An operation monitoring method and monitoring system for an isothermal dehumidification and humidification fresh air unit

Abstract

The present application discloses an operation monitoring method and monitoring system for an isothermal dehumidification and humidification fresh air unit, which belongs to the field of air conditioning technology, including: receiving first energy consumption data and generating second energy consumption data; sending the second energy consumption data to an edge device, performing energy consumption analysis, and generating energy consumption conditions; sending the energy consumption conditions to a monitoring platform, generating an energy consumption report, and sending the energy consumption report to a display terminal; predicting the energy consumption conditions of the fresh air unit, and comparing the predicted results with the actually received energy consumption conditions; and sending an alarm message to the management personnel based on the comparison results. In the implementation of the present application, the first energy consumption data at each collection node of the fresh air unit is collected, and the second energy consumption data is obtained after merging. The second energy consumption data is analyzed by the edge device to generate energy consumption conditions based on the collection nodes, thereby facilitating the subsequent comparison process, monitoring the fresh air unit based on historical energy consumption, and issuing an alarm in a timely manner.

Description

Technical Field

[0001] The present application relates to the field of air conditioning technology, and specifically to an operation monitoring method and a monitoring system for an isothermal dehumidification and humidification fresh air unit. Background Art

[0002] The fresh air unit is an air conditioning equipment that provides fresh air. Its function can achieve constant temperature and humidity or simply provide fresh air according to the requirements of the use environment. Its working principle is to draw fresh air from outdoors and send it to the room through a fan after dust removal, dehumidification (or humidification), cooling (or heating), etc. It replaces the original air in the room when entering the indoor space and is widely used in various fields.

[0003] In existing fresh air units, energy consumption monitoring function is usually provided, which makes it easier to judge the operating status of the fresh air units. Energy consumption monitoring types include total energy consumption monitoring and real-time energy consumption monitoring. Energy consumption monitoring usually requires the use of an energy consumption collection system. The energy consumption collection system collects the energy consumption of the fresh air unit through the collection equipment and uploads it to the processing equipment for processing and optimization, thereby obtaining the energy consumption information of the fresh air unit.

[0004] However, for total energy consumption monitoring, it can be summarized at any time after the fresh air unit has finished operating, while for real-time energy consumption monitoring, there need to be certain requirements for the transmission method of collected data, so as to improve the real-time nature of the data, help enterprises control and optimize energy consumption, and promptly discover and solve energy consumption anomalies or waste problems. In the existing technology, there is still a certain delay in the real-time energy consumption of the fresh air unit. In some scenarios with high requirements for real-time energy consumption display, this delay will affect the judgment of the operating status of the fresh air unit, and thus it is impossible to obtain accurate energy consumption conditions, so it needs to be optimized.

[0005] Therefore, it is necessary to provide an operation monitoring method and monitoring system for an isothermal dehumidification and humidification fresh air unit to solve the above problems.

[0006] It should be noted that the above information disclosed in this background technology section is only for understanding the background technology of the present application concept, and therefore, it may contain information that does not constitute the prior art. Summary of the invention

[0007] Based on the above-mentioned problems existing in the prior art, the problem to be solved by the present application is: to provide an isothermal dehumidification and humidification fresh air unit operation monitoring method and monitoring system, so as to be able to predict the operating energy consumption of the fresh air unit, and trigger an alarm based on the prediction results, while improving the real-time nature of the monitoring results.

[0008] The technical solution adopted by the present application to solve the technical problem is: an operation monitoring method of an isothermal dehumidification and humidification fresh air unit, comprising:

[0009] The server receives the first energy consumption data collected by the collection devices at each collection node of the fresh air unit, and merges the first energy consumption data to generate second energy consumption data;

[0010] The server sends the second energy consumption data to the edge device, and the edge device performs energy consumption analysis on the second energy consumption data to generate energy consumption conditions based on the division of the collection nodes;

[0011] The edge device sends the energy consumption information to the monitoring platform. After receiving the energy consumption information, the monitoring platform generates an energy consumption report based on the energy consumption information and sends the energy consumption report to the display terminal for display.

