CN116151644A - Refrigerating station evaluation system and method based on energy Internet of things - Google Patents
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Abstract
A refrigerating station evaluation system and method based on the energy Internet of things comprises a data acquisition module, a data processing module, a central monitoring module and an evaluation visualization module; the data acquisition module is used for acquiring the operation data of each device of the refrigeration station in real time; the data processing module is used for processing the collected operation data; the central monitoring module is used for storing the processed operation data in a classified manner; and the evaluation visualization module is used for evaluating all the equipment of the refrigeration station according to the operation data stored by the central monitoring module and visualizing the evaluation result. The comprehensive evaluation method and the system realize comprehensive evaluation of the refrigerating station, and visually display the real-time running state of the refrigerating station.
Description
Technical Field
The application belongs to the technical field of data processing of the Internet of things, and particularly relates to a refrigerating station evaluation system and method based on the Internet of things of energy.
Background
At present, many cold supply facilities in public workshops select a refrigeration station for cold supply, such as a supermarket, a hotel and a railway station. However, the refrigerating station system is used as a system for cooling workshops, and is operated under 50% load for over 70% of the whole year, so that great energy waste is caused, and as the energy price increases, the reduction of energy consumption is a common public knowledge. However, the number and performance of the equipment in the refrigerating station are basically controlled according to engineering experience accumulated by technologies of heating ventilation, air conditioning and building automation in the market, the refrigerating station system is not adjusted according to specific refrigerating station performance scores so as to reduce energy consumption, and a method for realizing energy saving optimization based on an evaluating system of the refrigerating station established by working environment and actual operation data of the refrigerating station is relatively few.
Disclosure of Invention
The application provides a refrigerating station evaluation system and method based on the energy Internet of things, which meet the evaluation of the operation and maintenance requirements of the refrigerating station, respectively relate to the operation safety, the energy efficiency and the cooling quality of the refrigerating station, and realize the comprehensive evaluation of the refrigerating station; and establishing an online monitoring and evaluating index of the refrigerating station suitable for the architecture of the Internet of things.
To achieve the above object, the present application provides the following solutions:
a refrigerating station evaluation system based on the energy Internet of things comprises a data acquisition module, a data processing module, a central monitoring module and an evaluation visualization module;
the data acquisition module is used for acquiring the operation data of each device of the refrigeration station in real time;
the data processing module is used for processing the collected operation data;
the central monitoring module is used for storing the processed operation data in a classified mode;
and the assessment visualization module is used for assessing each device of the refrigeration station according to the operation data stored by the central monitoring module and visualizing the assessment result.
Preferably, the data acquisition module comprises a temperature sensor, a flow sensor, a pressure sensor, a water quality sensor and a multifunctional ammeter;
the operation data comprise temperature data, flow data, water pressure data, water quality data and power consumption data;
the temperature sensor is used for detecting temperature and generating temperature data;
the flow sensor is used for detecting flow and generating flow data;
the pressure sensor is used for detecting water pressure and generating water pressure data;
the water quality sensor is used for detecting the pH value and hardness of chilled water and generating water quality data;
the multifunctional ammeter is used for recording the power consumption condition of each device of the refrigeration station and generating power consumption data.
Preferably, the data processing module comprises an anomaly detection unit and a calculation unit;
the abnormality detection unit is used for receiving and detecting the temperature data, the flow data, the water pressure data, the water quality data and the power consumption data in a classified manner to respectively obtain normal values and abnormal values of various data;
the calculating unit is used for carrying out operation processing on the normal value of the temperature data and the normal value of the water pressure data; the operation processing comprises the steps of respectively calculating temperature difference and pressure difference of inlet and outlet temperature data and water pressure data, and obtaining temperature difference data and pressure difference data.
Preferably, the anomaly detection unit detects the abnormal value of each type of data:
based on priori knowledge, a distribution model of the various data is assumed;
based on the distribution model of the various data, an outlier detection model based on statistics is constructed, and abnormal values of the various data are identified through an inconsistency test.
Preferably, the central monitoring module comprises a normal value unit and an abnormal value unit;
the normal value unit is used for storing normal values of the temperature difference data, the flow data, the pressure difference data, the water quality data and the power consumption data in a classified mode;
the abnormal value unit is used for storing abnormal values of the temperature data, the flow data, the water pressure data, the water quality data and the power consumption data in a classified mode.
