Intelligent refrigeration station efficient operation and maintenance management method based on multi-source energy consumption data fusion

Abstract

The invention discloses an intelligent refrigeration station efficient operation and maintenance management method based on multi-source energy consumption data fusion, and particularly relates to the technical field of operation and maintenance management, and the method comprises the steps: building a multi-source energy consumption data monitoring system, carrying out the real-time monitoring and data collection of various types of energy in a refrigeration station, and collecting the historical operation and maintenance data of each device in the refrigeration station; the method comprises the following steps: acquiring power consumption, gas consumption and steam flow data, obtaining energy efficiency ratio data of equipment, determining energy information of the equipment, and determining fault information of the equipment based on the energy efficiency ratio data of the equipment; through comprehensive analysis of energy information and fault information of equipment, an equipment evaluation model is constructed, and potential risks of the equipment are quantified; the analysis result is converted into intelligent decision making and optimization execution, an early warning signal is generated through threshold value comparison, optimization scheduling, energy-saving management and resource allocation of the refrigeration station are achieved, and the equipment health risk can be recognized as soon as possible.

Description

Technical Field

[0001] This invention relates to the field of software development technology, and more specifically, to a method for efficient operation and maintenance management of intelligent refrigeration plants based on multi-source energy consumption data fusion. Background Technology

[0002] Currently, while energy consumption monitoring has seen some application in chiller plant management, traditional energy data acquisition typically focuses only on a single type of energy (such as electricity or gas), failing to comprehensively consider the interactive effects of multiple energy sources and lacking intelligent early warning and fault prediction mechanisms. Existing chiller plant energy efficiency management is usually based on a single energy consumption indicator, ignoring energy efficiency fluctuations caused by load fluctuations, equipment aging, and other factors during actual operation. This significantly reduces the accuracy and effectiveness of energy efficiency optimization. Consequently, traditional operation and maintenance management models struggle to detect and assess equipment health status in real time, often relying on manual judgment or periodic inspections, lacking intelligent early warning systems based on data analysis, and failing to promptly identify potential equipment problems. Summary of the Invention

[0003] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide an efficient operation and maintenance management method for intelligent cold storage plants based on multi-source energy consumption data fusion, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: The intelligent cold storage plant operation and maintenance management method based on multi-source energy consumption data fusion includes the following steps: S1: Establish a multi-source energy consumption data monitoring system, including real-time monitoring and data collection of various energy sources in the refrigeration plant, and collection of historical operation and maintenance data of various equipment in the refrigeration plant; S2: Collect power consumption, gas consumption and steam flow data, obtain equipment energy efficiency ratio data, determine equipment energy information, and determine equipment fault information based on equipment energy efficiency ratio data; S3: By comprehensively analyzing the energy and fault information of the equipment, an equipment evaluation model is constructed to quantify the potential risks of the equipment; S4: Transform the analysis results into intelligent decision-making and optimized execution, generate early warning signals through threshold comparison, and realize optimized scheduling, energy-saving management and resource allocation of the refrigeration station.

[0005] In a preferred embodiment, determining the device's energy information and fault information includes: The system collects energy and fault information from the equipment. Energy information is represented by an energy anomaly accumulation coefficient and an energy efficiency fluctuation anomaly coefficient. Fault information is represented by a fault interval time anomaly coefficient. The coefficient for abnormal energy accumulation. This is the energy efficiency fluctuation anomaly coefficient. This is the fault interval time anomaly coefficient.

[0006] In a preferred embodiment, the logic for obtaining the energy anomaly accumulation coefficient is as follows: Energy data from the collected equipment within the monitoring range is included, comprising electricity consumption, gas consumption, and steam flow rate data. The collected energy data is arranged chronologically to form a time series. A mathematical model describing the equipment's energy consumption is constructed using this time series data. The mathematical model is obtained by fitting the time series data with an appropriate function form based on the equipment's operating patterns. The parameter estimation method for the fitted mathematical model uses the least squares method. The mathematical model for the electricity consumption, gas consumption, and steam flow rate data is denoted as follows: , as well as ; Based on the normal operating range of the equipment, set power consumption threshold, gas consumption threshold and steam flow threshold, and obtain the time period in which the energy data of the equipment in the monitoring range exceeds the power consumption threshold, gas consumption threshold and steam flow threshold. The formula for calculating the energy anomaly accumulation coefficient is as follows: ;in, This refers to the time period during which the power consumption data in the equipment's energy data exceeds the power consumption threshold. This refers to the time period during which the gas consumption data of the equipment exceeds the gas consumption threshold. This refers to the time period during which the steam flow rate data of the equipment exceeds the steam flow rate threshold.