[0012] After receiving the energy consumption data, the monitoring platform predicts the energy consumption of each collection node of the fresh air unit through the prediction model, and compares the prediction results with the actual energy consumption data received;

[0013] A first threshold is set. During the comparison process, if the difference between the predicted result and the actual energy consumption is greater than the first threshold, an alarm mechanism will be triggered to send an alarm message to the management personnel.

[0014] During the implementation of the technical solution of the present application, the first energy consumption data at each collection node of the fresh air unit is collected, and the second energy consumption data is obtained by merging the data. The second energy consumption data is analyzed by the edge device to generate an energy consumption situation based on the collection node, thereby facilitating the subsequent comparison process, monitoring the fresh air unit according to the historical energy consumption, and timely issuing an alarm for abnormal energy consumption.

[0015] Furthermore, the edge device performs energy consumption analysis on the second energy consumption data further including:

[0016] Screening abnormal values ​​in the received second energy consumption data;

[0017] Calculate the statistical parameters of each collection node according to the filtered second energy consumption data, the statistical parameters including the energy consumption mean and the energy consumption peak;

[0018] Based on statistical parameters, evaluate the energy efficiency and operating status of each collection node;

[0019] Generate energy consumption reports based on the collection nodes and send the reports to the server or designated terminal.

[0020] Furthermore, the prediction model has a data input end and a data output end, wherein the data input end is used to input the second energy consumption data. A prediction algorithm runs in the prediction model, and after receiving the input data, the prediction result of the energy consumption of each collection node is output according to fixed rules.

[0021] Furthermore, before the comparison, the output results of the prediction model need to be optimized. The optimization of the output results of the prediction model includes:

[0022] Get the output of the prediction model and assign weights to each output according to the model performance;

[0023] Make a weighted decision on the output results according to the assigned weights, and output the weighted decision results;

[0024] The assigned weights are tuned, the weighted decision is re-made, and the weighted decision results are updated.

[0025] Furthermore, the value of the first threshold is 10% of the actual energy consumption of each node.

[0026] Furthermore, the prediction model includes a combination of one or more of a data-driven model, a time series model, and a machine learning model.

[0027] Furthermore, the sum of all assigned weights is one.

[0028] Furthermore, the weighted decision refers to multiplying the weight value by the prediction result, the prediction result is the energy consumption, and the unit of the energy consumption is joule.

[0029] Furthermore, the method also includes: based on the energy consumption report and alarm information, the management personnel formulate targeted energy-saving measures or maintenance plans, and continuously monitor the energy consumption of the fresh air unit, and feed back the continuous monitoring results to the management personnel.

[0030] An operation monitoring system for an isothermal dehumidification and humidification fresh air unit, the system comprising:

[0031] A data merging module is used for the server to receive the first energy consumption data collected by the collection devices at each collection node of the fresh air unit, and to merge the first energy consumption data to generate second energy consumption data;

[0032] An energy consumption analysis module, which is used for the server to send the second energy consumption data to the edge device, and the edge device performs energy consumption analysis on the second energy consumption data to generate energy consumption conditions based on the division of the collection nodes;

[0033] The energy consumption report generation module is used for the edge device to send the energy consumption status to the monitoring platform. After receiving the energy consumption status, the monitoring platform generates an energy consumption report according to the energy consumption status and sends the energy consumption report to the display terminal for display;

[0034] The energy consumption prediction and comparison module is used to predict the energy consumption of each collection node of the fresh air unit through the prediction model after the monitoring platform receives the energy consumption information, and compare the prediction results with the actual received energy consumption information;

[0035] An alarm module is used to set a first threshold. During the comparison process, if the difference between the predicted result and the actual energy consumption is greater than the first threshold, an alarm mechanism will be triggered to send an alarm message to the management personnel;

[0036] The feedback module is used for managers to formulate targeted energy-saving measures or maintenance plans based on energy consumption reports and alarm information, and to continuously monitor the energy consumption of the fresh air unit and feed back the continuous monitoring results to the managers.

[0037] The beneficial effect of the present application is as follows: the present application provides an isothermal dehumidification and humidification fresh air unit operation monitoring method and monitoring system, which collects the first energy consumption data at each collection node of the fresh air unit, merges the data to obtain the second energy consumption data, and analyzes the second energy consumption data through the edge device to generate energy consumption conditions based on the collection nodes, thereby facilitating the subsequent comparison process, monitoring the fresh air unit according to historical energy consumption, and promptly issuing an alarm for abnormal energy consumption.