Preferably, the assessment visualization module comprises an operation safety assessment unit, a system energy efficiency assessment unit, a cooling quality assessment unit, a comprehensive assessment unit and a visualization unit;
the operation safety evaluation unit is used for setting the fault condition, the operation state and the water quality condition of each device of the refrigeration station as operation safety evaluation items according to the normal value and the abnormal value of the temperature data, the flow data, the water pressure data, the water quality data and the power consumption data, and performing operation safety scoring;
the system energy efficiency evaluation unit is used for calculating the comprehensive refrigeration performance coefficient, the seasonal energy efficiency ratio, the active power ratio and the inlet and outlet water temperature difference of the refrigeration station according to the temperature difference data, the flow data, the pressure difference data, the water quality data, the normal value of the power consumption data and the preset energy consumption range, setting the comprehensive refrigeration performance coefficient, the seasonal energy efficiency ratio, the active power ratio and the inlet and outlet water temperature difference of the refrigeration station as system energy efficiency evaluation items, and grading the system energy efficiency;
the cooling quality evaluation unit is used for obtaining cooling quality and cooling quality evaluation items according to normal values and abnormal values of the temperature data and voting by users and scoring the cooling quality;
the comprehensive evaluation unit is used for determining respective weights for the operation safety score, the system energy efficiency score and the cooling quality score, setting a score range and obtaining a comprehensive score of the refrigerating station;
and the visualization unit is used for rendering the grading result to the client page.
Preferably, the running safety score, the system energy efficiency score and the cooling quality score rule are: the buckling and separating process comprises the following specific steps:
setting the weight occupied by each evaluation item, and customizing the score of each evaluation unit;
when the evaluation items do not reach the preset standard, multiplying the weight occupied by each evaluation item by the score of each evaluation unit to obtain a deduction value;
and subtracting the corresponding deduction value from the score of each evaluation unit to obtain the final score of each evaluation unit.
Preferably, the weight is set by adopting an entropy weight method, and the specific process is as follows:
acquiring data of each evaluation item, and performing data standardization to obtain standardized data;
calculating information entropy of each evaluation item based on the standardized data;
and determining the weight of each evaluation item based on the information entropy of each evaluation item.
A refrigerating station evaluation method based on the energy Internet of things comprises the following steps:
collecting operation data of all equipment of the refrigerating station in real time;
processing the collected operation data;
storing the processed operation data in a classified manner;
and evaluating each device of the refrigerating station based on the classified stored operation data, and visualizing an evaluation result.
The beneficial effects of this application are: (1) The method is characterized by comprising the steps of evaluating the operation and maintenance requirements of the refrigerating station, respectively relating to the operation safety, the system energy efficiency and the cooling quality of the refrigerating station, and realizing comprehensive evaluation of the refrigerating station; and establishing an online monitoring and evaluating index of the refrigerating station suitable for the architecture of the Internet of things. (2) The outlier detection model based on statistics is constructed to process data, abnormal values are obtained, and because the freezing station has complete evaluation standards, and because various data of freezing station equipment are obtained in real time, the data set is huge, so that the outlier detection model based on statistics is used for effectively and reliably detecting the abnormal values. (3) The weight is determined by using the entropy weight method, so that the influence of subjective factors on the scientificity of the final score due to the fact that the weight is set manually is avoided, and the objective weight is determined by using the entropy weight method according to the variability of the index, so that the method has the advantages of accuracy, objectivity and scientificity.
The application has wide popularization space and use value.
Drawings
For a clearer description of the technical solutions of the present application, the drawings that are required to be used in the embodiments are briefly described below, it being evident that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic structural diagram of a refrigerating station evaluation system based on the energy internet of things according to an embodiment of the present application;
fig. 2 is a flow chart of a refrigerating station evaluation method based on the energy internet of things according to a second embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
In order that the above-recited objects, features and advantages of the present application will become more readily apparent, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings.
Embodiment one: refrigerating station evaluation system based on energy Internet of things
As shown in fig. 1, the refrigerating station evaluation system based on the energy internet of things comprises a data acquisition module, a data processing module, a central monitoring module and an evaluation visualization module;
the data acquisition module is used for acquiring the operation data of each device of the refrigeration station in real time;
the data processing module is used for processing the collected operation data;
the central monitoring module is used for storing the processed operation data in a classified manner;
and the assessment visualization module is used for assessing each device of the refrigeration station according to the operation data stored by the central monitoring module and visualizing the assessment result.
The refrigerating station of the embodiment comprises the following devices: refrigerating unit, chilled water pump, cooling water pump and cooling tower.