[0007] In a preferred embodiment, the logic for obtaining the energy efficiency fluctuation anomaly coefficient is as follows: Based on the energy data of the equipment within the monitoring interval, and by determining the output energy efficiency of the equipment within the monitoring interval, the energy efficiency ratio of the equipment within the monitoring interval is obtained and marked as follows: Where i = 1, 2, 3, ..., I, I is a positive integer, and i is the number of each sampling time point within the monitoring interval; Calculate the average and standard deviation of the equipment energy efficiency ratio within the monitoring interval, and label the average and standard deviation of the equipment energy efficiency ratio within the monitoring interval as avg and std; where, , ; The formula for calculating the energy efficiency fluctuation anomaly coefficient is as follows: .

[0008] In a preferred embodiment, the logic for obtaining the fault interval time anomaly coefficient is as follows: Based on the equipment's fault data, the time point of the equipment fault is obtained, and the time interval between adjacent time points is calculated to obtain the equipment's fault interval time. The equipment's fault interval time is marked as: Where n = 1, 2, 3, ..., N, N is a positive integer, and n is the number of the time interval between adjacent time points; Set a fault interval time threshold and mark the fault interval time threshold as: The fault interval time of equipment within the monitoring interval is compared with the fault interval time threshold to obtain the fault interval time within the monitoring interval that is less than the fault interval time threshold. The fault interval time within the monitoring interval that is less than the fault interval time threshold is then re-marked as: Where m = 1, 2, 3, ..., M, M is a positive integer, and m is the number of the fault interval time within the monitoring interval that is less than the fault interval time threshold. The abnormality coefficient of the fault interval time is calculated using the following formula: .

[0009] In a preferred embodiment, a device evaluation model is constructed. A comprehensive analysis of the equipment's energy and fault information is conducted. A weighted calculation is performed using the energy anomaly accumulation coefficient, energy efficiency fluctuation anomaly coefficient, and fault interval time anomaly coefficient to construct an equipment evaluation model and generate an equipment fault evaluation coefficient. The formula for calculating the equipment fault evaluation coefficient is as follows: ;in, This is the equipment failure assessment coefficient.

[0010] In a preferred embodiment, generating a warning signal includes: Set equipment failure assessment coefficients and compare the equipment failure assessment coefficients of each piece of equipment in the refrigeration station with the equipment failure assessment coefficient threshold. If the equipment failure assessment coefficient is greater than the equipment failure assessment coefficient threshold, an early warning signal is generated. If the equipment failure assessment coefficient is less than the equipment failure assessment coefficient threshold, no early warning signal is generated.

[0011] The technical effects and advantages of this invention are as follows: This invention uses electricity metering equipment, gas sensors, and steam flow meters to collect real-time energy consumption data of various types of energy in a refrigeration plant. At the same time, it collects historical operation and maintenance data of each device, quantifies the energy efficiency and failure frequency of the devices, and then assesses the potential failure risks and health status of the devices. This invention helps to identify equipment health risks early. Attached Figure Description

[0012] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1This is a flowchart illustrating the efficient operation and maintenance management method for intelligent refrigeration plants based on multi-source energy consumption data fusion, as proposed in this invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] Example 1 Figure 1 This is a flowchart illustrating the efficient operation and maintenance management method for intelligent cold storage plants based on multi-source energy consumption data fusion according to the present invention, which specifically includes the following steps: S1: Establish a multi-source energy consumption data monitoring system, including real-time monitoring and data collection of various energy sources in the refrigeration plant, and collection of historical operation and maintenance data of various equipment in the refrigeration plant; S2: Collect power consumption, gas consumption and steam flow data, obtain equipment energy efficiency ratio data, determine equipment energy information, and determine equipment fault information based on equipment energy efficiency ratio data; S3: By comprehensively analyzing the energy and fault information of the equipment, an equipment evaluation model is constructed to quantify the potential risks of the equipment; S4: Transform the analysis results into intelligent decision-making and optimized execution, generate early warning signals through threshold comparison, and realize optimized scheduling, energy-saving management and resource allocation of the refrigeration station.