[0038] In addition to the above-described purposes, features and advantages, the present application also has other purposes, features and advantages. The present application will be further described in detail with reference to the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings in the specification, which constitute a part of the present application, are used to provide further understanding of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute improper limitations on the present application.

[0040] In the attached picture:

[0041] Figure 1 This is a schematic diagram of the overall process of an operation monitoring method of an isothermal dehumidification and humidification fresh air unit in this application;

[0042] Figure 2 This is a schematic diagram of the module structure of an operation monitoring system of an isothermal dehumidification and humidification fresh air unit in this application;

[0043] Figure 3 This is a schematic diagram of an operation monitoring device for an isothermal dehumidification and humidification fresh air unit in the present application. DETAILED DESCRIPTION

[0044] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0045] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0046] Embodiment 1: Figure 1 As shown, the present application provides an isothermal dehumidification and humidification fresh air unit operation monitoring method, which is used to monitor the operation process of the fresh air unit, upload the collected fresh air unit operation energy consumption data in a timely manner, and optimize the real-time energy consumption delay problem of the fresh air unit;

[0047] The fresh air unit is an air conditioning device that provides fresh air. Its working principle is to extract fresh air from the outdoors, and send it to the indoor through a fan after dust removal, dehumidification (or humidification), cooling (or heating) and other treatments. When it enters the indoor space, it replaces the original air in the room. The control components of the existing fresh air unit usually include supply air temperature control, supply air relative humidity control, antifreeze control, carbon dioxide concentration control and various linkage contents. The linkage operation of various control components will bring high energy consumption. In order to monitor the operating status of the fresh air unit, it is usually necessary to monitor the energy consumption of each control component;

[0048] The operation monitoring method comprises the following steps:

[0049] Step 01: The server receives the first energy consumption data collected by the collection devices at each collection node of the fresh air unit, and merges the first energy consumption data to generate second energy consumption data;

[0050] Since the fresh air unit is composed of multiple modules, in order to monitor the real-time energy consumption of the fresh air unit, it is necessary to set up collection equipment at different collection nodes to collect energy consumption. The collection nodes are set according to the functions of the corresponding modules. For example, the collection nodes can be divided according to the type of medium passing through the module: the module controlling the liquid is divided into the same collection node, the module controlling the gas is divided into another collection node, and the remaining control devices are divided into separate collection nodes, thereby saving the layout of the collection equipment to a certain extent, reducing costs and realizing distributed monitoring;

[0051] In this embodiment, the server refers to a software and hardware module with functions such as data reception, data processing, and data storage, rather than the definition of a server in the prior art. The server can be used as a transfer station in the process of transmitting the collected energy consumption data, and enter other links after being processed by the server;

[0052] The acquisition device may be a variety of sensors used alone or in combination, for example, an electric power meter may be set alone in each node, or an electric power meter may be used in combination with a flow meter, a temperature sensor, a pressure sensor, etc. and set in each node, which is not limited in the present invention;

[0053] After obtaining the first energy consumption data collected at each node, the first energy consumption data needs to be merged to facilitate subsequent transmission processing. The merged second energy consumption data is classified according to the node name or number and can be temporarily stored in the server. In subsequent processing, the abnormal node can be quickly located, thereby facilitating maintenance;

[0054] Step 02: The server sends the second energy consumption data to the edge device, and the edge device performs energy consumption analysis on the second energy consumption data to generate energy consumption conditions based on the division of the collection nodes;

[0055] After the first energy consumption data is merged and processed, classified second energy consumption data will be generated. After the server sends the second energy consumption data to the edge device, the edge device analyzes and processes the second energy consumption data, thereby generating energy consumption information of the fresh air unit based on the collection node;