The data acquisition module comprises a temperature sensor, a flow sensor, a pressure sensor, a water quality sensor and a multifunctional ammeter;
the operation data comprise temperature data, flow data, water pressure data, water quality data and power consumption data;
the temperature sensor is used for detecting temperature and generating temperature data;
the flow sensor is used for detecting flow and generating flow data;
the pressure sensor is used for detecting water pressure and generating water pressure data;
the water quality sensor is used for detecting the pH value and hardness of the chilled water and generating water quality data;
the multifunctional ammeter is used for recording the power consumption condition of each device of the refrigeration station and generating power consumption data.
The data acquisition module classifies and transmits the acquired temperature data, flow data, water pressure data, water quality data and power consumption data to the data processing unit.
The water chilling unit, the chilled water pump, the cooling water pump and the cooling tower are all provided with data acquisition modules, namely a temperature sensor, a flow sensor, a pressure sensor, a water quality sensor and a multifunctional ammeter; the system can be added with a data acquisition device according to different refrigerating station systems and equipment, and is not limited to acquisition of temperature data, flow data, water pressure data, water quality data and power consumption data.
The data processing module comprises an abnormality detection unit and a calculation unit;
the abnormality detection unit is used for receiving and detecting temperature data, flow data, water pressure data, water quality data and power consumption data in a classified manner, and respectively obtaining normal values and abnormal values of various data;
the calculation unit is used for carrying out operation processing on the normal value of the temperature data and the normal value of the water pressure data; the operation processing comprises the steps of respectively calculating temperature difference and pressure difference of inlet and outlet temperature data and water pressure data, and obtaining temperature difference data and pressure difference data.
The embodiment collects data such as inlet and outlet water temperature, inlet and outlet water pressure, voltage, current and active power of the water chilling unit;
collecting data such as inlet and outlet water temperatures, inlet and outlet water pressures, total flow, voltage, current and active power of the chilled water pump and the cooling water pump;
and collecting data such as the internal and external temperature, inlet and outlet water temperature, voltage, current and active power of the cooling tower.
And calculating the temperature difference and the pressure difference of the inlet and outlet water temperatures.
The abnormal detection unit detects abnormal values of various data in the following processes:
based on priori knowledge, a distribution model of various data is assumed;
based on a distribution model of various data, an outlier detection model based on statistics is constructed, and abnormal values of the various data are identified through an inconsistency test.
Because the evaluation system acquires the running data of each device of the refrigeration station in real time through a plurality of sensors, and each type of data has respective preset indexes, a data distribution model can be assumed, and when a certain type of data acquired at a certain moment deviates from the assumed data distribution obviously by utilizing the statistical outlier detection model, the data is identified as abnormal data.
The central monitoring module comprises a normal value unit and an abnormal value unit;
the normal value unit is used for storing normal values of temperature data, water pressure data, temperature difference data, flow data, pressure difference data, water quality data and power consumption data in a classified manner;
the abnormal value unit is used for storing abnormal values of temperature data, flow data, water pressure data, water quality data and power consumption data in a classified mode.
The assessment visualization module comprises an operation safety assessment unit, a system energy efficiency assessment unit, a cooling quality assessment unit and a comprehensive assessment unit;
the evaluation visualization module comprises an operation safety evaluation unit, a system energy efficiency evaluation unit, a cooling quality evaluation unit, a comprehensive evaluation unit and a visualization unit;
the operation safety evaluation unit is used for setting the fault condition, the operation state and the water quality condition of each equipment of the refrigeration station as operation safety evaluation items according to the normal value and the abnormal value of the temperature data, the flow data, the water pressure data, the water quality data and the power consumption data, and carrying out operation safety scoring;
the system energy efficiency evaluation unit is used for calculating the comprehensive refrigeration performance coefficient, the seasonal energy efficiency ratio, the active power ratio and the inlet and outlet water temperature difference of the refrigeration station according to the normal values of the temperature difference data, the flow data, the pressure difference data, the water quality data and the power consumption data and the preset energy consumption range, setting the comprehensive refrigeration performance coefficient, the seasonal energy efficiency ratio, the active power ratio and the inlet and outlet water temperature difference of the refrigeration station as system energy efficiency evaluation items, and grading the system energy efficiency;
the cooling quality assessment unit is used for obtaining cooling quality and cooling quality assessment items according to normal values and abnormal values of the temperature data and voting by users and scoring the cooling quality;
the comprehensive evaluation unit is used for determining respective weights for the operation safety score, the system energy efficiency score and the cooling quality score, setting a score range and obtaining a comprehensive score of the refrigerating station;
and the visualization unit is used for rendering the scoring result to the client page.
The operation safety score, the system energy efficiency score and the cooling quality score rule are as follows: the buckling and separating process comprises the following specific steps:
setting the weight occupied by each evaluation item, and customizing the score of each evaluation unit;
when the evaluation items do not reach the preset standard, multiplying the weight occupied by each evaluation item by the score of each evaluation unit to obtain a deduction value;
and subtracting the corresponding deduction value from the value of each evaluation unit to obtain the final score of each evaluation unit.