[0015] In smart refrigeration plants, the fusion of multi-source energy consumption data can significantly improve operation and maintenance efficiency, optimize energy consumption management, and enhance the stability and economy of the overall system. As a complex system involving multiple equipment and energy sources such as refrigeration, heating, compression, and condensation, refrigeration plants can provide a comprehensive perspective on energy management by monitoring the consumption data of different energy sources, thereby improving energy efficiency, reducing operating costs, and timely identifying potential problems. The energy types in refrigeration plants include electricity, gas, and steam. To effectively monitor multi-source energy consumption data, the refrigeration plant is equipped with various devices to collect and record the consumption of various energy sources in real time, including: electricity metering equipment: installed at the inlet of various equipment (such as compressors, electric pumps, fans, etc.) to collect electricity consumption data in real time; gas sensors: used to monitor refrigerant (such as ammonia, R134a, etc.) leakage to ensure the safe and stable operation of the refrigeration plant; and steam flow meters, which help monitor steam consumption.

[0016] It should be noted that the collected data undergoes noise reduction, standardization, and missing value imputation to ensure accuracy and consistency. Furthermore, it ensures precise temporal alignment of monitoring data from different devices and energy sources, facilitating subsequent analysis and comparison. This unified integration of multi-source energy consumption data constructs a comprehensive data platform spanning multiple devices and energy sources. Data integration tools or data warehouse technologies are employed to aggregate various energy consumption data into a central database.

[0017] In addition to collecting multi-source energy consumption data from the equipment, it is also necessary to collect and integrate historical operation and maintenance data of the equipment. Historical operation and maintenance data is crucial for analyzing the health status of the equipment, changes in energy efficiency, and predicting future operation and maintenance needs. The sources of historical data collection include equipment maintenance records and operation logs, covering the equipment's repair history, maintenance records, replacement of parts, fault records, etc.

[0018] It should be noted that historical equipment operation and maintenance data is imported from different management systems into the data integration platform for unified labeling and organization, ensuring time synchronization between real-time monitoring data and historical operation and maintenance data for joint analysis. This is achieved through standardized time formats (such as timestamps) for correlation.

[0019] Each piece of equipment in the refrigeration station is analyzed individually, and its energy and fault information is collected. The energy information of the equipment is represented by the energy anomaly accumulation coefficient and the energy efficiency fluctuation anomaly coefficient, and the fault information of the equipment is represented by the fault interval time anomaly coefficient.

[0020] The advantages of the energy anomaly accumulation coefficient are: By analyzing cumulative data over a period of time, the energy anomaly accumulation coefficient can help maintenance personnel identify long-term energy efficiency fluctuations in equipment within the monitoring period. Quantifying abnormal energy consumption within a specific time period can reveal trends in energy efficiency decline, helping to pinpoint potential equipment problems. Long-term abnormal energy consumption is often a precursor to equipment failure or performance degradation. Calculating the energy anomaly accumulation coefficient can help identify abnormal operating patterns in equipment, thereby predicting equipment failure in advance. For example, consistently exceeding the limits for equipment power consumption, gas consumption, or steam flow often indicates damage, aging, or other hidden faults in equipment components. The source anomaly accumulation coefficient can be used not only for monitoring single energy sources (such as electricity), but also to consider the combined effects of multiple energy sources such as gas consumption and steam flow, thus providing a more comprehensive reflection of the equipment's overall energy efficiency. By assessing the anomaly accumulation of multiple energy sources, the overall operating status of the equipment can be determined more accurately.

[0021] The logic for obtaining the energy anomaly accumulation coefficient is as follows: Energy data from the equipment within the monitoring interval is collected. This energy data includes electricity consumption data, gas consumption data, and steam flow data. The collected energy data is arranged chronologically to form a time series. A mathematical model describing the equipment's energy consumption is constructed using this time series data. The mathematical model is obtained by selecting an appropriate function form based on the equipment's operating rules to fit the time series data. The parameter estimation method for the fitted mathematical model uses the least squares method. The mathematical model for the electricity consumption data, gas consumption data, and steam flow data is labeled as follows: , as well as ; It should be noted that the monitoring interval is a specific time period set by professionals in the field. The energy data depends on the operating characteristics of the equipment and the type of energy. The equipment may contain multiple energy consumption data or a single energy consumption data.