[0056] Among them, edge devices refer to software and hardware devices used for edge computing, and are not devices that provide entry points to the core networks of enterprises or service providers in the prior art; edge computing refers to the process of analyzing data as close to the collection device or at the edge of the collection device as possible, reducing the data transmission process, and completing data analysis at the collection end as much as possible. In the subsequent transmission process, only the analysis results are transmitted instead of the collected data. Because the amount of collected data is large and the types are complex, it is easy to cause loss, errors, delays, etc. during the transmission process, and the analysis results will not change significantly during the transmission process, and the data transmission volume of the analysis results is smaller than that of directly transmitting the collected data, and the transmission efficiency is higher, so the real-time performance is higher. Among them, edge devices used for edge computing include but are not limited to edge gateways, edge servers, embedded systems and other devices, which are not limited in this embodiment;

[0057] The edge device further performs energy consumption analysis on the second energy consumption data including:

[0058] Step A: screening abnormal values ​​in the received second energy consumption data;

[0059] Due to various reasons (such as sensor failure, transmission error, etc.), there may be abnormal values ​​in the collected energy consumption data, which will affect the accuracy of energy consumption analysis. Therefore, before analysis, it is necessary to screen out the abnormal values ​​and process or exclude them. The energy consumption data can be judged as abnormal by setting a reasonable threshold range. For example, when the energy consumption data exceeds or is lower than a preset threshold, it can be judged as an abnormal value. Due to the different data types, only the method of judging abnormal data is given in this embodiment. The specific threshold range can refer to the existing technology, such as the Chinese invention patent with announcement number CN117592386B.

[0060] Step B: Calculate the statistical parameters of each collection node according to the filtered second energy consumption data, the statistical parameters including the energy consumption mean and the energy consumption peak;

[0061] After removing outliers, it is necessary to calculate the energy consumption statistical parameters of each collection node, such as the mean energy consumption, peak energy consumption (maximum or minimum value), etc., in order to subsequently analyze the overall energy consumption of the fresh air unit and the energy consumption differences of each node.

[0062] Step C: Based on statistical parameters, evaluate the energy efficiency and operation status of each collection node;

[0063] Based on the calculated statistical parameters, the energy efficiency and operating status of each collection node can be evaluated. For example, by comparing the average energy consumption of different nodes, it is possible to determine which nodes have higher energy consumption, which may have problems such as energy waste or equipment failure; by comparing the maximum and minimum values, it is possible to determine the fluctuation of energy consumption data and thus evaluate the stability of the fresh air unit.

[0064] Step D: Generate an energy consumption report based on the collection nodes, and send the report to the server or designated terminal;

[0065] Finally, based on the energy consumption analysis results, an energy consumption report is generated based on the collection nodes. The report can include information such as energy consumption statistical parameters of each node, energy efficiency evaluation results, and operation status evaluation results, and can be viewed and further processed through the server or designated terminal.

[0066] Step 03: The edge device sends the energy consumption information to the monitoring platform. After receiving the energy consumption information, the monitoring platform generates an energy consumption report based on the energy consumption information and sends the energy consumption report to the display terminal for display.

[0067] In order to enable managers to intuitively see the energy consumption of the fresh air unit, after the edge device completes the energy consumption analysis, it sends the energy consumption to the monitoring platform. The monitoring platform generates an energy consumption report based on the energy consumption. The energy consumption report includes but is not limited to the energy consumption of each collection node, energy consumption proportion, energy consumption change trend and other information, so that managers can quickly understand the energy consumption of the fresh air unit and make corresponding optimization measures;

[0068] Step 04: After receiving the energy consumption data, the monitoring platform predicts the energy consumption of each collection node of the fresh air unit through the prediction model, and compares the prediction results with the actual energy consumption data received;

[0069] The prediction model has a data input end and a data output end, wherein the data input end is used to input the second energy consumption data. A prediction algorithm is run in the prediction model. After receiving the input data, the prediction result of the energy consumption of each acquisition node is output according to a fixed rule. The prediction model can be a data-driven model, such as a regression analysis model, which uses statistical methods to establish a relationship model between energy consumption and influencing factors (such as time, temperature, load, etc.); a time series model, which uses time series analysis techniques, such as an autoregressive moving average model (ARMA), an autoregressive integrated moving average model (ARIMA), etc., to capture trends, periodicity and seasonality in the data and make predictions; a machine learning model, including various machine learning algorithms such as decision trees, random forests, support vector machines (SVM), neural networks, etc., which can perform nonlinear modeling and prediction based on the complex relationship of the data. The prediction model in this step can be a combination of one or more of the above methods;