The weight is set by adopting an entropy weight method, and the specific process is as follows:
(1) Acquiring data of each evaluation item, and carrying out data standardization to obtain standardized data;
specifically, n refrigeration station devices, m assessment items, X are determined ij The value of the j-th evaluation item for the i-th device (i=1, 2., n; j=1, 2.,. The., m);
normalization:
forward index:
negative index:
(2) Calculating information entropy of each evaluation item based on the standardized data;
specifically, firstly, calculating the specific gravity of the ith refrigeration station equipment in the jth evaluation item to the evaluation item:
then calculating the entropy value of the j-th evaluation item:
where k=1/ln (n) > 0, so e is satisfied j ≥0；
Calculating information entropy redundancy: d, d j ＝1-e j ,j＝1,...,m
(3) Based on the information entropy of each evaluation item, determining the weight of each evaluation item:
calculating a composite score for each refrigeration station apparatus:
the partial scoring process is as follows: calculating the weight occupied by each evaluation item and the weight occupied by each evaluation unit according to an entropy weight method, running the total score of the safety evaluation units for 100 minutes, wherein the total score accounts for 0.3, and the system automatically counts the number of times of abnormality of each evaluation item currently, wherein the product of the number of times and the weight is a deduction value; the total score of the system energy efficiency scoring unit is 100 minutes, the ratio of the total score to the comprehensive score is 0.5, each evaluation item is set to 0 in a preset system energy consumption range, no score is given, and the evaluation item is set to 1 outside the preset energy consumption range; the total score of the cooling quality scoring unit is 100 minutes, the ratio of the total score to the cooling quality scoring unit is 0.2, the temperature difference between the supplied backwater and the actual preset supplied backwater is within a preset range, the temperature difference is set to be 0, the total score is not buckled, and the total score is set to be 1 outside the preset range; in particular, when evaluating the cooling quality, the air conditioner end user votes on the heat sensation and the heat comfort do not distinguish different devices of the refrigerating station, and the user satisfaction is set to 0 in a preset range, is not buckled, and is set to 1 outside the preset range for uniform judgment. As shown in table 1:
TABLE 1
The system finishes the grading of the operation safety, the system energy efficiency and the cooling quality of each device of the refrigerating station, and the visualization unit renders the grading result to a grading page of the client, so as to realize grading visualization, wherein the grading visualization comprises histogram grading display and line drawing grading display, and the operation state of the refrigerating station is visually displayed.
Embodiment two: refrigerating station evaluation method based on energy Internet of things
As shown in fig. 2, a refrigerating station evaluation method based on the energy internet of things comprises the following steps:
collecting operation data of all equipment of the refrigerating station in real time;
processing the collected operation data;
storing the processed operation data in a classified manner;
and evaluating each device of the refrigerating station based on the classified stored operation data, and visualizing an evaluation result.
The foregoing embodiments are merely illustrative of the preferred embodiments of the present application and are not intended to limit the scope of the present application, and various modifications and improvements made by those skilled in the art to the technical solutions of the present application should fall within the protection scope defined by the claims of the present application.
Claims (9)
1. The refrigerating station evaluation system based on the energy Internet of things is characterized by comprising a data acquisition module, a data processing module, a central monitoring module and an evaluation visualization module;
the data acquisition module is used for acquiring the operation data of each device of the refrigeration station in real time;
the data processing module is used for processing the collected operation data;
the central monitoring module is used for storing the processed operation data in a classified mode;
and the assessment visualization module is used for assessing each device of the refrigeration station according to the operation data stored by the central monitoring module and visualizing the assessment result.
2. The energy internet of things-based refrigerating station evaluation system according to claim 1, wherein the data acquisition module comprises a temperature sensor, a flow sensor, a pressure sensor, a water quality sensor and a multifunctional ammeter;
the operation data comprise temperature data, flow data, water pressure data, water quality data and power consumption data;
the temperature sensor is used for detecting temperature and generating the temperature data;
the flow sensor is used for detecting flow and generating flow data;
the pressure sensor is used for detecting water pressure and generating the water pressure data;
the water quality sensor is used for detecting the pH value and hardness of chilled water and generating the water quality data;
the multifunctional ammeter is used for recording the power consumption condition of each device of the refrigeration station and generating the power consumption data.