[0022] Based on the normal operating range of the equipment, set power consumption threshold, gas consumption threshold and steam flow threshold, and obtain the time period in which the energy data of the equipment in the monitoring range exceeds the power consumption threshold, gas consumption threshold and steam flow threshold. It should be noted that thresholds can be determined based on equipment design specifications, historical data, industry standards, etc. For example, the normal range of power consumption may be the maximum consumption value when the equipment is working at full load. When the equipment exceeds the set threshold, it may indicate that the equipment is overloaded, malfunctioning, has reduced energy efficiency, or is affected by external environmental factors.

[0023] The formula for calculating the energy anomaly accumulation coefficient is as follows: ;in, The coefficient for abnormal energy accumulation. This refers to the time period during which the power consumption data in the equipment's energy data exceeds the power consumption threshold. This refers to the time period during which the gas consumption data of the equipment exceeds the gas consumption threshold. This refers to the time period during which the steam flow rate data of the equipment exceeds the steam flow rate threshold.

[0024] As the formula shows, the larger the energy anomaly accumulation coefficient, the longer the abnormal energy consumption of the equipment lasts within the monitoring range. This may indicate that the equipment has problems such as decreased energy efficiency, overload operation, or potential failures. In other words, the more serious the energy consumption problem of the equipment within the monitoring range, the more likely it is that the equipment has long-term problems such as overload, decreased energy efficiency, failure, or external environmental influences. This prompts maintenance personnel to check, maintain, or optimize the equipment to ensure its normal operation and improve energy efficiency, thereby reducing energy waste and unnecessary energy consumption.

[0025] The advantages of the energy efficiency fluctuation anomaly coefficient are: The energy efficiency fluctuation anomaly coefficient can effectively quantify the energy efficiency fluctuation of equipment within a certain time interval. Compared with the energy efficiency ratio or standard deviation alone, the energy efficiency fluctuation anomaly coefficient not only considers the amplitude of the equipment's energy efficiency fluctuation, but also reveals the potential problems behind the changes in equipment energy efficiency; An increase in the energy efficiency fluctuation anomaly coefficient is usually closely related to equipment performance degradation, malfunction, or unstable operating conditions. By regularly calculating and monitoring the energy efficiency fluctuation anomaly coefficient, abnormalities in equipment operation can be detected in a timely manner, providing early warning of potential equipment failures or performance problems. Unstable equipment energy efficiency is often accompanied by energy waste. By monitoring the anomaly coefficient of energy efficiency fluctuation, maintenance personnel can identify and resolve issues with large energy efficiency fluctuations, thereby reducing energy waste and optimizing energy use efficiency.

[0026] The logic for obtaining the energy efficiency fluctuation anomaly coefficient is as follows: based on the energy data of the equipment within the monitoring interval, and determining the output energy efficiency of the equipment within the monitoring interval, the energy efficiency ratio of the equipment within the monitoring interval is obtained, and the energy efficiency ratio of the equipment within the monitoring interval is marked as: Where i = 1, 2, 3, ..., I, I is a positive integer, and i is the number of each sampling time point within the monitoring interval; Calculate the average and standard deviation of the equipment energy efficiency ratio within the monitoring interval, and label the average and standard deviation of the equipment energy efficiency ratio within the monitoring interval as avg and std; where, , ; It should be noted that the larger the standard deviation of the equipment's energy efficiency ratio within the monitoring period, the more significant the changes in the equipment's load and operating status may have occurred within the monitoring period, leading to unstable energy efficiency performance. For example, some equipment may experience large fluctuations in its energy efficiency ratio at different points in time due to uneven load or improper adjustments.

[0027] The formula for calculating the energy efficiency fluctuation anomaly coefficient is as follows: ;in, This is the energy efficiency fluctuation anomaly coefficient.

[0028] As the formula shows, a larger energy efficiency fluctuation anomaly coefficient usually indicates that the equipment's load is unstable within the monitoring range, and it may frequently experience changes between high and low loads. When the load fluctuates significantly, the equipment's operating efficiency will fluctuate considerably, thus affecting its energy efficiency ratio.

[0029] The advantages of the fault interval time anomaly coefficient are: By calculating the relationship between the equipment's failure interval time and a set threshold, the failure interval time anomaly coefficient provides a simple and intuitive way to assess the equipment's operating status and failure frequency. If the equipment frequently experiences short-interval failures, the anomaly coefficient will be high, indicating that the equipment has reliability issues. By calculating the fault interval time anomaly coefficient, equipment maintenance cycles can be optimized. For equipment exhibiting a large anomaly coefficient, more preventative maintenance can be scheduled, reducing the risk of unexpected downtime and improving overall equipment operating efficiency. The equipment failure interval time anomaly coefficient provides maintenance personnel with a key indicator, helping them make more informed maintenance and management decisions. Maintenance personnel can use this coefficient to prioritize equipment that experiences frequent problems, preventing more serious equipment failures.