[0070] Before the comparison, the output results of the prediction model need to be optimized to improve the accuracy of the prediction results. Specifically, the optimization of the output results of the prediction model includes:

[0071] Get the output of the prediction model and assign weights to each output according to the model performance;

[0072] When using multiple methods to predict energy consumption, due to differences in model performance, the prediction results may not be exactly the same. Therefore, it is necessary to output the results after each method performs prediction separately, and assign weights to these output results according to the model performance, where the sum of all weights is one, and the higher the model prediction accuracy, the greater the weight value assigned to it. Specifically, the prediction accuracy of the model can refer to the confidence of the corresponding model in the prior art, which is not described in detail in this embodiment, and it is only necessary to ensure that the sum of all weights is one;

[0073] Make a weighted decision on the output results according to the assigned weights, and output the weighted decision results;

[0074] After assigning the weights, it is also necessary to make a weighted decision on the output results, where the weighted decision means multiplying the weight value by the predicted result (energy consumption). For example, when using two different types of prediction models to predict energy consumption, the prediction result of model A is a joule, and the prediction result of model B is b joule. The weight assigned to model A is 0.7, and the weight assigned to model B is 0.3. Then the weighted decision result is 0.7a+0.3b, which is the optimized prediction result.

[0075] Adjust the assigned weights, re-make weighted decisions, and update the weighted decision results;

[0076] In order to improve the accuracy of the prediction results, the assigned weights need to be optimized. The optimization of the assigned weights can refer to the Chinese patent publication number CN112231965A, which will not be repeated in this embodiment. After the weights are optimized, the weighted decision is re-performed to update the weighted decision results in the aforementioned process.

[0077] Step 05: Set a first threshold. During the comparison process, if the difference between the predicted result and the actual energy consumption is greater than the first threshold, an alarm mechanism will be triggered to send an alarm message to the management personnel.

[0078] During the comparison process, a comparison standard needs to be set. The comparison standard is the first threshold. If the difference between the predicted result and the actual energy consumption is greater than the first threshold, it means that the actual energy consumption may be abnormal. Therefore, an alarm mechanism is triggered and the alarm information is sent to the management personnel or other display terminals. The alarm information contains the specific data of the difference, the possible cause and the recommended countermeasures, so that the management personnel can respond quickly and solve the potential problems.

[0079] The first threshold value may be set according to actual conditions. In this embodiment, the first threshold value may be set to 10% of the actual energy consumption. When the actual energy consumption is 100 joules, the first threshold value is 10 joules.

[0080] Step 06: Based on the energy consumption report and alarm information, the manager formulates targeted energy-saving measures or maintenance plans, and continuously monitors the energy consumption of the fresh air unit, and feeds back the continuous monitoring results to the manager.

[0081] For example, for nodes with abnormally high energy consumption, their operating parameters can be optimized or equipment maintenance can be performed; for nodes with unstable operation, system upgrades or equipment replacements can be performed.

[0082] After implementing energy-saving measures or maintenance plans, the monitoring platform will continuously monitor the energy consumption of the fresh air unit and compare and analyze the new energy consumption data with the previous data to evaluate the effectiveness of the energy-saving measures or maintenance plans.

[0083] If energy-saving measures or maintenance plans achieve significant results, the monitoring platform will feed this information back to managers and recommend that these measures or plans be incorporated into routine management. At the same time, the monitoring platform will continue to learn and optimize the prediction model to improve the accuracy of the prediction.

[0084] Through the implementation of the above steps, this system can realize real-time monitoring, analysis, prediction and alarm of the energy consumption of the fresh air unit, help managers to promptly discover problems, formulate measures, and evaluate the effects, thereby realizing effective management and control of the energy consumption of the fresh air unit. It can also realize real-time energy consumption monitoring of the fresh air unit, promptly discover and deal with abnormal energy consumption, thereby optimizing the operating efficiency of the fresh air unit and reducing operating costs.