3. The system for evaluating the refrigerating station based on the energy Internet of things according to claim 2, wherein,
the data processing module comprises an abnormality detection unit and a calculation unit;
the abnormality detection unit is used for receiving and detecting the temperature data, the flow data, the water pressure data, the water quality data and the power consumption data in a classified manner to respectively obtain normal values and abnormal values of various data;
the calculating unit is used for carrying out operation processing on the normal value of the temperature data and the normal value of the water pressure data; the operation processing comprises the steps of respectively calculating temperature difference and pressure difference of inlet and outlet temperature data and water pressure data, and obtaining temperature difference data and pressure difference data.
4. The refrigerating station evaluation system based on the energy internet of things according to claim 3, wherein the anomaly detection unit detects the abnormal value of various data:
based on priori knowledge, a distribution model of the various data is assumed;
based on the distribution model of the various data, an outlier detection model based on statistics is constructed, and abnormal values of the various data are identified through an inconsistency test.
5. The system for evaluating the refrigerating station based on the energy Internet of things according to claim 3, wherein,
the central monitoring module comprises a normal value unit and an abnormal value unit;
the normal value unit is used for storing normal values of the temperature difference data, the flow data, the pressure difference data, the water quality data and the power consumption data in a classified mode;
the abnormal value unit is used for storing abnormal values of the temperature data, the flow data, the water pressure data, the water quality data and the power consumption data in a classified mode.
6. The energy internet of things-based refrigerating station evaluation system according to claim 5, wherein the evaluation visualization module comprises an operation safety evaluation unit, a system energy efficiency evaluation unit cooling quality evaluation unit, a comprehensive evaluation unit and a visualization unit;
the operation safety evaluation unit is used for setting the fault condition, the operation state and the water quality condition of each device of the refrigeration station as operation safety evaluation items according to the normal value and the abnormal value of the temperature data, the flow data, the water pressure data, the water quality data and the power consumption data, and performing operation safety scoring;
the system energy efficiency evaluation unit is used for calculating the comprehensive refrigeration performance coefficient, the seasonal energy efficiency ratio, the active power ratio and the inlet and outlet water temperature difference of the refrigeration station according to the temperature difference data, the flow data, the pressure difference data, the water quality data, the normal value of the power consumption data and the preset energy consumption range, setting the comprehensive refrigeration performance coefficient, the seasonal energy efficiency ratio, the active power ratio and the inlet and outlet water temperature difference of the refrigeration station as system energy efficiency evaluation items, and grading the system energy efficiency;
the cooling quality evaluation unit is used for obtaining cooling quality and cooling quality evaluation items according to normal values and abnormal values of the temperature data and voting by users and scoring the cooling quality;
the comprehensive evaluation unit is used for determining respective weights for the operation safety score, the system energy efficiency score and the cooling quality score, setting a score range and obtaining a comprehensive score of the refrigerating station;
and the visualization unit is used for rendering the grading result to the client page.
7. The energy internet of things-based refrigerating station assessment system of claim 6, wherein the operational safety score, the system energy efficiency score, and the cooling quality score rule are all: the buckling and separating process comprises the following specific steps:
setting the weight occupied by each evaluation item, and customizing the score of each evaluation unit;
when the evaluation items do not reach the preset standard, multiplying the weight occupied by each evaluation item by the score of each evaluation unit to obtain a deduction value;
and subtracting the corresponding deduction value from the score of each evaluation unit to obtain the final score of each evaluation unit.
8. The refrigerating station evaluation system based on the energy internet of things according to claim 7, wherein the weight is set by adopting an entropy weight method, and the specific process is as follows:
acquiring data of each evaluation item, and performing data standardization to obtain standardized data;
calculating information entropy of each evaluation item based on the standardized data;
and determining the weight of each evaluation item based on the information entropy of each evaluation item.
9. The refrigerating station evaluation method based on the energy Internet of things is characterized by comprising the following steps of:
collecting operation data of all equipment of the refrigerating station in real time;
processing the collected operation data;
storing the processed operation data in a classified manner;
and evaluating each device of the refrigerating station based on the classified stored operation data, and visualizing an evaluation result.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117519054A (en) * | 2023-12-11 | 2024-02-06 | 广州智业节能科技有限公司 | High-efficient cold station control system |
| CN117519054B (en) * | 2023-12-11 | 2024-06-11 | 广州智业节能科技有限公司 | High-efficient cold station control system |
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2022
- 2022-10-25 CN CN202211308333.1A patent/CN116151644A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117519054A (en) * | 2023-12-11 | 2024-02-06 | 广州智业节能科技有限公司 | High-efficient cold station control system |
| CN117519054B (en) * | 2023-12-11 | 2024-06-11 | 广州智业节能科技有限公司 | High-efficient cold station control system |
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