[0030] The logic for obtaining the fault interval time anomaly coefficient is as follows: Based on the equipment's fault data, the time point of equipment fault occurrence is obtained, and the time interval between adjacent time points is calculated to obtain the equipment's fault interval time. The equipment's fault interval time is then marked as follows: Where n = 1, 2, 3, ..., N, N is a positive integer, and n is the number of the time interval between adjacent time points; It should be noted that the exact time of the failure needs to be recorded precisely. This can be obtained through automated monitoring systems, log records, manual inspection, etc. The failure time point should include a detailed record of all equipment failures.

[0031] Set a fault interval time threshold and mark the fault interval time threshold as: The fault interval time of equipment within the monitoring interval is compared with the fault interval time threshold to obtain the fault interval time within the monitoring interval that is less than the fault interval time threshold. The fault interval time within the monitoring interval that is less than the fault interval time threshold is then re-marked as: Where m = 1, 2, 3, ..., M, M is a positive integer, and m is the number of the fault interval time within the monitoring interval that is less than the fault interval time threshold. It should be noted that the interval between failures (ITF) threshold refers to the minimum acceptable time interval between two equipment failures. Exceeding this threshold may indicate that the equipment is failing too frequently, has reliability issues, or requires maintenance. The ITF threshold is set by professionals in the relevant field.

[0032] The abnormality coefficient of the fault interval time is calculated using the following formula: ;in, This is the fault interval time anomaly coefficient.

[0033] As can be seen from the formula, the larger the fault interval time anomaly coefficient, the higher the frequency of equipment failures. In particular, frequent failures within a short time interval may indicate poor equipment reliability, design flaws, severe component wear, or failure to maintain the equipment in a timely manner.

[0034] A comprehensive analysis of the equipment's energy and fault information is conducted. A weighted calculation is performed using the energy anomaly accumulation coefficient, energy efficiency fluctuation anomaly coefficient, and fault interval time anomaly coefficient to construct an equipment evaluation model and generate an equipment fault evaluation coefficient. The formula for calculating the equipment fault evaluation coefficient is as follows: ;in, This is the equipment failure assessment coefficient.

[0035] A fault assessment coefficient is set, and the fault assessment coefficient of each piece of equipment in the refrigeration plant is compared with a threshold. If the fault assessment coefficient is greater than the threshold, an early warning signal is generated, indicating that the equipment may face higher maintenance risks. For equipment with a high fault assessment coefficient, more frequent preventative maintenance can be scheduled to reduce the risk of equipment failure. For example, increasing the frequency of equipment inspections, replacing parts, cleaning, and lubrication. If the fault assessment coefficient is less than the threshold, no early warning signal is generated.

[0036] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0037] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0038] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0039] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0040] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0041] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0042] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0043] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for efficient operation and maintenance management of intelligent cold storage plants based on multi-source energy consumption data fusion, characterized in that, Specifically, the following steps are included: S1: Establish a multi-source energy consumption data monitoring system, including real-time monitoring and data collection of various energy sources in the refrigeration plant, and collection of historical operation and maintenance data of various equipment in the refrigeration plant; S2: Collect power consumption, gas consumption and steam flow data, obtain equipment energy efficiency ratio data, determine equipment energy information, and determine equipment fault information based on equipment energy efficiency ratio data; S3: By comprehensively analyzing the energy and fault information of the equipment, an equipment evaluation model is constructed to quantify the potential risks of the equipment; S4: Transform the analysis results into intelligent decision-making and optimized execution, generate early warning signals through threshold comparison, and realize optimized scheduling, energy-saving management and resource allocation of the refrigeration station.

2. The method for efficient operation and maintenance management of intelligent cold storage plants based on multi-source energy consumption data fusion according to claim 1, characterized in that, Determine the equipment's energy and fault information, including: The system collects energy and fault information from the equipment. Energy information is represented by an energy anomaly accumulation coefficient and an energy efficiency fluctuation anomaly coefficient. Fault information is represented by a fault interval time anomaly coefficient. The coefficient for abnormal energy accumulation. This is the energy efficiency fluctuation anomaly coefficient. This is the fault interval time anomaly coefficient.