[0085] Embodiment 2: Figure 2 As shown, the present application also provides an operation monitoring system for an isothermal dehumidification and humidification fresh air unit, the system operates the monitoring method in embodiment 1, and the system includes:

[0086] A data merging module is used for the server to receive the first energy consumption data collected by the collection devices at each collection node of the fresh air unit, and to merge the first energy consumption data to generate second energy consumption data;

[0087] An energy consumption analysis module, which is used for the server to send the second energy consumption data to the edge device, and the edge device performs energy consumption analysis on the second energy consumption data to generate energy consumption conditions based on the division of the collection nodes;

[0088] The energy consumption report generation module is used for the edge device to send the energy consumption status to the monitoring platform. After receiving the energy consumption status, the monitoring platform generates an energy consumption report according to the energy consumption status and sends the energy consumption report to the display terminal for display;

[0089] The energy consumption prediction and comparison module is used to predict the energy consumption of each collection node of the fresh air unit through the prediction model after the monitoring platform receives the energy consumption information, and compare the prediction results with the actual received energy consumption information;

[0090] An alarm module is used to set a first threshold. During the comparison process, if the difference between the predicted result and the actual energy consumption is greater than the first threshold, an alarm mechanism will be triggered to send an alarm message to the management personnel;

[0091] The feedback module is used for managers to formulate targeted energy-saving measures or maintenance plans based on energy consumption reports and alarm information, and to continuously monitor the energy consumption of the fresh air unit and feed back the continuous monitoring results to the managers.

[0092] Embodiment 3: Figure 3As shown, the present application also proposes an operation monitoring device for an isothermal dehumidification and humidification fresh air unit, which includes a collection device, a server, an edge device and a monitoring platform. The collection device is used to collect the first energy consumption data at each collection node of the fresh air unit, the server is used to receive and merge the first energy consumption data to generate the second energy consumption data, the edge device is used to perform energy consumption analysis on the second energy consumption data to generate energy consumption status, and send the energy consumption status to the monitoring platform, the monitoring platform is used to receive the energy consumption status and generate an energy consumption report, and perform anomaly detection based on the energy consumption status, and when an abnormal energy consumption status is detected, an abnormal alarm information is generated and sent to the management personnel.

[0093] Through this device, real-time energy consumption monitoring and abnormal alarm of the fresh air unit can be achieved, thereby improving the operating efficiency and safety of the fresh air unit.

[0094] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An operation monitoring method for an isothermal dehumidification and humidification fresh air unit, characterized in that: include: The server receives first energy consumption data collected by collection devices at each collection node of the fresh air unit, and merges the first energy consumption data to generate second energy consumption data, wherein the fresh air unit is composed of multiple modules, wherein the collection nodes are set according to the medium type of the module; The server sends the second energy consumption data to the edge device, and the edge device performs energy consumption analysis on the second energy consumption data to generate energy consumption conditions based on the division of the collection nodes; The edge device sends the energy consumption information to the monitoring platform. After receiving the energy consumption information, the monitoring platform generates an energy consumption report based on the energy consumption information and sends the energy consumption report to the display terminal for display. After receiving the energy consumption data, the monitoring platform predicts the energy consumption of each collection node of the fresh air unit through the prediction model, and compares the prediction results with the actual received energy consumption data. Before the comparison, the output results of the prediction model need to be optimized. The optimization of the output results of the prediction model includes: Get the output of the prediction model and assign weights to each output according to the model performance; Make a weighted decision on the output results according to the assigned weights, and output the weighted decision results; Adjust the assigned weights, re-make weighted decisions, and update the weighted decision results; A first threshold is set. During the comparison process, if the difference between the predicted result and the actual energy consumption is greater than the first threshold, an alarm mechanism will be triggered to send an alarm message to the management personnel; The edge device further performs energy consumption analysis on the second energy consumption data including: Screening abnormal values ​​in the received second energy consumption data; Calculate the statistical parameters of each collection node according to the filtered second energy consumption data, the statistical parameters including the energy consumption mean and the energy consumption peak; Based on statistical parameters, the energy efficiency and operating status of each collection node are evaluated. By comparing the average energy consumption of different nodes, it is determined which nodes have higher energy consumption. By comparing the maximum and minimum values, the fluctuation of energy consumption data is determined. Generate an energy consumption report based on the collection nodes, and send the report to the server or designated terminal. The energy consumption report includes energy consumption statistical parameters of each node, energy efficiency evaluation results, and operation status evaluation results.

