire the cooling capacity through a temperature sensor, and acquire the cooling energy efficiency ratio according to the cooling capacity and the cooling energy consumption; Obtaining a dehumidification amount through a humidity sensor, and obtaining a dehumidification energy efficiency ratio according to the dehumidification amount and the dehumidification energy consumption; The ventilation volume is obtained by a wind speed sensor, and the ventilation energy efficiency ratio is obtained according to the ventilation volume and the ventilation energy consumption; Acquire a maximum allowable light intensity according to the light intensity, and acquire a natural light utilization rate according to the maximum allowable light intensity and the light intensity; The actual environmental energy efficiency is obtained according to the natural light utilization rate, ventilation energy efficiency ratio, dehumidification energy efficiency ratio and refrigeration energy efficiency ratio.

6. The BI analysis method for energy efficiency management data according to claim 1 is characterized in that: The step of generating a visualized trend change graph according to the plurality of energy consumptions comprises: Obtaining a corresponding time point according to each of the energy consumptions; With the time point as the X-axis and the energy consumption as the Y-axis, a time-energy consumption coordinate axis is established; The energy consumption corresponding to each time point is plotted as a connection point on the time-energy consumption axis; Connect the plurality of connection points in sequence through broken lines to obtain a visualized trend change graph.

7. A BI analysis system for energy efficiency management data, characterized in that: include: A first acquisition module is used to acquire a plurality of multi-dimensional real-time data of a target object within a preset time period, wherein the multi-dimensional real-time data includes equipment operation efficiency data, cost structure data and environmental parameter data; A second acquisition module is used to acquire the device power, operation time and fault characteristics according to each of the device operation efficiency data, and acquire the corresponding actual device operation energy efficiency according to each of the device power, operation time and fault characteristics; A third acquisition module is used to obtain energy procurement cost, equipment maintenance cost, carbon emission cost and labor management cost according to each of the cost structure data, and obtain the corresponding actual cost energy efficiency according to each of the energy procurement cost, equipment maintenance cost, carbon emission cost and labor management cost; A fourth acquisition module is used to acquire cooling energy consumption, dehumidification energy consumption, light intensity and ventilation energy consumption according to each of the environmental parameter data, and to acquire the corresponding actual environmental energy efficiency according to the cooling energy consumption, dehumidification energy consumption, light intensity and ventilation energy consumption; A fifth acquisition module, used for acquiring corresponding energy consumption according to each of the actual environment energy efficiency, the actual cost energy efficiency and the actual equipment operation energy efficiency; A generation module is used to generate a visual trend change graph according to the multiple energy consumptions, and send the visual trend change graph to a visual analysis system for display.

8. The BI analysis system for energy efficiency management data according to claim 7, characterized in that: The second acquisition module includes: A first acquisition unit, configured to acquire a load rate and an idle rate according to the equipment operation efficiency data, and acquire an actual equipment power according to the load rate and the equipment power; A second acquisition unit is used to acquire the actual operation time according to the operation time and the no-load rate, and acquire the first actual energy consumption according to the actual operation time and the actual device power; A third acquisition unit is used to acquire a load threshold, and acquire an energy consumption penalty coefficient according to the load rate and the load threshold; a fourth acquisition unit, configured to acquire a second actual energy consumption according to the energy consumption penalty coefficient and the first actual energy consumption; A fifth acquisition unit is used to acquire total fault energy consumption according to the fault characteristics, and to acquire actual equipment operation energy efficiency according to the second actual energy consumption and the total fault energy consumption.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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