3. The method for efficient operation and maintenance management of intelligent cold storage plants based on multi-source energy consumption data fusion as described in claim 2, characterized in that: The logic for obtaining the energy anomaly accumulation coefficient is as follows: Energy data from the collected equipment within the monitoring range is included, comprising electricity consumption, gas consumption, and steam flow rate data. The collected energy data is arranged chronologically to form a time series. A mathematical model describing the equipment's energy consumption is constructed using this time series data. The mathematical model is obtained by fitting the time series data with an appropriate function form based on the equipment's operating patterns. The parameter estimation method for the fitted mathematical model uses the least squares method. The mathematical model for the electricity consumption, gas consumption, and steam flow rate data is denoted as follows: , as well as ; Based on the normal operating range of the equipment, set power consumption threshold, gas consumption threshold and steam flow threshold, and obtain the time period in which the energy data of the equipment in the monitoring range exceeds the power consumption threshold, gas consumption threshold and steam flow threshold. The formula for calculating the energy anomaly accumulation coefficient is as follows: ;in, This refers to the time period during which the power consumption data in the equipment's energy data exceeds the power consumption threshold. This refers to the time period during which the gas consumption data of the equipment exceeds the gas consumption threshold. This refers to the time period during which the steam flow rate data of the equipment exceeds the steam flow rate threshold.

4. The method for efficient operation and maintenance management of intelligent cold storage plants based on multi-source energy consumption data fusion according to claim 3, characterized in that, The logic for obtaining the energy efficiency fluctuation anomaly coefficient is as follows: Based on the energy data of the equipment within the monitoring interval, and by determining the output energy efficiency of the equipment within the monitoring interval, the energy efficiency ratio of the equipment within the monitoring interval is obtained and marked as follows: Where i = 1, 2, 3, ..., I, I is a positive integer, and i is the number of each sampling time point within the monitoring interval; Calculate the average and standard deviation of the equipment energy efficiency ratio within the monitoring interval, and label the average and standard deviation of the equipment energy efficiency ratio within the monitoring interval as avg and std; where, , ; The formula for calculating the energy efficiency fluctuation anomaly coefficient is as follows: .

5. The method for efficient operation and maintenance management of intelligent cold storage plants based on multi-source energy consumption data fusion according to claim 4, characterized in that... The logic for obtaining the fault interval time anomaly coefficient is as follows: Based on the equipment's fault data, the time points of equipment failure are obtained, and the time intervals between adjacent time points are calculated to obtain the equipment's fault interval time. The equipment's fault interval time is then marked as: Where n = 1, 2, 3, ..., N, N is a positive integer, and n is the number of the time interval between adjacent time points; Set a fault interval time threshold and mark the fault interval time threshold as: The fault interval time of equipment within the monitoring interval is compared with the fault interval time threshold to obtain the fault interval time within the monitoring interval that is less than the fault interval time threshold. The fault interval time within the monitoring interval that is less than the fault interval time threshold is then re-marked as: Where m = 1, 2, 3, ..., M, M is a positive integer, and m is the number of the fault interval time within the monitoring interval that is less than the fault interval time threshold. The abnormality coefficient of the fault interval time is calculated using the following formula: .

6. The method for efficient operation and maintenance management of intelligent cold storage plants based on multi-source energy consumption data fusion according to claim 5, characterized in that... Build an equipment evaluation model A comprehensive analysis of the equipment's energy and fault information is conducted. A weighted calculation is performed using the energy anomaly accumulation coefficient, energy efficiency fluctuation anomaly coefficient, and fault interval time anomaly coefficient to construct an equipment evaluation model and generate an equipment fault evaluation coefficient. The formula for calculating the equipment fault evaluation coefficient is as follows: ;in, This is the equipment failure assessment coefficient.

7. The method for efficient operation and maintenance management of intelligent cold storage plants based on multi-source energy consumption data fusion according to claim 6, characterized in that... Generate early warning signals, including: Set equipment failure assessment coefficients and compare the equipment failure assessment coefficients of each piece of equipment in the refrigeration station with the equipment failure assessment coefficient threshold. If the equipment failure assessment coefficient is greater than the equipment failure assessment coefficient threshold, an early warning signal is generated. If the equipment failure assessment coefficient is less than the equipment failure assessment coefficient threshold, no early warning signal is generated.

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