2. The operation monitoring method of an isothermal dehumidification and humidification fresh air unit according to claim 1 is characterized in that: The prediction model has a data input end and a data output end, wherein the data input end is used to input the second energy consumption data. A prediction algorithm runs in the prediction model, and after receiving the input data, the prediction result of the energy consumption of each collection node is output according to a fixed rule.

3. The operation monitoring method of an isothermal dehumidification and humidification fresh air unit according to claim 1 is characterized in that: The value of the first threshold is 10% of the actual energy consumption at each node.

4. The operation monitoring method of an isothermal dehumidification and humidification fresh air unit according to claim 1 is characterized in that: The prediction model includes a combination of one or more of a data-driven model, a time series model, and a machine learning model.

5. The operation monitoring method of an isothermal dehumidification and humidification fresh air unit according to claim 1 is characterized in that: The sum of all assigned weights is one.

6. The operation monitoring method of an isothermal dehumidification and humidification fresh air unit according to claim 1 is characterized in that: The weighted decision refers to multiplying the weight value by the prediction result, the prediction result is the energy consumption situation, and the unit of the energy consumption situation is joule.

7. The operation monitoring method of an isothermal dehumidification and humidification fresh air unit according to claim 1 is characterized in that: The method also includes: based on the energy consumption report and alarm information, the management personnel formulate targeted energy-saving measures or maintenance plans, and continuously monitor the energy consumption of the fresh air unit, and feed back the continuous monitoring results to the management personnel.

8. An operation monitoring system for an isothermal dehumidification and humidification fresh air unit, used for implementing the operation monitoring method for an isothermal dehumidification and humidification fresh air unit according to any one of claims 1 to 7, characterized in that: The system includes: A data merging module, used for the server to receive the first energy consumption data collected by the collection devices at each collection node of the fresh air unit, and merge the first energy consumption data to generate second energy consumption data, wherein the fresh air unit is composed of a plurality of modules, wherein the setting of the collection nodes is divided according to the medium type of the module; An energy consumption analysis module, which is used for the server to send the second energy consumption data to the edge device, and the edge device performs energy consumption analysis on the second energy consumption data to generate energy consumption conditions based on the division of the collection nodes; The energy consumption report generation module is used for the edge device to send the energy consumption status to the monitoring platform. After receiving the energy consumption status, the monitoring platform generates an energy consumption report according to the energy consumption status and sends the energy consumption report to the display terminal for display; The edge device further performs energy consumption analysis on the second energy consumption data including: Screening abnormal values ​​in the received second energy consumption data; Calculate the statistical parameters of each collection node according to the filtered second energy consumption data, the statistical parameters including the energy consumption mean and the energy consumption peak; Based on statistical parameters, the energy efficiency and operating status of each collection node are evaluated. By comparing the average energy consumption of different nodes, it is determined which nodes have higher energy consumption. By comparing the maximum and minimum values, the fluctuation of energy consumption data is determined. Generate an energy consumption report based on the collection nodes, and send the report to the server or designated terminal. The energy consumption report includes energy consumption statistical parameters of each node, energy efficiency evaluation results, and operation status evaluation results; The energy consumption prediction and comparison module is used to predict the energy consumption of each collection node of the fresh air unit through the prediction model after the monitoring platform receives the energy consumption information, and compare the prediction results with the actual received energy consumption information. Before the comparison, the output results of the prediction model need to be optimized. The optimization of the output results of the prediction model includes: Get the output of the prediction model and assign weights to each output according to the model performance; Make a weighted decision on the output results according to the assigned weights, and output the weighted decision results; Adjust the assigned weights, re-make weighted decisions, and update the weighted decision results; An alarm module is used to set a first threshold. During the comparison process, if the difference between the predicted result and the actual energy consumption is greater than the first threshold, an alarm mechanism will be triggered to send an alarm message to the management personnel; The feedback module is used for managers to formulate targeted energy-saving measures or maintenance plans based on energy consumption reports and alarm information, and to continuously monitor the energy consumption of the fresh air unit and feed back the continuous monitoring results to the managers.

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